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February 7, 2026 18:30
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Solution draft log for https://github.com/VisageDvachevsky/k8sautoscl/pull/2
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| # Solve.mjs Log - 2026-02-07T18:13:03.611Z | |
| [2026-02-07T18:13:03.612Z] [INFO] 📁 Log file: /home/hive/solve-2026-02-07T18-13-03-611Z.log | |
| [2026-02-07T18:13:03.613Z] [INFO] (All output will be logged here) | |
| [2026-02-07T18:13:04.231Z] [INFO] | |
| [2026-02-07T18:13:04.232Z] [INFO] 🚀 solve v1.16.0 | |
| [2026-02-07T18:13:04.232Z] [INFO] 🔧 Raw command executed: | |
| [2026-02-07T18:13:04.232Z] [INFO] /home/hive/.nvm/versions/node/v20.20.0/bin/node /home/hive/.bun/bin/solve https://github.com/VisageDvachevsky/k8sautoscl/pull/2 --model opus --attach-logs --verbose --no-tool-check --auto-resume-on-limit-reset --tokens-budget-stats | |
| [2026-02-07T18:13:04.233Z] [INFO] | |
| [2026-02-07T18:13:04.248Z] [INFO] | |
| [2026-02-07T18:13:04.248Z] [WARNING] ⚠️ SECURITY WARNING: --attach-logs is ENABLED | |
| [2026-02-07T18:13:04.249Z] [INFO] | |
| [2026-02-07T18:13:04.249Z] [INFO] This option will upload the complete solution draft log file to the Pull Request. | |
| [2026-02-07T18:13:04.249Z] [INFO] The log may contain sensitive information such as: | |
| [2026-02-07T18:13:04.249Z] [INFO] • API keys, tokens, or secrets | |
| [2026-02-07T18:13:04.249Z] [INFO] • File paths and directory structures | |
| [2026-02-07T18:13:04.250Z] [INFO] • Command outputs and error messages | |
| [2026-02-07T18:13:04.250Z] [INFO] • Internal system information | |
| [2026-02-07T18:13:04.250Z] [INFO] | |
| [2026-02-07T18:13:04.250Z] [INFO] ⚠️ DO NOT use this option with public repositories or if the log | |
| [2026-02-07T18:13:04.250Z] [INFO] might contain sensitive data that should not be shared publicly. | |
| [2026-02-07T18:13:04.251Z] [INFO] | |
| [2026-02-07T18:13:04.251Z] [INFO] Continuing in 5 seconds... (Press Ctrl+C to abort) | |
| [2026-02-07T18:13:04.251Z] [INFO] | |
| [2026-02-07T18:13:09.258Z] [INFO] | |
| [2026-02-07T18:13:09.287Z] [INFO] 💾 Disk space check: 31841MB available (2048MB required) ✅ | |
| [2026-02-07T18:13:09.289Z] [INFO] 🧠 Memory check: 8752MB available, swap: 4095MB (0MB used), total: 12847MB (256MB required) ✅ | |
| [2026-02-07T18:13:09.309Z] [INFO] ⏩ Skipping tool connection validation (dry-run mode or skip-tool-connection-check enabled) | |
| [2026-02-07T18:13:09.309Z] [INFO] ⏩ Skipping GitHub authentication check (dry-run mode or skip-tool-connection-check enabled) | |
| [2026-02-07T18:13:09.310Z] [INFO] 📋 URL validation: | |
| [2026-02-07T18:13:09.310Z] [INFO] Input URL: https://github.com/VisageDvachevsky/k8sautoscl/pull/2 | |
| [2026-02-07T18:13:09.310Z] [INFO] Is Issue URL: false | |
| [2026-02-07T18:13:09.310Z] [INFO] Is PR URL: true | |
| [2026-02-07T18:13:09.310Z] [INFO] 🔍 Checking repository access for auto-fork... | |
| [2026-02-07T18:13:10.094Z] [INFO] Repository visibility: public | |
| [2026-02-07T18:13:10.094Z] [INFO] ✅ Auto-fork: No write access detected, enabling fork mode | |
| [2026-02-07T18:13:10.095Z] [INFO] ✅ Repository access check: Skipped (fork mode enabled) | |
| [2026-02-07T18:13:10.431Z] [INFO] Repository visibility: public | |
| [2026-02-07T18:13:10.431Z] [INFO] Auto-cleanup default: false (repository is public) | |
| [2026-02-07T18:13:10.432Z] [INFO] 🔄 Continue mode: Working with PR #2 | |
| [2026-02-07T18:13:10.432Z] [INFO] Continue mode activated: PR URL provided directly | |
| [2026-02-07T18:13:10.433Z] [INFO] PR Number set to: 2 | |
| [2026-02-07T18:13:10.433Z] [INFO] Will fetch PR details and linked issue | |
| [2026-02-07T18:13:10.889Z] [INFO] 🍴 Detected fork PR from konard/VisageDvachevsky-k8sautoscl | |
| [2026-02-07T18:13:10.890Z] [INFO] Fork owner: konard | |
| [2026-02-07T18:13:10.890Z] [INFO] Will clone fork repository for continue mode | |
| [2026-02-07T18:13:10.890Z] [INFO] 📝 PR branch: issue-1-612bd974d0a2 | |
| [2026-02-07T18:13:10.891Z] [INFO] 🔗 Found linked issue #1 | |
| [2026-02-07T18:13:10.892Z] [INFO] | |
| Creating temporary directory: /tmp/gh-issue-solver-1770487990892 | |
| [2026-02-07T18:13:10.894Z] [INFO] | |
| 🍴 Fork mode: ENABLED | |
| [2026-02-07T18:13:10.894Z] [INFO] Checking fork status... | |
| [2026-02-07T18:13:11.230Z] [INFO] 🔍 Detecting fork conflicts... | |
| [2026-02-07T18:13:12.169Z] [INFO] ✅ No fork conflict: Safe to proceed | |
| [2026-02-07T18:13:12.709Z] [INFO] ✅ Fork exists: konard/VisageDvachevsky-k8sautoscl | |
| [2026-02-07T18:13:12.710Z] [INFO] 🔍 Validating fork parent... | |
| [2026-02-07T18:13:13.092Z] [INFO] ✅ Fork parent validated: VisageDvachevsky/k8sautoscl | |
| [2026-02-07T18:13:13.094Z] [INFO] | |
| 📥 Cloning repository: konard/VisageDvachevsky-k8sautoscl | |
| [2026-02-07T18:13:14.562Z] [INFO] ✅ Cloned to: /tmp/gh-issue-solver-1770487990892 | |
| [2026-02-07T18:13:14.616Z] [INFO] 🔗 Setting upstream: VisageDvachevsky/k8sautoscl | |
| [2026-02-07T18:13:14.664Z] [INFO] ℹ️ Upstream exists: Using existing upstream remote | |
| [2026-02-07T18:13:14.665Z] [INFO] 🔄 Fetching upstream... | |
| [2026-02-07T18:13:15.115Z] [INFO] ✅ Upstream fetched: Successfully | |
| [2026-02-07T18:13:15.115Z] [INFO] 🔄 Syncing default branch... | |
| [2026-02-07T18:13:15.515Z] [INFO] ℹ️ Default branch: main | |
| [2026-02-07T18:13:15.898Z] [INFO] ✅ Default branch synced: with upstream/main | |
| [2026-02-07T18:13:15.898Z] [INFO] 🔄 Pushing to fork: main branch | |
| [2026-02-07T18:13:16.334Z] [INFO] ✅ Fork updated: Default branch pushed to fork | |
| [2026-02-07T18:13:16.336Z] [INFO] | |
| 🔍 Checking PR fork: Determining if branch is in another fork... | |
| [2026-02-07T18:13:16.649Z] [INFO] ℹ️ PR fork owner: Same as current user, using origin remote | |
| [2026-02-07T18:13:16.796Z] [INFO] | |
| 📌 Default branch: main | |
| [2026-02-07T18:13:16.856Z] [INFO] | |
| 🔄 Checking out PR branch: issue-1-612bd974d0a2 | |
| [2026-02-07T18:13:16.857Z] [INFO] 📥 Fetching branches: From remote... | |
| [2026-02-07T18:13:17.275Z] [INFO] 🔍 Verifying: Branch checkout... | |
| [2026-02-07T18:13:17.321Z] [INFO] ✅ Branch checked out: issue-1-612bd974d0a2 | |
| [2026-02-07T18:13:17.322Z] [INFO] ✅ Current branch: issue-1-612bd974d0a2 | |
| [2026-02-07T18:13:17.322Z] [INFO] Branch operation: Checkout existing PR branch | |
| [2026-02-07T18:13:17.322Z] [INFO] Branch verification: Matches expected | |
| [2026-02-07T18:13:17.326Z] [INFO] | |
| 🔄 Continue mode: ACTIVE | |
| [2026-02-07T18:13:17.326Z] [INFO] Using existing PR: #2 | |
| [2026-02-07T18:13:17.326Z] [INFO] PR URL: https://github.com/VisageDvachevsky/k8sautoscl/pull/2 | |
| [2026-02-07T18:13:17.326Z] [INFO] | |
| 🚀 Starting work session: 2026-02-07T18:13:17.326Z | |
| [2026-02-07T18:13:17.705Z] [INFO] 📝 Converting PR: Back to draft mode... | |
| [2026-02-07T18:13:18.528Z] [INFO] ✅ PR converted: Now in draft mode | |
| [2026-02-07T18:13:19.603Z] [INFO] 💬 Posted: AI Work Session Started comment | |
| [2026-02-07T18:13:19.896Z] [INFO] 👤 Current user: konard | |
| [2026-02-07T18:13:19.896Z] [INFO] | |
| 📊 Comment counting conditions: | |
| [2026-02-07T18:13:19.897Z] [INFO] prNumber: 2 | |
| [2026-02-07T18:13:19.897Z] [INFO] branchName: issue-1-612bd974d0a2 | |
| [2026-02-07T18:13:19.897Z] [INFO] isContinueMode: true | |
| [2026-02-07T18:13:19.897Z] [INFO] Will count comments: true | |
| [2026-02-07T18:13:19.897Z] [INFO] 💬 Counting comments: Checking for new comments since last commit... | |
| [2026-02-07T18:13:19.897Z] [INFO] PR #2 on branch: issue-1-612bd974d0a2 | |
| [2026-02-07T18:13:19.897Z] [INFO] Owner/Repo: VisageDvachevsky/k8sautoscl | |
| [2026-02-07T18:13:20.375Z] [INFO] 📅 Last commit time (from API): 2026-02-07T18:07:11.000Z | |
| [2026-02-07T18:13:21.316Z] [INFO] 💬 New PR comments: 2 | |
| [2026-02-07T18:13:21.317Z] [INFO] 💬 New PR review comments: 0 | |
| [2026-02-07T18:13:21.317Z] [INFO] 💬 New issue comments: 0 | |
| [2026-02-07T18:13:21.317Z] [INFO] Total new comments: 2 | |
| [2026-02-07T18:13:21.317Z] [INFO] Comment lines to add: Yes | |
| [2026-02-07T18:13:21.318Z] [INFO] PR review comments fetched: 0 | |
| [2026-02-07T18:13:21.318Z] [INFO] PR conversation comments fetched: 3 | |
| [2026-02-07T18:13:21.318Z] [INFO] Total PR comments checked: 3 | |
| [2026-02-07T18:13:23.763Z] [INFO] Feedback info will be added to prompt: | |
| [2026-02-07T18:13:23.763Z] [INFO] - New comments on the pull request: 2 | |
| [2026-02-07T18:13:23.763Z] [INFO] - Pull request description was edited after last commit | |
| [2026-02-07T18:13:23.763Z] [INFO] - Merge status is UNSTABLE (non-passing commit status) | |
| [2026-02-07T18:13:23.764Z] [INFO] 📅 Getting timestamps: From GitHub servers... | |
| [2026-02-07T18:13:24.089Z] [INFO] 📝 Issue updated: 2026-02-07T18:05:57.000Z | |
| [2026-02-07T18:13:24.405Z] [INFO] 💬 Last comment: 2026-02-07T18:05:57.000Z | |
| [2026-02-07T18:13:24.779Z] [INFO] 🔀 Recent PR: 2026-02-07T18:02:48.000Z | |
| [2026-02-07T18:13:24.779Z] [INFO] | |
| ✅ Reference time: 2026-02-07T18:05:57.000Z | |
| [2026-02-07T18:13:24.780Z] [INFO] | |
| 🔍 Checking for uncommitted changes to include as feedback... | |
| [2026-02-07T18:13:24.835Z] [INFO] ✅ No uncommitted changes found | |
| [2026-02-07T18:13:25.184Z] [INFO] 📦 Fork workflows detected: https://github.com/konard/VisageDvachevsky-k8sautoscl/actions?query=branch%3Aissue-1-612bd974d0a2 | |
| [2026-02-07T18:13:27.235Z] [INFO] 🎭 Playwright MCP detected - enabling browser automation hints | |
| [2026-02-07T18:13:27.356Z] [INFO] 👁️ Model vision capability: supported | |
| [2026-02-07T18:13:27.358Z] [INFO] | |
| 📝 Final prompt structure: | |
| [2026-02-07T18:13:27.358Z] [INFO] Characters: 666 | |
| [2026-02-07T18:13:27.359Z] [INFO] System prompt characters: 12718 | |
| [2026-02-07T18:13:27.359Z] [INFO] Feedback info: Included | |
| [2026-02-07T18:13:27.360Z] [INFO] | |
| 🤖 Executing Claude: OPUS | |
| [2026-02-07T18:13:27.361Z] [INFO] Model: opus | |
| [2026-02-07T18:13:27.361Z] [INFO] Working directory: /tmp/gh-issue-solver-1770487990892 | |
| [2026-02-07T18:13:27.361Z] [INFO] Branch: issue-1-612bd974d0a2 | |
| [2026-02-07T18:13:27.361Z] [INFO] Prompt length: 666 chars | |
| [2026-02-07T18:13:27.361Z] [INFO] System prompt length: 12718 chars | |
| [2026-02-07T18:13:27.362Z] [INFO] Feedback info included: Yes (3 lines) | |
| [2026-02-07T18:13:27.423Z] [INFO] 📈 System resources before execution: | |
| [2026-02-07T18:13:27.423Z] [INFO] Memory: MemFree: 596228 kB | |
| [2026-02-07T18:13:27.424Z] [INFO] Load: 2.36 2.03 1.74 4/633 628086 | |
| [2026-02-07T18:13:27.424Z] [INFO] | |
| 📝 Raw command: | |
| [2026-02-07T18:13:27.424Z] [INFO] (cd "/tmp/gh-issue-solver-1770487990892" && claude --output-format stream-json --verbose --dangerously-skip-permissions --model claude-opus-4-6 -p "Issue to solve: https://github.com/VisageDvachevsky/k8sautoscl/issues/1 | |
| Your prepared branch: issue-1-612bd974d0a2 | |
| Your prepared working directory: /tmp/gh-issue-solver-1770487990892 | |
| Your prepared Pull Request: https://github.com/VisageDvachevsky/k8sautoscl/pull/2 | |
| Your forked repository: konard/VisageDvachevsky-k8sautoscl | |
| Original repository (upstream): VisageDvachevsky/k8sautoscl | |
| GitHub Actions on your fork: https://github.com/konard/VisageDvachevsky-k8sautoscl/actions?query=branch%3Aissue-1-612bd974d0a2 | |
| New comments on the pull request: 2 | |
| Pull request description was edited after last commit | |
| Merge status is UNSTABLE (non-passing commit status) | |
| Continue. | |
| " --append-system-prompt "You are an AI issue solver. You prefer to find the root cause of each and every issue. When you talk, you prefer to speak with facts which you have double-checked yourself or cite sources that provide evidence, like quote actual code or give references to documents or pages found on the internet. You are polite and patient, and prefer to assume good intent, trying your best to be helpful. If you are unsure or have assumptions, you prefer to test them yourself or ask questions to clarify requirements. | |
| General guidelines. | |
| - When you execute commands, always save their logs to files for easier reading if the output becomes large. | |
| - When running commands, do not set a timeout yourself — let them run as long as needed (default timeout - 2 minutes is more than enough), and once they finish, review the logs in the file. | |
| - When running sudo commands (especially package installations like apt-get, yum, npm install, etc.), always run them in the background to avoid timeout issues and permission errors when the process needs to be killed. Use the run_in_background parameter or append & to the command. | |
| - When CI is failing or user reports failures, consider adding a detailed investigation protocol to your todo list with these steps: | |
| Step 1: List recent runs with timestamps using: gh run list --repo VisageDvachevsky/k8sautoscl --branch issue-1-612bd974d0a2 --limit 5 --json databaseId,conclusion,createdAt,headSha | |
| Step 2: Verify runs are after the latest commit by checking timestamps and SHA | |
| Step 3: For each non-passing run, download logs to preserve them: gh run view {run-id} --repo VisageDvachevsky/k8sautoscl --log > ci-logs/{workflow}-{run-id}.log | |
| Step 4: Read each downloaded log file using Read tool to understand the actual failures | |
| Step 5: Report findings with specific errors and line numbers from logs | |
| This detailed investigation is especially helpful when user mentions CI failures, asks to investigate logs, you see non-passing status, or when finalizing a PR. | |
| Note: If user says \"failing\" but tools show \"passing\", this might indicate stale data - consider downloading fresh logs and checking timestamps to resolve the discrepancy. | |
| - When a code or log file has more than 1500 lines, read it in chunks of 1500 lines. | |
| - When facing a complex problem, do as much tracing as possible and turn on all verbose modes. | |
| - When you create debug, test, or example/experiment scripts for fixing, always keep them in an ./examples and/or ./experiments folders so you can reuse them later. | |
| - When testing your assumptions, use the experiment scripts, and add it to ./experiments folder. | |
| - When your experiments can show real world use case of the software, add it to ./examples folder. | |
| - When you face something extremely hard, use divide and conquer — it always helps. | |
| Initial research. | |
| - When you start, make sure you create detailed plan for yourself and follow your todo list step by step, make sure that as many points from these guidelines are added to your todo list to keep track of everything that can help you solve the issue with highest possible quality. | |
| - When user mentions CI failures or asks to investigate logs, consider adding these todos to track the investigation: (1) List recent CI runs with timestamps, (2) Download logs from failed runs to ci-logs/ directory, (3) Analyze error messages and identify root cause, (4) Implement fix, (5) Verify fix resolves the specific errors found in logs. | |
| - When you read issue, read all details and comments thoroughly. | |
| - When you see screenshots or images in issue descriptions, pull request descriptions, comments, or discussions, use WebFetch tool (or fetch tool) to download the image first, then use Read tool to view and analyze it. IMPORTANT: Before reading downloaded images with the Read tool, verify the file is a valid image (not HTML). Use a CLI tool like 'file' command to check the actual file format. Reading corrupted or non-image files (like GitHub's HTML 404 pages saved as .png) can cause \"Could not process image\" errors and may crash the AI solver process. If the file command shows \"HTML\" or \"text\", the download failed and you should retry or skip the image. | |
| - When you need issue details, use gh issue view https://github.com/VisageDvachevsky/k8sautoscl/issues/1. | |
| - When you need related code, use gh search code --owner VisageDvachevsky [keywords]. | |
| - When you need repo context, read files in your working directory. | |
| - When you study related work, study the most recent related pull requests. | |
| - When issue is not defined enough, write a comment to ask clarifying questions. | |
| - When accessing GitHub Gists (especially private ones), use gh gist view command instead of direct URL fetching to ensure proper authentication. | |
| - When you are fixing a bug, please make sure you first find the actual root cause, do as many experiments as needed. | |
| - When you are fixing a bug and code does not have enough tracing/logs, add them and make sure they stay in the code, but are switched off by default. | |
| - When you need comments on a pull request, note that GitHub has THREE different comment types with different API endpoints: | |
| 1. PR review comments (inline code comments): gh api repos/VisageDvachevsky/k8sautoscl/pulls/2/comments --paginate | |
| 2. PR conversation comments (general discussion): gh api repos/VisageDvachevsky/k8sautoscl/issues/2/comments --paginate | |
| 3. PR reviews (approve/request changes): gh api repos/VisageDvachevsky/k8sautoscl/pulls/2/reviews --paginate | |
| IMPORTANT: The command \"gh pr view --json comments\" ONLY returns conversation comments and misses review comments! | |
| - When you need latest comments on issue, use gh api repos/VisageDvachevsky/k8sautoscl/issues/1/comments --paginate. | |
| Solution development and testing. | |
| - When issue is solvable, implement code with tests. | |
| - When implementing features, search for similar existing implementations in the codebase and use them as examples instead of implementing everything from scratch. | |
| - When coding, each atomic step that can be useful by itself should be commited to the pull request's branch, meaning if work will be interrupted by any reason parts of solution will still be kept intact and safe in pull request. | |
| - When you test: | |
| start from testing of small functions using separate scripts; | |
| write unit tests with mocks for easy and quick start. | |
| - When you test integrations, use existing framework. | |
| - When you test solution draft, include automated checks in pr. | |
| - When issue is unclear, write comment on issue asking questions. | |
| - When you encounter any problems that you unable to solve yourself (any human feedback or help), write a comment to the pull request asking for help. | |
| - When you need human help, use gh pr comment 2 --body \"your message\" to comment on existing PR. | |
| Preparing pull request. | |
| - When you code, follow contributing guidelines. | |
| - When you commit, write clear message. | |
| - When you need examples of style, use gh pr list --repo VisageDvachevsky/k8sautoscl --state merged --search [keywords]. | |
| - When you open pr, describe solution draft and include tests. | |
| - When there is a package with version and GitHub Actions workflows for automatic release, update the version (or other necessary release trigger) in your pull request to prepare for next release. | |
| - When you update existing pr 2, use gh pr edit to modify title and description. | |
| - When you are about to commit or push code, ALWAYS run local CI checks first if they are available in contributing guidelines (like ruff check, mypy, eslint, etc.) to catch errors before pushing. | |
| - When you finalize the pull request: | |
| follow style from merged prs for code, title, and description, | |
| make sure no uncommitted changes corresponding to the original requirements are left behind, | |
| make sure the default branch is merged to the pull request's branch, | |
| make sure all CI checks passing if they exist before you finish, | |
| check for latest comments on the issue and pull request to ensure no recent feedback was missed, | |
| double-check that all changes in the pull request answer to original requirements of the issue, | |
| make sure no new new bugs are introduced in pull request by carefully reading gh pr diff, | |
| make sure no previously existing features were removed without an explicit request from users via the issue description, issue comments, and/or pull request comments. | |
| - When you finish implementation, use gh pr ready 2. | |
| Workflow and collaboration. | |
| - When you check branch, verify with git branch --show-current. | |
| - When you push, push only to branch issue-1-612bd974d0a2. | |
| - When you finish, create a pull request from branch issue-1-612bd974d0a2. (Note: PR 2 already exists, update it instead) | |
| - When you organize workflow, use pull requests instead of direct merges to default branch (main or master). | |
| - When you manage commits, preserve commit history for later analysis. | |
| - When you contribute, keep repository history forward-moving with regular commits, pushes, and reverts if needed. | |
| - When you face conflict that you cannot resolve yourself, ask for help. | |
| - When you collaborate, respect branch protections by working only on issue-1-612bd974d0a2. | |
| - When you mention result, include pull request url or comment url. | |
| - When you need to create pr, remember pr 2 already exists for this branch. | |
| Self review. | |
| - When you check your solution draft, run all tests locally. | |
| - When you check your solution draft, verify git status shows a clean working tree with no uncommitted changes. | |
| - When you compare with repo style, use gh pr diff [number]. | |
| - When you finalize, confirm code, tests, and description are consistent. | |
| GitHub CLI command patterns. | |
| - IMPORTANT: Always use --paginate flag when fetching lists from GitHub API to ensure all results are returned (GitHub returns max 30 per page by default). | |
| - When listing PR review comments (inline code comments), use gh api repos/OWNER/REPO/pulls/NUMBER/comments --paginate. | |
| - When listing PR conversation comments, use gh api repos/OWNER/REPO/issues/NUMBER/comments --paginate. | |
| - When listing PR reviews, use gh api repos/OWNER/REPO/pulls/NUMBER/reviews --paginate. | |
| - When listing issue comments, use gh api repos/OWNER/REPO/issues/NUMBER/comments --paginate. | |
| - When adding PR comment, use gh pr comment NUMBER --body \"text\" --repo OWNER/REPO. | |
| - When adding issue comment, use gh issue comment NUMBER --body \"text\" --repo OWNER/REPO. | |
| - When viewing PR details, use gh pr view NUMBER --repo OWNER/REPO. | |
| - When filtering with jq, use gh api repos/\${owner}/\${repo}/pulls/\${prNumber}/comments --paginate --jq 'reverse | .[0:5]'. | |
| Playwright MCP usage (browser automation via mcp__playwright__* tools). | |
| - When you develop frontend web applications (HTML, CSS, JavaScript, React, Vue, Angular, etc.), use Playwright MCP tools to test the UI in a real browser. | |
| - When WebFetch tool fails to retrieve expected content (e.g., returns empty content, JavaScript-rendered pages, or login-protected pages), use Playwright MCP tools (browser_navigate, browser_snapshot) as a fallback for web browsing. | |
| - When you need to interact with dynamic web pages that require JavaScript execution, use Playwright MCP tools. | |
| - When you need to visually verify how a web page looks or take screenshots, use browser_take_screenshot from Playwright MCP. | |
| - When you need to fill forms, click buttons, or perform user interactions on web pages, use Playwright MCP tools (browser_click, browser_type, browser_fill_form). | |
| - When you need to test responsive design or different viewport sizes, use browser_resize from Playwright MCP. | |
| - When you finish using the browser, always close it with browser_close to free resources. | |
| Visual UI work and screenshots. | |
| - When you work on visual UI changes (frontend, CSS, HTML, design), include a render or screenshot of the final result in the pull request description. | |
| - When you need to show visual results, take a screenshot and save it to the repository (e.g., in a docs/screenshots/ or assets/ folder). | |
| - When you save screenshots to the repository, use permanent raw file links in the pull request description markdown (e.g., https://raw.githubusercontent.com/VisageDvachevsky/k8sautoscl/issue-1-612bd974d0a2/docs/screenshots/result.png). | |
| - When uploading images, commit them to the branch first, then reference them using the raw GitHub URL format. | |
| - When the visual result is important for review, mention it explicitly in the pull request description with the embedded image." | jq -c .) | |
| [2026-02-07T18:13:27.425Z] [INFO] | |
| [2026-02-07T18:13:27.425Z] [INFO] 📋 User prompt: | |
| [2026-02-07T18:13:27.425Z] [INFO] ---BEGIN USER PROMPT--- | |
| [2026-02-07T18:13:27.425Z] [INFO] Issue to solve: https://github.com/VisageDvachevsky/k8sautoscl/issues/1 | |
| Your prepared branch: issue-1-612bd974d0a2 | |
| Your prepared working directory: /tmp/gh-issue-solver-1770487990892 | |
| Your prepared Pull Request: https://github.com/VisageDvachevsky/k8sautoscl/pull/2 | |
| Your forked repository: konard/VisageDvachevsky-k8sautoscl | |
| Original repository (upstream): VisageDvachevsky/k8sautoscl | |
| GitHub Actions on your fork: https://github.com/konard/VisageDvachevsky-k8sautoscl/actions?query=branch%3Aissue-1-612bd974d0a2 | |
| New comments on the pull request: 2 | |
| Pull request description was edited after last commit | |
| Merge status is UNSTABLE (non-passing commit status) | |
| Continue. | |
| [2026-02-07T18:13:27.425Z] [INFO] ---END USER PROMPT--- | |
| [2026-02-07T18:13:27.426Z] [INFO] | |
| [2026-02-07T18:13:27.426Z] [INFO] 📋 System prompt: | |
| [2026-02-07T18:13:27.426Z] [INFO] ---BEGIN SYSTEM PROMPT--- | |
| [2026-02-07T18:13:27.426Z] [INFO] You are an AI issue solver. You prefer to find the root cause of each and every issue. When you talk, you prefer to speak with facts which you have double-checked yourself or cite sources that provide evidence, like quote actual code or give references to documents or pages found on the internet. You are polite and patient, and prefer to assume good intent, trying your best to be helpful. If you are unsure or have assumptions, you prefer to test them yourself or ask questions to clarify requirements. | |
| General guidelines. | |
| - When you execute commands, always save their logs to files for easier reading if the output becomes large. | |
| - When running commands, do not set a timeout yourself — let them run as long as needed (default timeout - 2 minutes is more than enough), and once they finish, review the logs in the file. | |
| - When running sudo commands (especially package installations like apt-get, yum, npm install, etc.), always run them in the background to avoid timeout issues and permission errors when the process needs to be killed. Use the run_in_background parameter or append & to the command. | |
| - When CI is failing or user reports failures, consider adding a detailed investigation protocol to your todo list with these steps: | |
| Step 1: List recent runs with timestamps using: gh run list --repo VisageDvachevsky/k8sautoscl --branch issue-1-612bd974d0a2 --limit 5 --json databaseId,conclusion,createdAt,headSha | |
| Step 2: Verify runs are after the latest commit by checking timestamps and SHA | |
| Step 3: For each non-passing run, download logs to preserve them: gh run view {run-id} --repo VisageDvachevsky/k8sautoscl --log > ci-logs/{workflow}-{run-id}.log | |
| Step 4: Read each downloaded log file using Read tool to understand the actual failures | |
| Step 5: Report findings with specific errors and line numbers from logs | |
| This detailed investigation is especially helpful when user mentions CI failures, asks to investigate logs, you see non-passing status, or when finalizing a PR. | |
| Note: If user says "failing" but tools show "passing", this might indicate stale data - consider downloading fresh logs and checking timestamps to resolve the discrepancy. | |
| - When a code or log file has more than 1500 lines, read it in chunks of 1500 lines. | |
| - When facing a complex problem, do as much tracing as possible and turn on all verbose modes. | |
| - When you create debug, test, or example/experiment scripts for fixing, always keep them in an ./examples and/or ./experiments folders so you can reuse them later. | |
| - When testing your assumptions, use the experiment scripts, and add it to ./experiments folder. | |
| - When your experiments can show real world use case of the software, add it to ./examples folder. | |
| - When you face something extremely hard, use divide and conquer — it always helps. | |
| Initial research. | |
| - When you start, make sure you create detailed plan for yourself and follow your todo list step by step, make sure that as many points from these guidelines are added to your todo list to keep track of everything that can help you solve the issue with highest possible quality. | |
| - When user mentions CI failures or asks to investigate logs, consider adding these todos to track the investigation: (1) List recent CI runs with timestamps, (2) Download logs from failed runs to ci-logs/ directory, (3) Analyze error messages and identify root cause, (4) Implement fix, (5) Verify fix resolves the specific errors found in logs. | |
| - When you read issue, read all details and comments thoroughly. | |
| - When you see screenshots or images in issue descriptions, pull request descriptions, comments, or discussions, use WebFetch tool (or fetch tool) to download the image first, then use Read tool to view and analyze it. IMPORTANT: Before reading downloaded images with the Read tool, verify the file is a valid image (not HTML). Use a CLI tool like 'file' command to check the actual file format. Reading corrupted or non-image files (like GitHub's HTML 404 pages saved as .png) can cause "Could not process image" errors and may crash the AI solver process. If the file command shows "HTML" or "text", the download failed and you should retry or skip the image. | |
| - When you need issue details, use gh issue view https://github.com/VisageDvachevsky/k8sautoscl/issues/1. | |
| - When you need related code, use gh search code --owner VisageDvachevsky [keywords]. | |
| - When you need repo context, read files in your working directory. | |
| - When you study related work, study the most recent related pull requests. | |
| - When issue is not defined enough, write a comment to ask clarifying questions. | |
| - When accessing GitHub Gists (especially private ones), use gh gist view command instead of direct URL fetching to ensure proper authentication. | |
| - When you are fixing a bug, please make sure you first find the actual root cause, do as many experiments as needed. | |
| - When you are fixing a bug and code does not have enough tracing/logs, add them and make sure they stay in the code, but are switched off by default. | |
| - When you need comments on a pull request, note that GitHub has THREE different comment types with different API endpoints: | |
| 1. PR review comments (inline code comments): gh api repos/VisageDvachevsky/k8sautoscl/pulls/2/comments --paginate | |
| 2. PR conversation comments (general discussion): gh api repos/VisageDvachevsky/k8sautoscl/issues/2/comments --paginate | |
| 3. PR reviews (approve/request changes): gh api repos/VisageDvachevsky/k8sautoscl/pulls/2/reviews --paginate | |
| IMPORTANT: The command "gh pr view --json comments" ONLY returns conversation comments and misses review comments! | |
| - When you need latest comments on issue, use gh api repos/VisageDvachevsky/k8sautoscl/issues/1/comments --paginate. | |
| Solution development and testing. | |
| - When issue is solvable, implement code with tests. | |
| - When implementing features, search for similar existing implementations in the codebase and use them as examples instead of implementing everything from scratch. | |
| - When coding, each atomic step that can be useful by itself should be commited to the pull request's branch, meaning if work will be interrupted by any reason parts of solution will still be kept intact and safe in pull request. | |
| - When you test: | |
| start from testing of small functions using separate scripts; | |
| write unit tests with mocks for easy and quick start. | |
| - When you test integrations, use existing framework. | |
| - When you test solution draft, include automated checks in pr. | |
| - When issue is unclear, write comment on issue asking questions. | |
| - When you encounter any problems that you unable to solve yourself (any human feedback or help), write a comment to the pull request asking for help. | |
| - When you need human help, use gh pr comment 2 --body "your message" to comment on existing PR. | |
| Preparing pull request. | |
| - When you code, follow contributing guidelines. | |
| - When you commit, write clear message. | |
| - When you need examples of style, use gh pr list --repo VisageDvachevsky/k8sautoscl --state merged --search [keywords]. | |
| - When you open pr, describe solution draft and include tests. | |
| - When there is a package with version and GitHub Actions workflows for automatic release, update the version (or other necessary release trigger) in your pull request to prepare for next release. | |
| - When you update existing pr 2, use gh pr edit to modify title and description. | |
| - When you are about to commit or push code, ALWAYS run local CI checks first if they are available in contributing guidelines (like ruff check, mypy, eslint, etc.) to catch errors before pushing. | |
| - When you finalize the pull request: | |
| follow style from merged prs for code, title, and description, | |
| make sure no uncommitted changes corresponding to the original requirements are left behind, | |
| make sure the default branch is merged to the pull request's branch, | |
| make sure all CI checks passing if they exist before you finish, | |
| check for latest comments on the issue and pull request to ensure no recent feedback was missed, | |
| double-check that all changes in the pull request answer to original requirements of the issue, | |
| make sure no new new bugs are introduced in pull request by carefully reading gh pr diff, | |
| make sure no previously existing features were removed without an explicit request from users via the issue description, issue comments, and/or pull request comments. | |
| - When you finish implementation, use gh pr ready 2. | |
| Workflow and collaboration. | |
| - When you check branch, verify with git branch --show-current. | |
| - When you push, push only to branch issue-1-612bd974d0a2. | |
| - When you finish, create a pull request from branch issue-1-612bd974d0a2. (Note: PR 2 already exists, update it instead) | |
| - When you organize workflow, use pull requests instead of direct merges to default branch (main or master). | |
| - When you manage commits, preserve commit history for later analysis. | |
| - When you contribute, keep repository history forward-moving with regular commits, pushes, and reverts if needed. | |
| - When you face conflict that you cannot resolve yourself, ask for help. | |
| - When you collaborate, respect branch protections by working only on issue-1-612bd974d0a2. | |
| - When you mention result, include pull request url or comment url. | |
| - When you need to create pr, remember pr 2 already exists for this branch. | |
| Self review. | |
| - When you check your solution draft, run all tests locally. | |
| - When you check your solution draft, verify git status shows a clean working tree with no uncommitted changes. | |
| - When you compare with repo style, use gh pr diff [number]. | |
| - When you finalize, confirm code, tests, and description are consistent. | |
| GitHub CLI command patterns. | |
| - IMPORTANT: Always use --paginate flag when fetching lists from GitHub API to ensure all results are returned (GitHub returns max 30 per page by default). | |
| - When listing PR review comments (inline code comments), use gh api repos/OWNER/REPO/pulls/NUMBER/comments --paginate. | |
| - When listing PR conversation comments, use gh api repos/OWNER/REPO/issues/NUMBER/comments --paginate. | |
| - When listing PR reviews, use gh api repos/OWNER/REPO/pulls/NUMBER/reviews --paginate. | |
| - When listing issue comments, use gh api repos/OWNER/REPO/issues/NUMBER/comments --paginate. | |
| - When adding PR comment, use gh pr comment NUMBER --body "text" --repo OWNER/REPO. | |
| - When adding issue comment, use gh issue comment NUMBER --body "text" --repo OWNER/REPO. | |
| - When viewing PR details, use gh pr view NUMBER --repo OWNER/REPO. | |
| - When filtering with jq, use gh api repos/${owner}/${repo}/pulls/${prNumber}/comments --paginate --jq 'reverse | .[0:5]'. | |
| Playwright MCP usage (browser automation via mcp__playwright__* tools). | |
| - When you develop frontend web applications (HTML, CSS, JavaScript, React, Vue, Angular, etc.), use Playwright MCP tools to test the UI in a real browser. | |
| - When WebFetch tool fails to retrieve expected content (e.g., returns empty content, JavaScript-rendered pages, or login-protected pages), use Playwright MCP tools (browser_navigate, browser_snapshot) as a fallback for web browsing. | |
| - When you need to interact with dynamic web pages that require JavaScript execution, use Playwright MCP tools. | |
| - When you need to visually verify how a web page looks or take screenshots, use browser_take_screenshot from Playwright MCP. | |
| - When you need to fill forms, click buttons, or perform user interactions on web pages, use Playwright MCP tools (browser_click, browser_type, browser_fill_form). | |
| - When you need to test responsive design or different viewport sizes, use browser_resize from Playwright MCP. | |
| - When you finish using the browser, always close it with browser_close to free resources. | |
| Visual UI work and screenshots. | |
| - When you work on visual UI changes (frontend, CSS, HTML, design), include a render or screenshot of the final result in the pull request description. | |
| - When you need to show visual results, take a screenshot and save it to the repository (e.g., in a docs/screenshots/ or assets/ folder). | |
| - When you save screenshots to the repository, use permanent raw file links in the pull request description markdown (e.g., https://raw.githubusercontent.com/VisageDvachevsky/k8sautoscl/issue-1-612bd974d0a2/docs/screenshots/result.png). | |
| - When uploading images, commit them to the branch first, then reference them using the raw GitHub URL format. | |
| - When the visual result is important for review, mention it explicitly in the pull request description with the embedded image. | |
| [2026-02-07T18:13:27.427Z] [INFO] ---END SYSTEM PROMPT--- | |
| [2026-02-07T18:13:27.427Z] [INFO] | |
| [2026-02-07T18:13:27.428Z] [INFO] 📊 CLAUDE_CODE_MAX_OUTPUT_TOKENS: 128000 | |
| [2026-02-07T18:13:27.428Z] [INFO] 📊 MCP_TIMEOUT: 900000ms (server startup) | |
| [2026-02-07T18:13:27.428Z] [INFO] 📊 MCP_TOOL_TIMEOUT: 900000ms (tool execution) | |
| [2026-02-07T18:13:27.429Z] [INFO] 📋 Command details: | |
| [2026-02-07T18:13:27.429Z] [INFO] 📂 Working directory: /tmp/gh-issue-solver-1770487990892 | |
| [2026-02-07T18:13:27.429Z] [INFO] 🌿 Branch: issue-1-612bd974d0a2 | |
| [2026-02-07T18:13:27.430Z] [INFO] 🤖 Model: Claude OPUS | |
| [2026-02-07T18:13:27.430Z] [INFO] 🍴 Fork: konard/VisageDvachevsky-k8sautoscl | |
| [2026-02-07T18:13:27.430Z] [INFO] | |
| ▶️ Streaming output: | |
| [2026-02-07T18:13:29.674Z] [INFO] { | |
| "type": "system", | |
| "subtype": "init", | |
| "cwd": "/tmp/gh-issue-solver-1770487990892", | |
| "session_id": "ce1ab353-0721-47e7-a373-84e062534c7f", | |
| "tools": [ | |
| "Task", | |
| "TaskOutput", | |
| "Bash", | |
| "Glob", | |
| "Grep", | |
| "ExitPlanMode", | |
| "Read", | |
| "Edit", | |
| "Write", | |
| "NotebookEdit", | |
| "WebFetch", | |
| "TodoWrite", | |
| "WebSearch", | |
| "TaskStop", | |
| "AskUserQuestion", | |
| "Skill", | |
| "EnterPlanMode", | |
| "ToolSearch", | |
| "mcp__playwright__browser_close", | |
| "mcp__playwright__browser_resize", | |
| "mcp__playwright__browser_console_messages", | |
| "mcp__playwright__browser_handle_dialog", | |
| "mcp__playwright__browser_evaluate", | |
| "mcp__playwright__browser_file_upload", | |
| "mcp__playwright__browser_fill_form", | |
| "mcp__playwright__browser_install", | |
| "mcp__playwright__browser_press_key", | |
| "mcp__playwright__browser_type", | |
| "mcp__playwright__browser_navigate", | |
| "mcp__playwright__browser_navigate_back", | |
| "mcp__playwright__browser_network_requests", | |
| "mcp__playwright__browser_run_code", | |
| "mcp__playwright__browser_take_screenshot", | |
| "mcp__playwright__browser_snapshot", | |
| "mcp__playwright__browser_click", | |
| "mcp__playwright__browser_drag", | |
| "mcp__playwright__browser_hover", | |
| "mcp__playwright__browser_select_option", | |
| "mcp__playwright__browser_tabs", | |
| "mcp__playwright__browser_wait_for" | |
| ], | |
| "mcp_servers": [ | |
| { | |
| "name": "playwright", | |
| "status": "connected" | |
| } | |
| ], | |
| "model": "claude-opus-4-6", | |
| "permissionMode": "bypassPermissions", | |
| "slash_commands": [ | |
| "keybindings-help", | |
| "debug", | |
| "compact", | |
| "context", | |
| "cost", | |
| "init", | |
| "pr-comments", | |
| "release-notes", | |
| "review", | |
| "security-review", | |
| "insights" | |
| ], | |
| "apiKeySource": "none", | |
| "claude_code_version": "2.1.34", | |
| "output_style": "default", | |
| "agents": [ | |
| "Bash", | |
| "general-purpose", | |
| "statusline-setup", | |
| "Explore", | |
| "Plan" | |
| ], | |
| "skills": [ | |
| "keybindings-help", | |
| "debug" | |
| ], | |
| "plugins": [], | |
| "uuid": "350f2b96-59c6-45a0-9edd-cf80fe4282e2" | |
| } | |
| [2026-02-07T18:13:29.676Z] [INFO] 📌 Session ID: ce1ab353-0721-47e7-a373-84e062534c7f | |
| [2026-02-07T18:13:29.676Z] [INFO] 📁 Log renamed to: /home/hive/ce1ab353-0721-47e7-a373-84e062534c7f.log | |
| [2026-02-07T18:13:33.572Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-6", | |
| "id": "msg_01W93p2ukWSUNawWR2Yjv4Yd", | |
| "type": "message", | |
| "role": "assistant", | |
| "content": [ | |
| { | |
| "type": "text", | |
| "text": "\n\nI'll start by reviewing the current state of the issue, PR, and any new comments." | |
| } | |
| ], | |
| "stop_reason": null, | |
| "stop_sequence": null, | |
| "usage": { | |
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| "cache_creation_input_tokens": 10409, | |
| "cache_read_input_tokens": 15339, | |
| "cache_creation": { | |
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| "ephemeral_1h_input_tokens": 10409 | |
| }, | |
| "output_tokens": 4, | |
| "service_tier": "standard", | |
| "inference_geo": "not_available" | |
| }, | |
| "context_management": null | |
| }, | |
| "parent_tool_use_id": null, | |
| "session_id": "ce1ab353-0721-47e7-a373-84e062534c7f", | |
| "uuid": "4345a6c3-96b2-42de-9983-c390ce814268" | |
| } | |
| [2026-02-07T18:13:34.980Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-6", | |
| "id": "msg_01W93p2ukWSUNawWR2Yjv4Yd", | |
| "type": "message", | |
| "role": "assistant", | |
| "content": [ | |
| { | |
| "type": "tool_use", | |
| "id": "toolu_01DACSdYeQzRJGoLLxTFsE4u", | |
| "name": "Bash", | |
| "input": { | |
| "command": "gh issue view https://github.com/VisageDvachevsky/k8sautoscl/issues/1 --json title,body,comments 2>&1 | tee /tmp/issue-details.json", | |
| "description": "Fetch issue details" | |
| } | |
| } | |
| ], | |
| "stop_reason": null, | |
| "stop_sequence": null, | |
| "usage": { | |
| "input_tokens": 2, | |
| "cache_creation_input_tokens": 10409, | |
| "cache_read_input_tokens": 15339, | |
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| "service_tier": "standard", | |
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| "session_id": "ce1ab353-0721-47e7-a373-84e062534c7f", | |
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| } | |
| [2026-02-07T18:13:36.055Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-6", | |
| "id": "msg_01W93p2ukWSUNawWR2Yjv4Yd", | |
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| "role": "assistant", | |
| "content": [ | |
| { | |
| "type": "tool_use", | |
| "id": "toolu_017fX4NKaaVfXnB41rscGxcW", | |
| "name": "Bash", | |
| "input": { | |
| "command": "gh pr view 2 --repo VisageDvachevsky/k8sautoscl --json title,body,state,headRefName,baseRefName,mergeable,statusCheckRollup 2>&1 | tee /tmp/pr-details.json", | |
| "description": "Fetch PR details" | |
| } | |
| } | |
| ], | |
| "stop_reason": null, | |
| "stop_sequence": null, | |
| "usage": { | |
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| "cache_creation_input_tokens": 10409, | |
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| "ephemeral_1h_input_tokens": 10409 | |
| }, | |
| "output_tokens": 4, | |
| "service_tier": "standard", | |
| "inference_geo": "not_available" | |
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| "context_management": null | |
| }, | |
| "parent_tool_use_id": null, | |
| "session_id": "ce1ab353-0721-47e7-a373-84e062534c7f", | |
| "uuid": "17124e28-8218-4be1-a7f6-066ea5aa2e7e" | |
| } | |
| [2026-02-07T18:13:37.265Z] [INFO] { | |
| "type": "user", | |
| "message": { | |
| "role": "user", | |
| "content": [ | |
| { | |
| "tool_use_id": "toolu_01DACSdYeQzRJGoLLxTFsE4u", | |
| "type": "tool_result", | |
| "content": "{\"body\":\"как мне запустить экспериментальный GUI и проверить как работает это все\\nтакже вопрос, что мы учитываем при предсказвыании нагрузки, что можем учитывать\\nка это все работает и так далее\",\"comments\":[{\"id\":\"IC_kwDORK39zM7mX16u\",\"author\":{\"login\":\"konard\"},\"authorAssociation\":\"NONE\",\"body\":\"# Ответы на вопросы по k8sautoscl\\n\\n## 1. Как запустить экспериментальный GUI и проверить как работает система?\\n\\n### Запуск интерактивного GUI Dashboard\\n\\nВ проекте есть экспериментальный GUI Dashboard, который визуализирует работу ML-автомасштабирования в режиме реального времени.\\n\\n#### Команда запуска:\\n\\n```bash\\npython scripts/experiment_gui.py\\n```\\n\\n### Что показывает GUI?\\n\\nGUI состоит из **3 вкладок**:\\n\\n#### 📊 Tab 1: Neural Network (Нейронная сеть)\\n- **ONNX Inference Pipeline** — визуализация пайплайна инференса:\\n - Raw Metrics (5 значений) → Feature Engineering (44 фичи) → StandardScaler → ONNX LSTM → Прогноз CPU\\n- **Buffer Status** — статус буфера данных (нужно 24 временных точки для предсказания)\\n- **ML Prediction** — текущий прогноз нагрузки в процентах CPU\\n- **Current Metrics** — текущие метрики (CPU, память, сеть, диск)\\n- **Prediction History** — график истории предсказаний (последние 80 шагов)\\n\\n#### ⚙️ Tab 2: Scaling Algorithm (Алгоритм масштабирования)\\n- **9-step pipeline** — пошаговая визуализация алгоритма принятия решений:\\n 1. **EMA Smoothing** — экспоненциальное сглаживание\\n 2. **Hysteresis** — определение направления (UP/DOWN/HOLD)\\n 3. **Desired Replicas** — вычисление желаемого количества реплик\\n 4. **Clamp** — ограничение [min=2, max=8]\\n 5. **Rate Limit** — ограничение скорости изменения\\n 6. **Cooldown Check** — проверка периода охлаждения\\n 7. **Stabilization** — стабилизация при scale-down\\n 8. **No Change?** — проверка необходимости изменения\\n 9. **Commit** — фиксация решения\\n- **State Panel** — текущее состояние алгоритма (cooldown, direction, replicas)\\n- **CPU + Thresholds** — график CPU с порогами срабатывания\\n- **Replica Count** — сравнение количества реплик ML vs HPA\\n- **Decision Log** — лог принятых решений\\n\\n#### 🧪 Tab 3: Experiment (Сравнение экспериментов)\\n- **Comparison Chart** — сравнение HPA vs ML автомасштабирования:\\n - График CPU нагрузки\\n - График количества реплик (HPA в красном, ML в синем)\\n - График разницы в репликах (ML - HPA)\\n- **8 KPI карточек** — ключевые метрики сравнения:\\n - Avg Replicas — среднее количество реплик\\n - Replica-Minutes — общее потребление ресурсов\\n - SLA Violations — нарушения SLA\\n - Scaling Events — количество событий масштабирования\\n - Over-Provisioning % — избыточное выделение ресурсов\\n - Under-Provisioning % — недостаточное выделение ресурсов\\n - Reaction Time — время реакции на изменения\\n - Cost Savings % — экономия затрат\\n\\n### Управление в GUI\\n\\n- **▶ Play** — автоматическое воспроизведение шагов симуляции\\n- **⏭ Step** — пошаговое выполнение (один шаг = 5 минут реального времени)\\n- **↺ Reset** — сброс симуляции к началу\\n- **Speed slider** — регулировка скорости воспроизведения\\n\\n### Параметры в боковой панели (левая панель)\\n\\nВы можете изменять параметры алгоритма в реальном времени:\\n\\n- **Target Util %** (20-90%) — целевая утилизация CPU\\n- **EMA Alpha** (0.1-1.0) — коэффициент экспоненциального сглаживания\\n- **Safety Margin** (0.0-0.5) — запас прочности для проактивного масштабирования\\n- **Up Threshold ×** (1.0-1.5) — порог для масштабирования вверх\\n- **Down Threshold ×** (0.3-0.9) — порог для масштабирования вниз\\n\\nПосле изменения параметров нажмите **\\\"Apply & Re-run\\\"** для перезапуска эксперимента.\\n\\n### 4 встроенных сценария\\n\\n- **Stable** — стабильная нагрузка (25 часов)\\n- **Ramp** — постепенное увеличение нагрузки от 30% до 85% (33 часа)\\n- **Spike** — три всплеска нагрузки (29 часов)\\n- **Diurnal** — дневной цикл с пиками в рабочее время (48 часов)\\n\\n### Требования для запуска GUI\\n\\n```bash\\n# Установка зависимостей\\npoetry install\\n\\n# Проверка наличия тестовых данных\\nls -lh data/processed/test.csv\\nls -lh data/processed/scaler.pkl\\nls -lh data/processed/metadata.json\\n```\\n\\nЕсли данные отсутствуют, GUI автоматически создаст синтетический demo-сценарий (synthetic_demo).\\n\\n### Альтернатива: Статический анализ (без GUI)\\n\\nЕсли GUI не запускается или вам нужны статические графики:\\n\\n```bash\\n# Генерация графиков и таблиц\\npython scripts/experiment_analysis.py --output-dir results/experiments\\n```\\n\\n**Выходные файлы**:\\n- `scenario_*.png` — графики для каждого сценария\\n- `combined_metrics.png` — сравнение метрик\\n- `cost_savings.png` — график экономии\\n- `experiment_results.csv` — данные в CSV\\n- `experiment_table.tex` — таблица для LaTeX\\n\\n---\\n\\n## 2. Что учитывается при предсказании нагрузки, и что можно учитывать?\\n\\n### Что учитывается СЕЙЧАС в системе\\n\\nСистема использует **44 признака** для прогнозирования нагрузки CPU на 15 минут вперед:\\n\\n#### A. Базовые метрики (5 метрик)\\n1. **cpu_usage** — текущая утилизация CPU (%)\\n2. **mem_util** — утилизация памяти (%)\\n3. **net_in** — входящий сетевой трафик (KB/s)\\n4. **net_out** — исходящий сетевой трафик (KB/s)\\n5. **disk_io** — операции ввода-вывода диска\\n\\nДля Docker Compose окружения:\\n- **request_rate** — частота входящих запросов\\n- **latency_p50** — медианная задержка\\n- **latency_p95** — 95-й процентиль задержки\\n- **latency_p99** — 99-й процентиль задержки\\n- **active_jobs** — активные задачи (синтетическая метрика)\\n\\n#### B. Временные признаки (4 признака)\\n- **hour** — час суток (0-23)\\n- **day_of_week** — день недели (0=понедельник, 6=воскресенье)\\n- **is_weekend** — признак выходного дня (0/1)\\n- **minute_of_day** — минута дня (0-1439)\\n\\n**Зачем?** — Позволяет модели учитывать дневные и недельные паттерны (пиковые часы, ночное снижение нагрузки).\\n\\n#### C. Lag Features (16 признаков)\\nЗадержки (lags) для каждой базовой метрики:\\n- **Lag 1** — значение 1 минуту назад\\n- **Lag 5** — значение 5 минут назад\\n- **Lag 15** — значение 15 минут назад\\n- **Lag 30** — значение 30 минут назад\\n\\n**Зачем?** — LSTM получает \\\"память\\\" о недавнем прошлом, что критично для временных рядов.\\n\\n#### D. Rolling Window Features (15 признаков)\\nСкользящие средние для каждой базовой метрики:\\n- **Rolling mean 5** — среднее за последние 5 минут\\n- **Rolling mean 15** — среднее за последние 15 минут\\n- **Rolling mean 30** — среднее за последние 30 минут\\n\\n**Зачем?** — Сглаживание шума и выявление трендов.\\n\\n#### E. Sliding Window для LSTM\\n- **Sequence length**: 24 временных шага (2 часа истории по 5 минут)\\n- **Forecast horizon**: 3 шага вперед (15 минут в будущее)\\n\\n**Зачем?** — LSTM обучается на последовательностях, чтобы находить паттерны изменения нагрузки.\\n\\n### Что МОЖНО учитывать (расширение функционала)\\n\\n#### 1. Метрики приложения (Application-Level Metrics)\\n- **Request rate per endpoint** — нагрузка по конкретным API endpoints\\n- **Database query latency** — задержки запросов к БД\\n- **Error rate (4xx, 5xx)** — частота ошибок\\n- **Active connections** — количество активных соединений\\n- **Queue depth** — глубина очередей задач\\n- **Cache hit rate** — эффективность кеширования\\n\\n#### 2. Бизнес-метрики\\n- **User count** — количество активных пользователей\\n- **Transaction rate** — частота транзакций\\n- **Session duration** — длительность сессий\\n- **Order rate** (для e-commerce) — частота заказов\\n\\n#### 3. Инфраструктурные метрики\\n- **Node CPU/Memory pressure** — нагрузка на узлы кластера\\n- **Pod restart count** — частота перезапусков подов\\n- **Pending pods** — количество подов в очереди\\n- **Network bandwidth utilization** — использование сетевой полосы\\n\\n#### 4. Внешние факторы\\n- **Public holidays** — праздничные дни\\n- **Marketing campaigns** — запланированные маркетинговые кампании\\n- **Scheduled maintenance** — плановое обслуживание\\n- **External API latency** — задержки внешних API\\n- **Weather data** (для location-based сервисов) — погодные данные\\n\\n#### 5. Prophet-специфичные компоненты\\n- **Trend** — долгосрочный тренд\\n- **Seasonality** — сезонность (дневная, недельная, годовая)\\n- **Holidays** — влияние праздников\\n- **Changepoints** — точки изменения тренда\\n\\n#### 6. Дополнительные временные признаки\\n- **is_business_hours** — рабочие часы (9:00-18:00)\\n- **week_of_year** — неделя года\\n- **quarter** — квартал\\n- **is_month_start / is_month_end** — начало/конец месяца\\n\\n### Текущая конфигурация\\n\\nКонфигурация находится в `src/k8s_ml_predictive_autoscaling/preprocessor/config.yaml`:\\n\\n```yaml\\nfeatures:\\n enable_time_features: true\\n lags: [1, 5, 15, 30] # Look back 1, 5, 15, 30 minutes\\n rolling_windows: [5, 15, 30] # Rolling averages\\n\\nsliding_window:\\n sequence_length: 60 # 60 minutes of history for LSTM\\n forecast_steps: [5, 15, 30] # Predict 5, 15, 30 minutes ahead\\n stride: 5 # Create windows every 5 minutes\\n target_metric: request_rate # Predict request rate\\n```\\n\\n### Как добавить новые признаки?\\n\\n1. **Добавить метрику в Prometheus** — настроить экспорт метрики\\n2. **Обновить collector config** — добавить PromQL запрос в `src/k8s_ml_predictive_autoscaling/collector/config.yaml`\\n3. **Обновить preprocessor** — добавить метрику в `scaler_features` и `features`\\n4. **Переобучить модель** — запустить pipeline заново\\n\\n---\\n\\n## 3. Как это все работает?\\n\\n### Общая архитектура (MAPE Loop)\\n\\nСистема следует паттерну **MAPE loop**: **Monitor → Analyze → Plan → Execute**.\\n\\n```\\n┌─────────────────────────────────────────────────────────────────┐\\n│ MAPE LOOP │\\n├─────────────────────────────────────────────────────────────────┤\\n│ │\\n│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌────────┐ │\\n│ │ Monitor │────>│ Analyze │────>│ Plan │────>│Execute │ │\\n│ │(Metrics) │ │(ML Model)│ │(Resource │ │(K8s API│ │\\n│ │ │ │ │ │ Planner) │ │ / HPA) │ │\\n│ └──────────┘ └──────────┘ └──────────┘ └────────┘ │\\n│ ▲ │ │\\n│ │ │ │\\n│ └────────────────────────────────────────────────────┘ │\\n│ Feedback Loop │\\n└─────────────────────────────────────────────────────────────────┘\\n```\\n\\n### 1. Monitor (Мониторинг)\\n\\n**Компоненты**:\\n- **Prometheus** — сбор метрик с микросервисов\\n- **Collector** (`src/k8s_ml_predictive_autoscaling/collector/`) — экспорт метрик из Prometheus\\n\\n**Что происходит**:\\n1. Микросервисы экспонируют `/metrics` (Prometheus format)\\n2. Prometheus scraping каждые 10-30 секунд\\n3. Метрики сохраняются как временные ряды\\n4. Collector экспортирует данные в CSV для обучения\\n\\n**Метрики**:\\n- CPU, память, сеть, диск\\n- Request rate, latency (p50, p95, p99)\\n- Error rate, custom application metrics\\n\\n### 2. Analyze (Анализ — ML слой)\\n\\n**Компоненты**:\\n- **Preprocessor** (`src/k8s_ml_predictive_autoscaling/preprocessor/`) — препроцессинг данных\\n- **ML Models** (`models/lstm/`, `models/prophet/`, `models/hybrid/`) — обученные модели\\n- **Predictor Service** (`src/k8s_ml_predictive_autoscaling/predictor/`) — FastAPI сервис инференса\\n\\n#### Шаг 2.1: Препроцессинг\\n\\n```bash\\npoetry run python -m k8s_ml_predictive_autoscaling.preprocessor.pipeline \\\\\\n --config src/k8s_ml_predictive_autoscaling/preprocessor/config.yaml\\n```\\n\\n**Действия**:\\n1. **Загрузка данных** из `data/raw/*.csv`\\n2. **Resampling** — агрегация по 1-минутным интервалам\\n3. **Anomaly detection** — фильтрация аномалий (z-score > 3.5)\\n4. **Feature engineering**:\\n - Добавление временных признаков (hour, day_of_week, weekend)\\n - Добавление lag features (1, 5, 15, 30 минут назад)\\n - Добавление rolling mean (5, 15, 30 минут)\\n5. **Sliding window generation** — создание последовательностей для LSTM\\n6. **Normalization** — StandardScaler\\n7. **Train/Val/Test split** — 70% / 15% / 15%\\n\\n**Выход**:\\n- `data/processed/train.csv`, `validation.csv`, `test.csv`\\n- `data/processed/sequences_*.npz` — sliding window sequences\\n- `data/processed/scaler.pkl` — сохранённый StandardScaler\\n\\n#### Шаг 2.2: Обучение моделей\\n\\n**Prophet**:\\n```bash\\npoetry run python -m models.prophet.train\\n```\\n- Модель с трендом + сезонностью (дневная, недельная)\\n- Обучение: ~10-20 секунд\\n- Результат: R² = 0.44 (умеренная точность)\\n\\n**LSTM** (лучшая модель):\\n```bash\\npoetry run python -m models.lstm.train\\n```\\n- 2-layer LSTM, hidden_size=64, dropout=0.2\\n- 65,753 параметра\\n- Обучение: ~56 секунд\\n- Результат: **R² = 0.89** (высокая точность)\\n\\n**Hybrid Prophet+LSTM**:\\n```bash\\npoetry run python -m models.hybrid.train\\n```\\n- Prophet для тренда, LSTM для остатков\\n- Результат: R² = 0.68 (средняя точность)\\n\\n#### Шаг 2.3: Экспорт в ONNX\\n\\n```bash\\npoetry run python scripts/export_onnx.py\\n```\\n- Экспорт PyTorch модели в ONNX Runtime\\n- Ускорение инференса: **3.8-4.0x**\\n- Размер модели: 262 KB (обычный) / 78.7 KB (int8 quantization)\\n- Latency: 0.13 ms (vs 0.50 ms PyTorch)\\n\\n#### Шаг 2.4: Inference Service (FastAPI)\\n\\n```bash\\npoetry run python -m k8s_ml_predictive_autoscaling.predictor.app\\n```\\n\\n**API endpoints**:\\n- `POST /predict` — прогноз нагрузки на основе текущих метрик\\n- `GET /health` — health check\\n- `GET /metrics` — Prometheus metrics\\n\\n**Процесс инференса**:\\n1. Получение 24 последних значений метрик (2 часа истории)\\n2. Feature engineering (44 признака)\\n3. Normalization (StandardScaler)\\n4. ONNX inference\\n5. Inverse transform (денормализация)\\n6. Возврат прогноза CPU на 5, 15, 30 минут вперед\\n\\n### 3. Plan (Планирование ресурсов)\\n\\n**Компоненты**:\\n- **Resource Planner** (`src/k8s_ml_predictive_autoscaling/planner/`) — алгоритм планирования\\n\\n**9-Step Algorithm (детальный алгоритм планирования)**:\\n\\n#### Входные данные\\n- `predicted_cpu` — прогноз CPU от ML модели (%)\\n- `current_replicas` — текущее количество реплик\\n\\n#### Параметры\\n- `target_utilization` = 60% — целевая утилизация\\n- `smoothing_alpha` = 0.7 — коэффициент EMA\\n- `safety_margin` = 15% — запас прочности\\n- `scale_up_threshold` = 66% (1.1x target) — порог для масштабирования вверх\\n- `scale_down_threshold` = 42% (0.7x target) — порог для масштабирования вниз\\n\\n#### Шаги алгоритма\\n\\n**Step 1: EMA Smoothing**\\n```\\nsmoothed_cpu = alpha * predicted_cpu + (1 - alpha) * smoothed_cpu_prev\\n```\\nСглаживание шума, предотвращение реакции на случайные всплески.\\n\\n**Step 2: Hysteresis Direction**\\n```\\nif smoothed_cpu > scale_up_threshold:\\n direction = UP\\nelif smoothed_cpu < scale_down_threshold:\\n direction = DOWN\\nelse:\\n direction = HOLD # Dead zone — no action\\n```\\nГистерезис предотвращает осцилляции в зоне [42%, 66%].\\n\\n**Step 3: Compute Desired Replicas**\\n```\\ndesired_replicas = ceil(current_replicas * smoothed_cpu / target_utilization)\\n\\nif direction == UP:\\n desired_replicas = ceil(desired_replicas * (1 + safety_margin))\\n```\\nДобавление 15% запаса при масштабировании вверх для проактивности.\\n\\n**Step 4: Clamp to [min, max]**\\n```\\ndesired_replicas = max(2, min(8, desired_replicas))\\n```\\nОграничение диапазона реплик.\\n\\n**Step 5: Rate Limit**\\n```\\nif direction == UP:\\n desired_replicas = min(desired_replicas, current_replicas + 2)\\nelse:\\n desired_replicas = max(desired_replicas, current_replicas - 1)\\n```\\nОграничение скорости изменения: макс +2 вверх, макс -1 вниз.\\n\\n**Step 6: Cooldown Check**\\n```\\nif time_since_last_scale_up < 60s:\\n block UP\\nif time_since_last_scale_down < 300s:\\n block DOWN\\n```\\nПредотвращение частых изменений.\\n\\n**Step 7: Scale-Down Stabilization**\\n```\\nif direction == DOWN:\\n if consecutive_below_threshold < 3:\\n block DOWN # Need 3 consecutive low readings\\n```\\nКонсервативное масштабирование вниз (требуется 3 подряд низких значения).\\n\\n**Step 8: No-Change Check**\\n```\\nif desired_replicas == current_replicas:\\n skip execution\\n```\\n\\n**Step 9: Commit Decision**\\n```\\nif not blocked:\\n execute scale to desired_replicas\\n update cooldown timers\\n reset stabilization counter\\n```\\n\\n### 4. Execute (Исполнение)\\n\\n**Компоненты**:\\n- **Executor** (`src/k8s_ml_predictive_autoscaling/executor/`) — интеграция с Kubernetes API\\n\\n**Два режима интеграции**:\\n\\n#### Режим 1: HPA External Metrics\\n1. ML-прогнозы экспортируются как **external metrics** для HPA\\n2. Prometheus Adapter читает метрики\\n3. HPA использует external metrics вместо CPU/памяти\\n4. Kubernetes API автоматически масштабирует Deployment\\n\\n#### Режим 2: KEDA (Event-Driven Autoscaling)\\n1. ML-прогнозы экспортируются в Prometheus\\n2. KEDA ScaledObject использует Prometheus trigger\\n3. KEDA создаёт/изменяет HPA на основе ML-метрик\\n\\n**Fallback**:\\n- При недоступности ML-сервиса система откатывается на классический HPA по CPU\\n\\n### Полный цикл работы (пример)\\n\\n```\\nt=0:\\n 1. Prometheus scrapes metrics: CPU=45%, mem=60%, request_rate=1000 req/s\\n 2. Predictor Service получает последние 24 точки (2 часа)\\n 3. ML Model прогнозирует: CPU через 15 мин = 72%\\n 4. Planner:\\n - Smoothed: 0.7*72 + 0.3*65 = 69.9%\\n - Direction: UP (69.9% > 66%)\\n - Desired: ceil(3 * 69.9 / 60) = 4 replicas\\n - Safety: ceil(4 * 1.15) = 5 replicas\\n - Clamp: 5 (in range [2,8])\\n - Rate limit: min(5, 3+2) = 5 OK\\n - Cooldown: OK (no recent scale)\\n - Decision: SCALE 3 → 5\\n 5. Executor отправляет команду в Kubernetes API\\n 6. Kubernetes создаёт 2 новых пода\\n 7. Через 15 минут нагрузка действительно вырастает до 72%, но система уже готова!\\n\\nt=15:\\n Фактическое CPU = 72%, но с 5 репликами эффективная утилизация = 72%/5*3 = 43%\\n → Система проактивно предотвратила перегрузку!\\n```\\n\\n### Ключевые отличия от HPA\\n\\n| Аспект | HPA (реактивный) | ML Autoscaler (проактивный) |\\n|--------|------------------|---------------------------|\\n| Принятие решений | На основе текущих метрик | На основе прогноза на 15 минут |\\n| Реакция на спайк | После факта (задержка ~30-60с) | За 15 минут до спайка |\\n| Стабильность | Частые осцилляции | -50% scaling events |\\n| Over-provisioning | Минимальный | Выше (+15% safety margin) |\\n| SLA violations | Возможны при резких спайках | Практически исключены |\\n| Сложность | Простой (формула) | Высокая (ML pipeline) |\\n\\n### Метрики сравнения (из экспериментов)\\n\\n| Метрика | HPA | ML Autoscaler |\\n|---------|-----|---------------|\\n| Avg Replicas | 4.03 | 4.69 |\\n| Scaling Events | 17.0 | **8.5** (-50%) |\\n| SLA Violations | 0 | 0 |\\n| Under-Provisioning | 0.04% | 0.12% |\\n| Over-Provisioning | 47.0% | 53.6% |\\n| Cost Savings | baseline | -15.8% (выше затраты, но выше стабильность) |\\n\\n---\\n\\n## Дополнительные материалы\\n\\n### Документация\\n- **README.md** — общее описание проекта\\n- **ROADMAP.md** — план развития (Phase 0-8)\\n- **RUN.md** — команды для быстрого старта\\n- **docs/experiments.md** — методология экспериментов\\n- **docs/model-comparison-results.md** — сравнение моделей Prophet/LSTM/Hybrid\\n\\n### Команды для работы с системой\\n\\n```bash\\n# 1. Локальное окружение (Docker Compose)\\ncd docker\\nexport AUTOSCALER_API_TOKEN=\\\"your-token\\\"\\ndocker compose up --build -d\\n\\n# Доступы:\\n# - Demo services: http://localhost:8001-8003\\n# - Prometheus: http://localhost:9090\\n# - Grafana: http://localhost:3000\\n\\n# 2. Сбор данных\\npoetry run python -m k8s_ml_predictive_autoscaling.collector.collect_historical \\\\\\n --config src/k8s_ml_predictive_autoscaling/collector/config.yaml\\n\\n# 3. Препроцессинг\\npoetry run python -m k8s_ml_predictive_autoscaling.preprocessor.pipeline \\\\\\n --config src/k8s_ml_predictive_autoscaling/preprocessor/config.yaml\\n\\n# 4. Обучение модели\\npoetry run python -m models.lstm.train\\n\\n# 5. Экспорт в ONNX\\npoetry run python scripts/export_onnx.py\\n\\n# 6. Inference service\\npoetry run python -m k8s_ml_predictive_autoscaling.predictor.app\\n\\n# 7. Эксперименты (статические графики)\\npython scripts/experiment_analysis.py --output-dir results/experiments\\n\\n# 8. GUI Dashboard\\npython scripts/experiment_gui.py\\n```\\n\\n### Kubernetes deployment\\n\\n```bash\\n# Создание кластера\\nkind create cluster --config k8s/kind-config.yaml --name autoscaling\\n\\n# Установка компонентов\\nkubectl apply -f k8s/manifests/namespace.yaml\\nkubectl apply -f k8s/manifests/prometheus/\\nkubectl apply -f k8s/manifests/demo-service-deployment.yaml\\nkubectl apply -f k8s/manifests/hpa/baseline-hpa.yaml\\n\\n# Деплой ML Predictor\\nkubectl apply -f k8s/manifests/ml-predictor-deployment.yaml\\n\\n# Проверка\\nkubectl get pods -n predictive-autoscaling\\nkubectl get hpa -n predictive-autoscaling\\n```\\n\\n---\\n\\n## Заключение\\n\\n**Основная идея проекта**: Перейти от **реактивного масштабирования** (HPA реагирует после факта) к **проактивному** (ML предсказывает нагрузку за 15 минут и заранее масштабирует).\\n\\n**Преимущества**:\\n- Меньше перегрузок и SLA violations\\n- Стабильнее работа (-50% scaling events)\\n- Лучше предсказуемость расходов\\n\\n**Недостатки**:\\n- Выше сложность (ML pipeline, обучение, ONNX, мониторинг)\\n- Выше over-provisioning (~15% запаса)\\n- Требует данных для обучения (минимум 48 часов)\\n\\n**Рекомендация**: Для продакшена начинайте с консервативных параметров (safety_margin=0.15, stabilization=3), затем постепенно снижайте safety margin на основе реального SLA.\\n\",\"createdAt\":\"2026-02-07T18:05:57Z\",\"includesCreatedEdit\":false,\"isMinimized\":false,\"minimizedReason\":\"\",\"reactionGroups\":[],\"url\":\"https://github.com/VisageDvachevsky/k8sautoscl/issues/1#issuecomment-3865009838\",\"viewerDidAuthor\":true}],\"title\":\"qna\"}", | |
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| "stdout": "{\"body\":\"как мне запустить экспериментальный GUI и проверить как работает это все\\nтакже вопрос, что мы учитываем при предсказвыании нагрузки, что можем учитывать\\nка это все работает и так далее\",\"comments\":[{\"id\":\"IC_kwDORK39zM7mX16u\",\"author\":{\"login\":\"konard\"},\"authorAssociation\":\"NONE\",\"body\":\"# Ответы на вопросы по k8sautoscl\\n\\n## 1. Как запустить экспериментальный GUI и проверить как работает система?\\n\\n### Запуск интерактивного GUI Dashboard\\n\\nВ проекте есть экспериментальный GUI Dashboard, который визуализирует работу ML-автомасштабирования в режиме реального времени.\\n\\n#### Команда запуска:\\n\\n```bash\\npython scripts/experiment_gui.py\\n```\\n\\n### Что показывает GUI?\\n\\nGUI состоит из **3 вкладок**:\\n\\n#### 📊 Tab 1: Neural Network (Нейронная сеть)\\n- **ONNX Inference Pipeline** — визуализация пайплайна инференса:\\n - Raw Metrics (5 значений) → Feature Engineering (44 фичи) → StandardScaler → ONNX LSTM → Прогноз CPU\\n- **Buffer Status** — статус буфера данных (нужно 24 временных точки для предсказания)\\n- **ML Prediction** — текущий прогноз нагрузки в процентах CPU\\n- **Current Metrics** — текущие метрики (CPU, память, сеть, диск)\\n- **Prediction History** — график истории предсказаний (последние 80 шагов)\\n\\n#### ⚙️ Tab 2: Scaling Algorithm (Алгоритм масштабирования)\\n- **9-step pipeline** — пошаговая визуализация алгоритма принятия решений:\\n 1. **EMA Smoothing** — экспоненциальное сглаживание\\n 2. **Hysteresis** — определение направления (UP/DOWN/HOLD)\\n 3. **Desired Replicas** — вычисление желаемого количества реплик\\n 4. **Clamp** — ограничение [min=2, max=8]\\n 5. **Rate Limit** — ограничение скорости изменения\\n 6. **Cooldown Check** — проверка периода охлаждения\\n 7. **Stabilization** — стабилизация при scale-down\\n 8. **No Change?** — проверка необходимости изменения\\n 9. **Commit** — фиксация решения\\n- **State Panel** — текущее состояние алгоритма (cooldown, direction, replicas)\\n- **CPU + Thresholds** — график CPU с порогами срабатывания\\n- **Replica Count** — сравнение количества реплик ML vs HPA\\n- **Decision Log** — лог принятых решений\\n\\n#### 🧪 Tab 3: Experiment (Сравнение экспериментов)\\n- **Comparison Chart** — сравнение HPA vs ML автомасштабирования:\\n - График CPU нагрузки\\n - График количества реплик (HPA в красном, ML в синем)\\n - График разницы в репликах (ML - HPA)\\n- **8 KPI карточек** — ключевые метрики сравнения:\\n - Avg Replicas — среднее количество реплик\\n - Replica-Minutes — общее потребление ресурсов\\n - SLA Violations — нарушения SLA\\n - Scaling Events — количество событий масштабирования\\n - Over-Provisioning % — избыточное выделение ресурсов\\n - Under-Provisioning % — недостаточное выделение ресурсов\\n - Reaction Time — время реакции на изменения\\n - Cost Savings % — экономия затрат\\n\\n### Управление в GUI\\n\\n- **▶ Play** — автоматическое воспроизведение шагов симуляции\\n- **⏭ Step** — пошаговое выполнение (один шаг = 5 минут реального времени)\\n- **↺ Reset** — сброс симуляции к началу\\n- **Speed slider** — регулировка скорости воспроизведения\\n\\n### Параметры в боковой панели (левая панель)\\n\\nВы можете изменять параметры алгоритма в реальном времени:\\n\\n- **Target Util %** (20-90%) — целевая утилизация CPU\\n- **EMA Alpha** (0.1-1.0) — коэффициент экспоненциального сглаживания\\n- **Safety Margin** (0.0-0.5) — запас прочности для проактивного масштабирования\\n- **Up Threshold ×** (1.0-1.5) — порог для масштабирования вверх\\n- **Down Threshold ×** (0.3-0.9) — порог для масштабирования вниз\\n\\nПосле изменения параметров нажмите **\\\"Apply & Re-run\\\"** для перезапуска эксперимента.\\n\\n### 4 встроенных сценария\\n\\n- **Stable** — стабильная нагрузка (25 часов)\\n- **Ramp** — постепенное увеличение нагрузки от 30% до 85% (33 часа)\\n- **Spike** — три всплеска нагрузки (29 часов)\\n- **Diurnal** — дневной цикл с пиками в рабочее время (48 часов)\\n\\n### Требования для запуска GUI\\n\\n```bash\\n# Установка зависимостей\\npoetry install\\n\\n# Проверка наличия тестовых данных\\nls -lh data/processed/test.csv\\nls -lh data/processed/scaler.pkl\\nls -lh data/processed/metadata.json\\n```\\n\\nЕсли данные отсутствуют, GUI автоматически создаст синтетический demo-сценарий (synthetic_demo).\\n\\n### Альтернатива: Статический анализ (без GUI)\\n\\nЕсли GUI не запускается или вам нужны статические графики:\\n\\n```bash\\n# Генерация графиков и таблиц\\npython scripts/experiment_analysis.py --output-dir results/experiments\\n```\\n\\n**Выходные файлы**:\\n- `scenario_*.png` — графики для каждого сценария\\n- `combined_metrics.png` — сравнение метрик\\n- `cost_savings.png` — график экономии\\n- `experiment_results.csv` — данные в CSV\\n- `experiment_table.tex` — таблица для LaTeX\\n\\n---\\n\\n## 2. Что учитывается при предсказании нагрузки, и что можно учитывать?\\n\\n### Что учитывается СЕЙЧАС в системе\\n\\nСистема использует **44 признака** для прогнозирования нагрузки CPU на 15 минут вперед:\\n\\n#### A. Базовые метрики (5 метрик)\\n1. **cpu_usage** — текущая утилизация CPU (%)\\n2. **mem_util** — утилизация памяти (%)\\n3. **net_in** — входящий сетевой трафик (KB/s)\\n4. **net_out** — исходящий сетевой трафик (KB/s)\\n5. **disk_io** — операции ввода-вывода диска\\n\\nДля Docker Compose окружения:\\n- **request_rate** — частота входящих запросов\\n- **latency_p50** — медианная задержка\\n- **latency_p95** — 95-й процентиль задержки\\n- **latency_p99** — 99-й процентиль задержки\\n- **active_jobs** — активные задачи (синтетическая метрика)\\n\\n#### B. Временные признаки (4 признака)\\n- **hour** — час суток (0-23)\\n- **day_of_week** — день недели (0=понедельник, 6=воскресенье)\\n- **is_weekend** — признак выходного дня (0/1)\\n- **minute_of_day** — минута дня (0-1439)\\n\\n**Зачем?** — Позволяет модели учитывать дневные и недельные паттерны (пиковые часы, ночное снижение нагрузки).\\n\\n#### C. Lag Features (16 признаков)\\nЗадержки (lags) для каждой базовой метрики:\\n- **Lag 1** — значение 1 минуту назад\\n- **Lag 5** — значение 5 минут назад\\n- **Lag 15** — значение 15 минут назад\\n- **Lag 30** — значение 30 минут назад\\n\\n**Зачем?** — LSTM получает \\\"память\\\" о недавнем прошлом, что критично для временных рядов.\\n\\n#### D. Rolling Window Features (15 признаков)\\nСкользящие средние для каждой базовой метрики:\\n- **Rolling mean 5** — среднее за последние 5 минут\\n- **Rolling mean 15** — среднее за последние 15 минут\\n- **Rolling mean 30** — среднее за последние 30 минут\\n\\n**Зачем?** — Сглаживание шума и выявление трендов.\\n\\n#### E. Sliding Window для LSTM\\n- **Sequence length**: 24 временных шага (2 часа истории по 5 минут)\\n- **Forecast horizon**: 3 шага вперед (15 минут в будущее)\\n\\n**Зачем?** — LSTM обучается на последовательностях, чтобы находить паттерны изменения нагрузки.\\n\\n### Что МОЖНО учитывать (расширение функционала)\\n\\n#### 1. Метрики приложения (Application-Level Metrics)\\n- **Request rate per endpoint** — нагрузка по конкретным API endpoints\\n- **Database query latency** — задержки запросов к БД\\n- **Error rate (4xx, 5xx)** — частота ошибок\\n- **Active connections** — количество активных соединений\\n- **Queue depth** — глубина очередей задач\\n- **Cache hit rate** — эффективность кеширования\\n\\n#### 2. Бизнес-метрики\\n- **User count** — количество активных пользователей\\n- **Transaction rate** — частота транзакций\\n- **Session duration** — длительность сессий\\n- **Order rate** (для e-commerce) — частота заказов\\n\\n#### 3. Инфраструктурные метрики\\n- **Node CPU/Memory pressure** — нагрузка на узлы кластера\\n- **Pod restart count** — частота перезапусков подов\\n- **Pending pods** — количество подов в очереди\\n- **Network bandwidth utilization** — использование сетевой полосы\\n\\n#### 4. Внешние факторы\\n- **Public holidays** — праздничные дни\\n- **Marketing campaigns** — запланированные маркетинговые кампании\\n- **Scheduled maintenance** — плановое обслуживание\\n- **External API latency** — задержки внешних API\\n- **Weather data** (для location-based сервисов) — погодные данные\\n\\n#### 5. Prophet-специфичные компоненты\\n- **Trend** — долгосрочный тренд\\n- **Seasonality** — сезонность (дневная, недельная, годовая)\\n- **Holidays** — влияние праздников\\n- **Changepoints** — точки изменения тренда\\n\\n#### 6. Дополнительные временные признаки\\n- **is_business_hours** — рабочие часы (9:00-18:00)\\n- **week_of_year** — неделя года\\n- **quarter** — квартал\\n- **is_month_start / is_month_end** — начало/конец месяца\\n\\n### Текущая конфигурация\\n\\nКонфигурация находится в `src/k8s_ml_predictive_autoscaling/preprocessor/config.yaml`:\\n\\n```yaml\\nfeatures:\\n enable_time_features: true\\n lags: [1, 5, 15, 30] # Look back 1, 5, 15, 30 minutes\\n rolling_windows: [5, 15, 30] # Rolling averages\\n\\nsliding_window:\\n sequence_length: 60 # 60 minutes of history for LSTM\\n forecast_steps: [5, 15, 30] # Predict 5, 15, 30 minutes ahead\\n stride: 5 # Create windows every 5 minutes\\n target_metric: request_rate # Predict request rate\\n```\\n\\n### Как добавить новые признаки?\\n\\n1. **Добавить метрику в Prometheus** — настроить экспорт метрики\\n2. **Обновить collector config** — добавить PromQL запрос в `src/k8s_ml_predictive_autoscaling/collector/config.yaml`\\n3. **Обновить preprocessor** — добавить метрику в `scaler_features` и `features`\\n4. **Переобучить модель** — запустить pipeline заново\\n\\n---\\n\\n## 3. Как это все работает?\\n\\n### Общая архитектура (MAPE Loop)\\n\\nСистема следует паттерну **MAPE loop**: **Monitor → Analyze → Plan → Execute**.\\n\\n```\\n┌─────────────────────────────────────────────────────────────────┐\\n│ MAPE LOOP │\\n├─────────────────────────────────────────────────────────────────┤\\n│ │\\n│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌────────┐ │\\n│ │ Monitor │────>│ Analyze │────>│ Plan │────>│Execute │ │\\n│ │(Metrics) │ │(ML Model)│ │(Resource │ │(K8s API│ │\\n│ │ │ │ │ │ Planner) │ │ / HPA) │ │\\n│ └──────────┘ └──────────┘ └──────────┘ └────────┘ │\\n│ ▲ │ │\\n│ │ │ │\\n│ └────────────────────────────────────────────────────┘ │\\n│ Feedback Loop │\\n└─────────────────────────────────────────────────────────────────┘\\n```\\n\\n### 1. Monitor (Мониторинг)\\n\\n**Компоненты**:\\n- **Prometheus** — сбор метрик с микросервисов\\n- **Collector** (`src/k8s_ml_predictive_autoscaling/collector/`) — экспорт метрик из Prometheus\\n\\n**Что происходит**:\\n1. Микросервисы экспонируют `/metrics` (Prometheus format)\\n2. Prometheus scraping каждые 10-30 секунд\\n3. Метрики сохраняются как временные ряды\\n4. Collector экспортирует данные в CSV для обучения\\n\\n**Метрики**:\\n- CPU, память, сеть, диск\\n- Request rate, latency (p50, p95, p99)\\n- Error rate, custom application metrics\\n\\n### 2. Analyze (Анализ — ML слой)\\n\\n**Компоненты**:\\n- **Preprocessor** (`src/k8s_ml_predictive_autoscaling/preprocessor/`) — препроцессинг данных\\n- **ML Models** (`models/lstm/`, `models/prophet/`, `models/hybrid/`) — обученные модели\\n- **Predictor Service** (`src/k8s_ml_predictive_autoscaling/predictor/`) — FastAPI сервис инференса\\n\\n#### Шаг 2.1: Препроцессинг\\n\\n```bash\\npoetry run python -m k8s_ml_predictive_autoscaling.preprocessor.pipeline \\\\\\n --config src/k8s_ml_predictive_autoscaling/preprocessor/config.yaml\\n```\\n\\n**Действия**:\\n1. **Загрузка данных** из `data/raw/*.csv`\\n2. **Resampling** — агрегация по 1-минутным интервалам\\n3. **Anomaly detection** — фильтрация аномалий (z-score > 3.5)\\n4. **Feature engineering**:\\n - Добавление временных признаков (hour, day_of_week, weekend)\\n - Добавление lag features (1, 5, 15, 30 минут назад)\\n - Добавление rolling mean (5, 15, 30 минут)\\n5. **Sliding window generation** — создание последовательностей для LSTM\\n6. **Normalization** — StandardScaler\\n7. **Train/Val/Test split** — 70% / 15% / 15%\\n\\n**Выход**:\\n- `data/processed/train.csv`, `validation.csv`, `test.csv`\\n- `data/processed/sequences_*.npz` — sliding window sequences\\n- `data/processed/scaler.pkl` — сохранённый StandardScaler\\n\\n#### Шаг 2.2: Обучение моделей\\n\\n**Prophet**:\\n```bash\\npoetry run python -m models.prophet.train\\n```\\n- Модель с трендом + сезонностью (дневная, недельная)\\n- Обучение: ~10-20 секунд\\n- Результат: R² = 0.44 (умеренная точность)\\n\\n**LSTM** (лучшая модель):\\n```bash\\npoetry run python -m models.lstm.train\\n```\\n- 2-layer LSTM, hidden_size=64, dropout=0.2\\n- 65,753 параметра\\n- Обучение: ~56 секунд\\n- Результат: **R² = 0.89** (высокая точность)\\n\\n**Hybrid Prophet+LSTM**:\\n```bash\\npoetry run python -m models.hybrid.train\\n```\\n- Prophet для тренда, LSTM для остатков\\n- Результат: R² = 0.68 (средняя точность)\\n\\n#### Шаг 2.3: Экспорт в ONNX\\n\\n```bash\\npoetry run python scripts/export_onnx.py\\n```\\n- Экспорт PyTorch модели в ONNX Runtime\\n- Ускорение инференса: **3.8-4.0x**\\n- Размер модели: 262 KB (обычный) / 78.7 KB (int8 quantization)\\n- Latency: 0.13 ms (vs 0.50 ms PyTorch)\\n\\n#### Шаг 2.4: Inference Service (FastAPI)\\n\\n```bash\\npoetry run python -m k8s_ml_predictive_autoscaling.predictor.app\\n```\\n\\n**API endpoints**:\\n- `POST /predict` — прогноз нагрузки на основе текущих метрик\\n- `GET /health` — health check\\n- `GET /metrics` — Prometheus metrics\\n\\n**Процесс инференса**:\\n1. Получение 24 последних значений метрик (2 часа истории)\\n2. Feature engineering (44 признака)\\n3. Normalization (StandardScaler)\\n4. ONNX inference\\n5. Inverse transform (денормализация)\\n6. Возврат прогноза CPU на 5, 15, 30 минут вперед\\n\\n### 3. Plan (Планирование ресурсов)\\n\\n**Компоненты**:\\n- **Resource Planner** (`src/k8s_ml_predictive_autoscaling/planner/`) — алгоритм планирования\\n\\n**9-Step Algorithm (детальный алгоритм планирования)**:\\n\\n#### Входные данные\\n- `predicted_cpu` — прогноз CPU от ML модели (%)\\n- `current_replicas` — текущее количество реплик\\n\\n#### Параметры\\n- `target_utilization` = 60% — целевая утилизация\\n- `smoothing_alpha` = 0.7 — коэффициент EMA\\n- `safety_margin` = 15% — запас прочности\\n- `scale_up_threshold` = 66% (1.1x target) — порог для масштабирования вверх\\n- `scale_down_threshold` = 42% (0.7x target) — порог для масштабирования вниз\\n\\n#### Шаги алгоритма\\n\\n**Step 1: EMA Smoothing**\\n```\\nsmoothed_cpu = alpha * predicted_cpu + (1 - alpha) * smoothed_cpu_prev\\n```\\nСглаживание шума, предотвращение реакции на случайные всплески.\\n\\n**Step 2: Hysteresis Direction**\\n```\\nif smoothed_cpu > scale_up_threshold:\\n direction = UP\\nelif smoothed_cpu < scale_down_threshold:\\n direction = DOWN\\nelse:\\n direction = HOLD # Dead zone — no action\\n```\\nГистерезис предотвращает осцилляции в зоне [42%, 66%].\\n\\n**Step 3: Compute Desired Replicas**\\n```\\ndesired_replicas = ceil(current_replicas * smoothed_cpu / target_utilization)\\n\\nif direction == UP:\\n desired_replicas = ceil(desired_replicas * (1 + safety_margin))\\n```\\nДобавление 15% запаса при масштабировании вверх для проактивности.\\n\\n**Step 4: Clamp to [min, max]**\\n```\\ndesired_replicas = max(2, min(8, desired_replicas))\\n```\\nОграничение диапазона реплик.\\n\\n**Step 5: Rate Limit**\\n```\\nif direction == UP:\\n desired_replicas = min(desired_replicas, current_replicas + 2)\\nelse:\\n desired_replicas = max(desired_replicas, current_replicas - 1)\\n```\\nОграничение скорости изменения: макс +2 вверх, макс -1 вниз.\\n\\n**Step 6: Cooldown Check**\\n```\\nif time_since_last_scale_up < 60s:\\n block UP\\nif time_since_last_scale_down < 300s:\\n block DOWN\\n```\\nПредотвращение частых изменений.\\n\\n**Step 7: Scale-Down Stabilization**\\n```\\nif direction == DOWN:\\n if consecutive_below_threshold < 3:\\n block DOWN # Need 3 consecutive low readings\\n```\\nКонсервативное масштабирование вниз (требуется 3 подряд низких значения).\\n\\n**Step 8: No-Change Check**\\n```\\nif desired_replicas == current_replicas:\\n skip execution\\n```\\n\\n**Step 9: Commit Decision**\\n```\\nif not blocked:\\n execute scale to desired_replicas\\n update cooldown timers\\n reset stabilization counter\\n```\\n\\n### 4. Execute (Исполнение)\\n\\n**Компоненты**:\\n- **Executor** (`src/k8s_ml_predictive_autoscaling/executor/`) — интеграция с Kubernetes API\\n\\n**Два режима интеграции**:\\n\\n#### Режим 1: HPA External Metrics\\n1. ML-прогнозы экспортируются как **external metrics** для HPA\\n2. Prometheus Adapter читает метрики\\n3. HPA использует external metrics вместо CPU/памяти\\n4. Kubernetes API автоматически масштабирует Deployment\\n\\n#### Режим 2: KEDA (Event-Driven Autoscaling)\\n1. ML-прогнозы экспортируются в Prometheus\\n2. KEDA ScaledObject использует Prometheus trigger\\n3. KEDA создаёт/изменяет HPA на основе ML-метрик\\n\\n**Fallback**:\\n- При недоступности ML-сервиса система откатывается на классический HPA по CPU\\n\\n### Полный цикл работы (пример)\\n\\n```\\nt=0:\\n 1. Prometheus scrapes metrics: CPU=45%, mem=60%, request_rate=1000 req/s\\n 2. Predictor Service получает последние 24 точки (2 часа)\\n 3. ML Model прогнозирует: CPU через 15 мин = 72%\\n 4. Planner:\\n - Smoothed: 0.7*72 + 0.3*65 = 69.9%\\n - Direction: UP (69.9% > 66%)\\n - Desired: ceil(3 * 69.9 / 60) = 4 replicas\\n - Safety: ceil(4 * 1.15) = 5 replicas\\n - Clamp: 5 (in range [2,8])\\n - Rate limit: min(5, 3+2) = 5 OK\\n - Cooldown: OK (no recent scale)\\n - Decision: SCALE 3 → 5\\n 5. Executor отправляет команду в Kubernetes API\\n 6. Kubernetes создаёт 2 новых пода\\n 7. Через 15 минут нагрузка действительно вырастает до 72%, но система уже готова!\\n\\nt=15:\\n Фактическое CPU = 72%, но с 5 репликами эффективная утилизация = 72%/5*3 = 43%\\n → Система проактивно предотвратила перегрузку!\\n```\\n\\n### Ключевые отличия от HPA\\n\\n| Аспект | HPA (реактивный) | ML Autoscaler (проактивный) |\\n|--------|------------------|---------------------------|\\n| Принятие решений | На основе текущих метрик | На основе прогноза на 15 минут |\\n| Реакция на спайк | После факта (задержка ~30-60с) | За 15 минут до спайка |\\n| Стабильность | Частые осцилляции | -50% scaling events |\\n| Over-provisioning | Минимальный | Выше (+15% safety margin) |\\n| SLA violations | Возможны при резких спайках | Практически исключены |\\n| Сложность | Простой (формула) | Высокая (ML pipeline) |\\n\\n### Метрики сравнения (из экспериментов)\\n\\n| Метрика | HPA | ML Autoscaler |\\n|---------|-----|---------------|\\n| Avg Replicas | 4.03 | 4.69 |\\n| Scaling Events | 17.0 | **8.5** (-50%) |\\n| SLA Violations | 0 | 0 |\\n| Under-Provisioning | 0.04% | 0.12% |\\n| Over-Provisioning | 47.0% | 53.6% |\\n| Cost Savings | baseline | -15.8% (выше затраты, но выше стабильность) |\\n\\n---\\n\\n## Дополнительные материалы\\n\\n### Документация\\n- **README.md** — общее описание проекта\\n- **ROADMAP.md** — план развития (Phase 0-8)\\n- **RUN.md** — команды для быстрого старта\\n- **docs/experiments.md** — методология экспериментов\\n- **docs/model-comparison-results.md** — сравнение моделей Prophet/LSTM/Hybrid\\n\\n### Команды для работы с системой\\n\\n```bash\\n# 1. Локальное окружение (Docker Compose)\\ncd docker\\nexport AUTOSCALER_API_TOKEN=\\\"your-token\\\"\\ndocker compose up --build -d\\n\\n# Доступы:\\n# - Demo services: http://localhost:8001-8003\\n# - Prometheus: http://localhost:9090\\n# - Grafana: http://localhost:3000\\n\\n# 2. Сбор данных\\npoetry run python -m k8s_ml_predictive_autoscaling.collector.collect_historical \\\\\\n --config src/k8s_ml_predictive_autoscaling/collector/config.yaml\\n\\n# 3. Препроцессинг\\npoetry run python -m k8s_ml_predictive_autoscaling.preprocessor.pipeline \\\\\\n --config src/k8s_ml_predictive_autoscaling/preprocessor/config.yaml\\n\\n# 4. Обучение модели\\npoetry run python -m models.lstm.train\\n\\n# 5. Экспорт в ONNX\\npoetry run python scripts/export_onnx.py\\n\\n# 6. Inference service\\npoetry run python -m k8s_ml_predictive_autoscaling.predictor.app\\n\\n# 7. Эксперименты (статические графики)\\npython scripts/experiment_analysis.py --output-dir results/experiments\\n\\n# 8. GUI Dashboard\\npython scripts/experiment_gui.py\\n```\\n\\n### Kubernetes deployment\\n\\n```bash\\n# Создание кластера\\nkind create cluster --config k8s/kind-config.yaml --name autoscaling\\n\\n# Установка компонентов\\nkubectl apply -f k8s/manifests/namespace.yaml\\nkubectl apply -f k8s/manifests/prometheus/\\nkubectl apply -f k8s/manifests/demo-service-deployment.yaml\\nkubectl apply -f k8s/manifests/hpa/baseline-hpa.yaml\\n\\n# Деплой ML Predictor\\nkubectl apply -f k8s/manifests/ml-predictor-deployment.yaml\\n\\n# Проверка\\nkubectl get pods -n predictive-autoscaling\\nkubectl get hpa -n predictive-autoscaling\\n```\\n\\n---\\n\\n## Заключение\\n\\n**Основная идея проекта**: Перейти от **реактивного масштабирования** (HPA реагирует после факта) к **проактивному** (ML предсказывает нагрузку за 15 минут и заранее масштабирует).\\n\\n**Преимущества**:\\n- Меньше перегрузок и SLA violations\\n- Стабильнее работа (-50% scaling events)\\n- Лучше предсказуемость расходов\\n\\n**Недостатки**:\\n- Выше сложность (ML pipeline, обучение, ONNX, мониторинг)\\n- Выше over-provisioning (~15% запаса)\\n- Требует данных для обучения (минимум 48 часов)\\n\\n**Рекомендация**: Для продакшена начинайте с консервативных параметров (safety_margin=0.15, stabilization=3), затем постепенно снижайте safety margin на основе реального SLA.\\n\",\"createdAt\":\"2026-02-07T18:05:57Z\",\"includesCreatedEdit\":false,\"isMinimized\":false,\"minimizedReason\":\"\",\"reactionGroups\":[],\"url\":\"https://github.com/VisageDvachevsky/k8sautoscl/issues/1#issuecomment-3865009838\",\"viewerDidAuthor\":true}],\"title\":\"qna\"}", | |
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как еще можно улучшить интерфейс, удобство, анимации всякие, проработку\\r\\nможет добавить больше инфы, возможностей всяких\\r\\n\\r\\nподумай что можно сделать и сделай это 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Please wait for the session to finish, and provide your feedback._\",\"author_association\":\"NONE\",\"pin\":null,\"reactions\":{\"url\":\"https://api.github.com/repos/VisageDvachevsky/k8sautoscl/issues/comments/3865024005/reactions\",\"total_count\":0,\"+1\":0,\"-1\":0,\"laugh\":0,\"hooray\":0,\"confused\":0,\"heart\":0,\"rocket\":0,\"eyes\":0},\"performed_via_github_app\":null}]", | |
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как еще можно улучшить интерфейс, удобство, анимации всякие, проработку\\r\\nможет добавить больше инфы, возможностей всяких\\r\\n\\r\\nподумай что можно сделать и сделай это 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| "stdout": "total 296\ndrwxrwxr-x 3 hive hive 4096 Feb 7 19:13 .\ndrwxrwxr-x 15 hive hive 4096 Feb 7 19:13 ..\n-rw-rw-r-- 1 hive hive 28582 Feb 7 19:13 QNA.md\n-rw-rw-r-- 1 hive hive 225094 Feb 7 19:13 architecture-diagram.png\n-rw-rw-r-- 1 hive hive 4904 Feb 7 19:13 eda-report.md\ndrwxrwxr-x 2 hive hive 4096 Feb 7 19:13 eda_figures\n-rw-rw-r-- 1 hive hive 9069 Feb 7 19:13 experiments.md\n-rw-rw-r-- 1 hive hive 4713 Feb 7 19:13 model-comparison-results.md\n-rw-rw-r-- 1 hive hive 1491 Feb 7 19:13 model-comparison-table.csv\n-rw-rw-r-- 1 hive hive 3517 Feb 7 19:13 setup-guide.md", | |
| "stderr": "", | |
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| [2026-02-07T18:13:56.058Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-6", | |
| "id": "msg_011d16vfF3uDN7u24ytDyNfs", | |
| "type": "message", | |
| "role": "assistant", | |
| "content": [ | |
| { | |
| "type": "tool_use", | |
| "id": "toolu_01YUqtEcjnHQbKWdfkJGKjin", | |
| "name": "Bash", | |
| "input": { | |
| "command": "wc -l scripts/experiment_gui.py", | |
| "description": "Check GUI file size" | |
| } | |
| } | |
| ], | |
| "stop_reason": null, | |
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| "service_tier": "standard", | |
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| "session_id": "ce1ab353-0721-47e7-a373-84e062534c7f", | |
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| [2026-02-07T18:13:56.134Z] [INFO] { | |
| "type": "user", | |
| "message": { | |
| "role": "user", | |
| "content": [ | |
| { | |
| "tool_use_id": "toolu_01YUqtEcjnHQbKWdfkJGKjin", | |
| "type": "tool_result", | |
| "content": "866 scripts/experiment_gui.py", | |
| "is_error": false | |
| } | |
| ] | |
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| "stdout": "866 scripts/experiment_gui.py", | |
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| [2026-02-07T18:13:57.993Z] [INFO] { | |
| "type": "assistant", | |
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| "model": "claude-opus-4-6", | |
| "id": "msg_011UihX1T3Y6mujzA9np5g91", | |
| "type": "message", | |
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| { | |
| "type": "text", | |
| "text": "Let me read the GUI file and understand the current implementation, along with the experiment simulator that powers it." | |
| } | |
| ], | |
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| [2026-02-07T18:13:58.955Z] [INFO] { | |
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| "model": "claude-opus-4-6", | |
| "id": "msg_011UihX1T3Y6mujzA9np5g91", | |
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| { | |
| "type": "tool_use", | |
| "id": "toolu_01LHJpBLaMYMiFGFvGTweTJM", | |
| "name": "Read", | |
| "input": { | |
| "file_path": "/tmp/gh-issue-solver-1770487990892/scripts/experiment_gui.py" | |
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| "uuid": "7b96e3ca-ca72-4bef-bd73-d31ce076af33" | |
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| [2026-02-07T18:13:59.415Z] [INFO] { | |
| "type": "user", | |
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| { | |
| "tool_use_id": "toolu_01LHJpBLaMYMiFGFvGTweTJM", | |
| "type": "tool_result", | |
| "content": " 1→\"\"\"ML Autoscaler — Experiment Dashboard (CustomTkinter).\n 2→\n 3→Interactive visualization of:\n 4→ - Neural network internals (buffer, features, ONNX inference)\n 5→ - Scaling algorithm 9-step pipeline\n 6→ - HPA vs ML autoscaler comparison\n 7→\n 8→Usage:\n 9→ python scripts/experiment_gui.py\n 10→\"\"\"\n 11→\n 12→from __future__ import annotations\n 13→\n 14→import math\n 15→import sys\n 16→from pathlib import Path\n 17→\n 18→import customtkinter as ctk\n 19→import matplotlib\n 20→import numpy as np\n 21→\n 22→matplotlib.use(\"TkAgg\")\n 23→import matplotlib.pyplot as plt\n 24→from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\n 25→from matplotlib.figure import Figure\n 26→\n 27→# Add project root to path for imports\n 28→ROOT = Path(__file__).resolve().parent.parent\n 29→sys.path.insert(0, str(ROOT / \"src\"))\n 30→sys.path.insert(0, str(ROOT))\n 31→\n 32→from scripts.experiment_scenarios import get_all_scenarios, Scenario\n 33→from scripts.experiment_simulator import (\n 34→ ExperimentRunner,\n 35→ HPAConfig,\n 36→ MLAutoscalerSimulator,\n 37→ MLConfig,\n 38→ StepRecord,\n 39→ compute_metrics,\n 40→)\n 41→\n 42→# ---------------------------------------------------------------------------\n 43→# Color palette (unified with Grafana dashboards)\n 44→# ---------------------------------------------------------------------------\n 45→\n 46→C = {\n 47→ \"bg\": \"#1a1a2e\",\n 48→ \"bg_card\": \"#16213e\",\n 49→ \"sidebar\": \"#0f3460\",\n 50→ \"accent\": \"#e94560\",\n 51→ \"green\": \"#73BF69\",\n 52→ \"yellow\": \"#FADE2A\",\n 53→ \"orange\": \"#FF9830\",\n 54→ \"red\": \"#F2495C\",\n 55→ \"blue\": \"#5794F2\",\n 56→ \"purple\": \"#B877D9\",\n 57→ \"text\": \"#e0e0e0\",\n 58→ \"text_dim\": \"#8892b0\",\n 59→ \"grid\": \"#2a2a4a\",\n 60→}\n 61→\n 62→# Matplotlib dark style\n 63→plt.rcParams.update({\n 64→ \"figure.facecolor\": C[\"bg_card\"],\n 65→ \"axes.facecolor\": C[\"bg\"],\n 66→ \"axes.edgecolor\": C[\"grid\"],\n 67→ \"axes.labelcolor\": C[\"text\"],\n 68→ \"text.color\": C[\"text\"],\n 69→ \"xtick.color\": C[\"text_dim\"],\n 70→ \"ytick.color\": C[\"text_dim\"],\n 71→ \"grid.color\": C[\"grid\"],\n 72→ \"grid.alpha\": 0.3,\n 73→ \"font.size\": 9,\n 74→ \"legend.facecolor\": C[\"bg_card\"],\n 75→ \"legend.edgecolor\": C[\"grid\"],\n 76→})\n 77→\n 78→\n 79→# ---------------------------------------------------------------------------\n 80→# Utility: embed a matplotlib figure in a CTkFrame\n 81→# ---------------------------------------------------------------------------\n 82→\n 83→\n 84→def embed_figure(parent: ctk.CTkFrame, fig: Figure) -> FigureCanvasTkAgg:\n 85→ canvas = FigureCanvasTkAgg(fig, master=parent)\n 86→ canvas.draw()\n 87→ canvas.get_tk_widget().pack(fill=\"both\", expand=True)\n 88→ return canvas\n 89→\n 90→\n 91→# ---------------------------------------------------------------------------\n 92→# KPI Card widget\n 93→# ---------------------------------------------------------------------------\n 94→\n 95→\n 96→class KPICard(ctk.CTkFrame):\n 97→ \"\"\"Compact KPI card: title + HPA value + ML value.\"\"\"\n 98→\n 99→ def __init__(self, master, title: str, **kw):\n 100→ super().__init__(master, fg_color=C[\"bg_card\"], corner_radius=8, **kw)\n 101→ self._title_lbl = ctk.CTkLabel(\n 102→ self, text=title, font=(\"Consolas\", 11), text_color=C[\"text_dim\"]\n 103→ )\n 104→ self._title_lbl.pack(pady=(6, 2))\n 105→\n 106→ row = ctk.CTkFrame(self, fg_color=\"transparent\")\n 107→ row.pack(pady=(0, 6))\n 108→ self._hpa_lbl = ctk.CTkLabel(\n 109→ row, text=\"HPA: --\", font=(\"Consolas\", 13, \"bold\"), text_color=C[\"red\"]\n 110→ )\n 111→ self._hpa_lbl.pack(side=\"left\", padx=6)\n 112→ self._ml_lbl = ctk.CTkLabel(\n 113→ row, text=\"ML: --\", font=(\"Consolas\", 13, \"bold\"), text_color=C[\"blue\"]\n 114→ )\n 115→ self._ml_lbl.pack(side=\"left\", padx=6)\n 116→\n 117→ def set_values(self, hpa_val: str, ml_val: str):\n 118→ self._hpa_lbl.configure(text=f\"HPA: {hpa_val}\")\n 119→ self._ml_lbl.configure(text=f\"ML: {ml_val}\")\n 120→\n 121→\n 122→# ---------------------------------------------------------------------------\n 123→# Pipeline Step block\n 124→# ---------------------------------------------------------------------------\n 125→\n 126→\n 127→class PipelineBlock(ctk.CTkFrame):\n 128→ \"\"\"One step in the 9-step scaling algorithm visualization.\"\"\"\n 129→\n 130→ def __init__(self, master, step_num: int, title: str, **kw):\n 131→ super().__init__(master, fg_color=C[\"bg_card\"], corner_radius=6,\n 132→ border_width=1, border_color=C[\"grid\"], **kw)\n 133→ self.configure(width=115, height=80)\n 134→ self.pack_propagate(False)\n 135→ ctk.CTkLabel(\n 136→ self, text=f\"Step {step_num}\", font=(\"Consolas\", 8),\n 137→ text_color=C[\"text_dim\"]\n 138→ ).pack(pady=(4, 0))\n 139→ ctk.CTkLabel(\n 140→ self, text=title, font=(\"Consolas\", 9, \"bold\"),\n 141→ text_color=C[\"text\"]\n 142→ ).pack()\n 143→ self._value_lbl = ctk.CTkLabel(\n 144→ self, text=\"—\", font=(\"Consolas\", 10), text_color=C[\"text_dim\"],\n 145→ wraplength=100\n 146→ )\n 147→ self._value_lbl.pack(pady=(2, 4))\n 148→\n 149→ def update(self, text: str, status: str = \"active\"):\n 150→ colors = {\n 151→ \"active\": C[\"green\"],\n 152→ \"blocked\": C[\"yellow\"],\n 153→ \"skipped\": C[\"text_dim\"],\n 154→ \"executed\": C[\"accent\"],\n 155→ }\n 156→ self._value_lbl.configure(text=text, text_color=colors.get(status, C[\"text\"]))\n 157→ border = {\n 158→ \"active\": C[\"green\"],\n 159→ \"blocked\": C[\"yellow\"],\n 160→ \"executed\": C[\"accent\"],\n 161→ }\n 162→ self.configure(border_color=border.get(status, C[\"grid\"]))\n 163→\n 164→\n 165→# ---------------------------------------------------------------------------\n 166→# Main Application\n 167→# ---------------------------------------------------------------------------\n 168→\n 169→\n 170→class ExperimentApp(ctk.CTk):\n 171→ def __init__(self):\n 172→ super().__init__()\n 173→ self.title(\"ML Autoscaler — Experiment Dashboard\")\n 174→ self.geometry(\"1440x920\")\n 175→ ctk.set_appearance_mode(\"dark\")\n 176→\n 177→ # State\n 178→ self._scenarios: list[Scenario] = []\n 179→ self._current_scenario_idx = 0\n 180→ self._step = 0\n 181→ self._playing = False\n 182→ self._speed_ms = 200\n 183→ self._history: list[StepRecord] = []\n 184→\n 185→ # ML simulator state for step-by-step\n 186→ self._ml_sim: MLAutoscalerSimulator | None = None\n 187→ self._hpa_replicas_trace: list[int] = []\n 188→ self._ml_replicas_trace: list[int] = []\n 189→ self._actual_trace: list[float] = []\n 190→ self._predicted_trace: list[float] = []\n 191→ self._smoothed_trace: list[float] = []\n 192→ self._decisions_log: list[str] = []\n 193→\n 194→ # Build UI\n 195→ self._build_controls()\n 196→ self._build_sidebar()\n 197→ self._build_tabview()\n 198→\n 199→ # Load data\n 200→ self._load_scenarios()\n 201→\n 202→ # -------------------------------------------------------------------\n 203→ # Data loading\n 204→ # -------------------------------------------------------------------\n 205→\n 206→ def _load_scenarios(self):\n 207→ try:\n 208→ self._scenarios = get_all_scenarios(\n 209→ ROOT / \"data\" / \"processed\" / \"test.csv\",\n 210→ ROOT / \"data\" / \"processed\" / \"scaler.pkl\",\n 211→ ROOT / \"data\" / \"processed\" / \"metadata.json\",\n 212→ )\n 213→ except Exception as e:\n 214→ self._scenarios = [self._synthetic_fallback()]\n 215→ self._select_scenario(0)\n 216→\n 217→ def _synthetic_fallback(self) -> Scenario:\n 218→ \"\"\"Create a simple synthetic scenario if data files are missing.\"\"\"\n 219→ n = 200\n 220→ rng = np.random.RandomState(42)\n 221→ t = np.linspace(0, 4 * np.pi, n)\n 222→ actual = 50 + 25 * np.sin(t) + rng.normal(0, 3, n)\n 223→ predicted = np.roll(actual, -3)\n 224→ predicted[-3:] = actual[-3:]\n 225→ predicted += rng.normal(0, 2, n)\n 226→ return Scenario(\n 227→ name=\"synthetic_demo\",\n 228→ description=\"Synthetic sine wave demo\",\n 229→ actual_cpu=np.clip(actual, 10, 100),\n 230→ predicted_cpu=np.clip(predicted, 10, 100),\n 231→ duration_hours=n * 5 / 60,\n 232→ )\n 233→\n 234→ def _select_scenario(self, idx: int):\n 235→ self._current_scenario_idx = idx\n 236→ self._reset_simulation()\n 237→ self._update_all()\n 238→\n 239→ def _reset_simulation(self):\n 240→ self._step = 0\n 241→ self._playing = False\n 242→ sc = self._scenarios[self._current_scenario_idx]\n 243→\n 244→ # Run full experiment for Tab 3\n 245→ runner = ExperimentRunner(\n 246→ sc.actual_cpu, sc.predicted_cpu,\n 247→ ml_config=self._build_ml_config(),\n 248→ )\n 249→ self._history, self._full_metrics = runner.run()\n 250→\n 251→ # Reset step-by-step state\n 252→ self._ml_sim = MLAutoscalerSimulator(self._build_ml_config())\n 253→ self._hpa_replicas_trace = []\n 254→ self._ml_replicas_trace = []\n 255→ self._actual_trace = []\n 256→ self._predicted_trace = []\n 257→ self._smoothed_trace = []\n 258→ self._decisions_log = []\n 259→\n 260→ def _build_ml_config(self) -> MLConfig:\n 261→ return MLConfig(\n 262→ target_utilization=self._target_var.get(),\n 263→ smoothing_alpha=self._alpha_var.get(),\n 264→ safety_margin=self._safety_var.get(),\n 265→ scale_up_threshold_factor=self._up_thresh_var.get(),\n 266→ scale_down_threshold_factor=self._down_thresh_var.get(),\n 267→ )\n 268→\n 269→ # -------------------------------------------------------------------\n 270→ # Control bar (top)\n 271→ # -------------------------------------------------------------------\n 272→\n 273→ def _build_controls(self):\n 274→ bar = ctk.CTkFrame(self, fg_color=C[\"bg_card\"], height=50)\n 275→ bar.pack(fill=\"x\", padx=8, pady=(8, 4))\n 276→\n 277→ ctk.CTkLabel(\n 278→ bar, text=\"ML Autoscaler — Experiment Dashboard\",\n 279→ font=(\"Consolas\", 16, \"bold\"), text_color=C[\"text\"]\n 280→ ).pack(side=\"left\", padx=12)\n 281→\n 282→ # Transport controls\n 283→ self._play_btn = ctk.CTkButton(\n 284→ bar, text=\"▶ Play\", width=90, fg_color=C[\"green\"],\n 285→ text_color=\"#000\", hover_color=\"#5ea554\",\n 286→ command=self._toggle_play\n 287→ )\n 288→ self._play_btn.pack(side=\"left\", padx=4)\n 289→\n 290→ ctk.CTkButton(\n 291→ bar, text=\"⏭ Step\", width=90, fg_color=C[\"blue\"],\n 292→ text_color=\"#000\", hover_color=\"#4678c9\",\n 293→ command=self._do_step\n 294→ ).pack(side=\"left\", padx=4)\n 295→\n 296→ ctk.CTkButton(\n 297→ bar, text=\"↺ Reset\", width=90, fg_color=C[\"orange\"],\n 298→ text_color=\"#000\", hover_color=\"#d97e24\",\n 299→ command=lambda: (self._reset_simulation(), self._update_all())\n 300→ ).pack(side=\"left\", padx=4)\n 301→\n 302→ # Step counter\n 303→ self._step_lbl = ctk.CTkLabel(\n 304→ bar, text=\"Step: 0 / 0\", font=(\"Consolas\", 12),\n 305→ text_color=C[\"text_dim\"]\n 306→ )\n 307→ self._step_lbl.pack(side=\"right\", padx=12)\n 308→\n 309→ # Speed slider\n 310→ ctk.CTkLabel(bar, text=\"Speed:\", text_color=C[\"text_dim\"]).pack(side=\"right\")\n 311→ self._speed_slider = ctk.CTkSlider(\n 312→ bar, from_=50, to=1000, width=120,\n 313→ command=lambda v: setattr(self, \"_speed_ms\", int(v))\n 314→ )\n 315→ self._speed_slider.set(200)\n 316→ self._speed_slider.pack(side=\"right\", padx=4)\n 317→\n 318→ def _toggle_play(self):\n 319→ self._playing = not self._playing\n 320→ self._play_btn.configure(\n 321→ text=\"⏸ Pause\" if self._playing else \"▶ Play\",\n 322→ fg_color=C[\"yellow\"] if self._playing else C[\"green\"],\n 323→ )\n 324→ if self._playing:\n 325→ self._auto_step()\n 326→\n 327→ def _auto_step(self):\n 328→ if not self._playing:\n 329→ return\n 330→ sc = self._scenarios[self._current_scenario_idx]\n 331→ if self._step < len(sc.actual_cpu):\n 332→ self._do_step()\n 333→ self.after(self._speed_ms, self._auto_step)\n 334→ else:\n 335→ self._playing = False\n 336→ self._play_btn.configure(text=\"▶ Play\", fg_color=C[\"green\"])\n 337→\n 338→ def _do_step(self):\n 339→ sc = self._scenarios[self._current_scenario_idx]\n 340→ if self._step >= len(sc.actual_cpu):\n 341→ return\n 342→\n 343→ t = self._step\n 344→ actual = float(sc.actual_cpu[t])\n 345→ predicted = float(sc.predicted_cpu[t])\n 346→\n 347→ # ML autoscaler step\n 348→ ml_replicas, ml_dir = self._ml_sim.step(predicted, t)\n 349→ smoothed = self._ml_sim._smoothed or predicted\n 350→\n 351→ # HPA (from pre-computed history)\n 352→ hpa_replicas = self._history[t].hpa_replicas if t < len(self._history) else 2\n 353→\n 354→ self._actual_trace.append(actual)\n 355→ self._predicted_trace.append(predicted)\n 356→ self._smoothed_trace.append(smoothed)\n 357→ self._ml_replicas_trace.append(ml_replicas)\n 358→ self._hpa_replicas_trace.append(hpa_replicas)\n 359→ self._decisions_log.append(\n 360→ f\"[t={t:>3}] {ml_dir:>4} | ML:{ml_replicas} HPA:{hpa_replicas} | \"\n 361→ f\"actual={actual:.1f}% pred={predicted:.1f}% smooth={smoothed:.1f}%\"\n 362→ )\n 363→\n 364→ self._step += 1\n 365→ self._update_all()\n 366→\n 367→ # -------------------------------------------------------------------\n 368→ # Sidebar (parameters)\n 369→ # -------------------------------------------------------------------\n 370→\n 371→ def _build_sidebar(self):\n 372→ sidebar = ctk.CTkFrame(self, fg_color=C[\"sidebar\"], width=220, corner_radius=0)\n 373→ sidebar.pack(side=\"left\", fill=\"y\", padx=0, pady=0)\n 374→ sidebar.pack_propagate(False)\n 375→\n 376→ ctk.CTkLabel(\n 377→ sidebar, text=\"Parameters\", font=(\"Consolas\", 14, \"bold\"),\n 378→ text_color=C[\"text\"]\n 379→ ).pack(pady=(12, 8))\n 380→\n 381→ def make_slider(parent, label, from_, to, default, resolution=None):\n 382→ ctk.CTkLabel(\n 383→ parent, text=label, font=(\"Consolas\", 10), text_color=C[\"text_dim\"]\n 384→ ).pack(pady=(8, 0), padx=8, anchor=\"w\")\n 385→ var = ctk.DoubleVar(value=default)\n 386→ val_lbl = ctk.CTkLabel(\n 387→ parent, text=f\"{default}\", font=(\"Consolas\", 10), text_color=C[\"text\"]\n 388→ )\n 389→ val_lbl.pack(padx=8, anchor=\"e\")\n 390→\n 391→ def on_change(v):\n 392→ val_lbl.configure(text=f\"{float(v):.2f}\")\n 393→\n 394→ slider = ctk.CTkSlider(\n 395→ parent, from_=from_, to=to, variable=var,\n 396→ width=180, command=on_change\n 397→ )\n 398→ slider.pack(padx=8)\n 399→ return var\n 400→\n 401→ self._target_var = make_slider(sidebar, \"Target Util %\", 20, 90, 60)\n 402→ self._alpha_var = make_slider(sidebar, \"EMA Alpha\", 0.1, 1.0, 0.7)\n 403→ self._safety_var = make_slider(sidebar, \"Safety Margin\", 0.0, 0.5, 0.15)\n 404→ self._up_thresh_var = make_slider(sidebar, \"Up Threshold ×\", 1.0, 1.5, 1.1)\n 405→ self._down_thresh_var = make_slider(sidebar, \"Down Threshold ×\", 0.3, 0.9, 0.7)\n 406→\n 407→ ctk.CTkButton(\n 408→ sidebar, text=\"Apply & Re-run\", fg_color=C[\"accent\"],\n 409→ text_color=\"#fff\", hover_color=\"#c73550\",\n 410→ command=lambda: (self._reset_simulation(), self._update_all())\n 411→ ).pack(pady=16, padx=12, fill=\"x\")\n 412→\n 413→ # Scenario selector\n 414→ ctk.CTkLabel(\n 415→ sidebar, text=\"Scenario\", font=(\"Consolas\", 14, \"bold\"),\n 416→ text_color=C[\"text\"]\n 417→ ).pack(pady=(16, 4))\n 418→\n 419→ self._scenario_btns: list[ctk.CTkButton] = []\n 420→ names = [\"Stable\", \"Ramp\", \"Spike\", \"Diurnal\"]\n 421→ for i, name in enumerate(names):\n 422→ btn = ctk.CTkButton(\n 423→ sidebar, text=name, width=180, height=28,\n 424→ fg_color=C[\"bg_card\"], hover_color=C[\"blue\"],\n 425→ command=lambda idx=i: self._select_scenario(idx)\n 426→ )\n 427→ btn.pack(pady=2, padx=12)\n 428→ self._scenario_btns.append(btn)\n 429→\n 430→ # -------------------------------------------------------------------\n 431→ # Tab view (3 tabs)\n 432→ # -------------------------------------------------------------------\n 433→\n 434→ def _build_tabview(self):\n 435→ self._tabview = ctk.CTkTabview(self, fg_color=C[\"bg\"])\n 436→ self._tabview.pack(fill=\"both\", expand=True, padx=8, pady=(0, 8))\n 437→\n 438→ self._tab_nn = self._tabview.add(\"Neural Network\")\n 439→ self._tab_algo = self._tabview.add(\"Scaling Algorithm\")\n 440→ self._tab_exp = self._tabview.add(\"Experiment\")\n 441→\n 442→ self._build_nn_tab()\n 443→ self._build_algo_tab()\n 444→ self._build_exp_tab()\n 445→\n 446→ # --- Tab 1: Neural Network ---\n 447→\n 448→ def _build_nn_tab(self):\n 449→ tab = self._tab_nn\n 450→\n 451→ # Top: pipeline diagram\n 452→ pipe_frame = ctk.CTkFrame(tab, fg_color=C[\"bg_card\"], corner_radius=8, height=90)\n 453→ pipe_frame.pack(fill=\"x\", padx=8, pady=4)\n 454→ pipe_frame.pack_propagate(False)\n 455→\n 456→ ctk.CTkLabel(\n 457→ pipe_frame, text=\"ONNX Inference Pipeline\",\n 458→ font=(\"Consolas\", 12, \"bold\"), text_color=C[\"text\"]\n 459→ ).pack(pady=(6, 4))\n 460→\n 461→ blocks_frame = ctk.CTkFrame(pipe_frame, fg_color=\"transparent\")\n 462→ blocks_frame.pack()\n 463→ stages = [\n 464→ (\"Raw Metrics\", \"5 values\", C[\"green\"]),\n 465→ (\"→\", \"\", C[\"text_dim\"]),\n 466→ (\"Feature Eng.\", \"44 features\", C[\"purple\"]),\n 467→ (\"→\", \"\", C[\"text_dim\"]),\n 468→ (\"StandardScaler\", \"normalize\", C[\"orange\"]),\n 469→ (\"→\", \"\", C[\"text_dim\"]),\n 470→ (\"ONNX LSTM\", \"(1,24,44)→(1,1)\", C[\"blue\"]),\n 471→ (\"→\", \"\", C[\"text_dim\"]),\n 472→ (\"Inverse Scale\", \"→ CPU %\", C[\"green\"]),\n 473→ ]\n 474→ for title, sub, color in stages:\n 475→ if title == \"→\":\n 476→ ctk.CTkLabel(\n 477→ blocks_frame, text=\"→\", font=(\"Consolas\", 16, \"bold\"),\n 478→ text_color=C[\"text_dim\"]\n 479→ ).pack(side=\"left\", padx=2)\n 480→ else:\n 481→ f = ctk.CTkFrame(blocks_frame, fg_color=C[\"bg\"], corner_radius=6,\n 482→ border_width=1, border_color=color)\n 483→ f.pack(side=\"left\", padx=4)\n 484→ ctk.CTkLabel(f, text=title, font=(\"Consolas\", 9, \"bold\"),\n 485→ text_color=color).pack(padx=6, pady=(2, 0))\n 486→ ctk.CTkLabel(f, text=sub, font=(\"Consolas\", 8),\n 487→ text_color=C[\"text_dim\"]).pack(padx=6, pady=(0, 2))\n 488→\n 489→ # Middle: prediction output + buffer\n 490→ mid = ctk.CTkFrame(tab, fg_color=\"transparent\")\n 491→ mid.pack(fill=\"x\", padx=8, pady=4)\n 492→\n 493→ # Buffer status\n 494→ buf_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8, width=250)\n 495→ buf_frame.pack(side=\"left\", padx=(0, 8), fill=\"y\")\n 496→ buf_frame.pack_propagate(False)\n 497→ ctk.CTkLabel(buf_frame, text=\"Buffer Status\", font=(\"Consolas\", 11, \"bold\"),\n 498→ text_color=C[\"text\"]).pack(pady=(8, 4))\n 499→ self._buf_progress = ctk.CTkProgressBar(buf_frame, width=200)\n 500→ self._buf_progress.pack(pady=4, padx=12)\n 501→ self._buf_progress.set(0)\n 502→ self._buf_lbl = ctk.CTkLabel(\n 503→ buf_frame, text=\"0 / 24 NOT READY\", font=(\"Consolas\", 10),\n 504→ text_color=C[\"red\"]\n 505→ )\n 506→ self._buf_lbl.pack()\n 507→\n 508→ # Prediction big number\n 509→ pred_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8, width=200)\n 510→ pred_frame.pack(side=\"left\", padx=8, fill=\"y\")\n 511→ pred_frame.pack_propagate(False)\n 512→ ctk.CTkLabel(pred_frame, text=\"ML Prediction\", font=(\"Consolas\", 11, \"bold\"),\n 513→ text_color=C[\"text\"]).pack(pady=(8, 2))\n 514→ self._pred_big = ctk.CTkLabel(\n 515→ pred_frame, text=\"—\", font=(\"Consolas\", 28, \"bold\"),\n 516→ text_color=C[\"blue\"]\n 517→ )\n 518→ self._pred_big.pack(pady=4)\n 519→ self._pred_actual = ctk.CTkLabel(\n 520→ pred_frame, text=\"Actual: —\", font=(\"Consolas\", 10),\n 521→ text_color=C[\"text_dim\"]\n 522→ )\n 523→ self._pred_actual.pack()\n 524→\n 525→ # Raw metrics panel\n 526→ raw_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8)\n 527→ raw_frame.pack(side=\"left\", fill=\"both\", expand=True, padx=8)\n 528→ ctk.CTkLabel(raw_frame, text=\"Current Metrics\", font=(\"Consolas\", 11, \"bold\"),\n 529→ text_color=C[\"text\"]).pack(pady=(8, 4))\n 530→ self._raw_labels: dict[str, ctk.CTkLabel] = {}\n 531→ for metric in [\"cpu_usage\", \"mem_util\", \"net_in\", \"net_out\", \"disk_io\"]:\n 532→ lbl = ctk.CTkLabel(raw_frame, text=f\"{metric}: —\",\n 533→ font=(\"Consolas\", 10), text_color=C[\"text_dim\"])\n 534→ lbl.pack(anchor=\"w\", padx=12)\n 535→ self._raw_labels[metric] = lbl\n 536→\n 537→ # Bottom: prediction history chart\n 538→ self._nn_fig, self._nn_ax = plt.subplots(1, 1, figsize=(10, 3), dpi=80)\n 539→ self._nn_canvas = embed_figure(tab, self._nn_fig)\n 540→\n 541→ # --- Tab 2: Scaling Algorithm ---\n 542→\n 543→ def _build_algo_tab(self):\n 544→ tab = self._tab_algo\n 545→\n 546→ # 9-step pipeline\n 547→ pipe = ctk.CTkFrame(tab, fg_color=\"transparent\")\n 548→ pipe.pack(fill=\"x\", padx=4, pady=4)\n 549→\n 550→ step_names = [\n 551→ \"EMA\", \"Hysteresis\", \"Desired\", \"Clamp\",\n 552→ \"Rate Limit\", \"Cooldown\", \"Stabilize\", \"No Change?\", \"Commit\"\n 553→ ]\n 554→ self._algo_blocks: list[PipelineBlock] = []\n 555→ for i, name in enumerate(step_names):\n 556→ if i > 0:\n 557→ ctk.CTkLabel(pipe, text=\"→\", font=(\"Consolas\", 14, \"bold\"),\n 558→ text_color=C[\"text_dim\"]).pack(side=\"left\", padx=1)\n 559→ block = PipelineBlock(pipe, i + 1, name)\n 560→ block.pack(side=\"left\", padx=2)\n 561→ self._algo_blocks.append(block)\n 562→\n 563→ # State panel\n 564→ state_frame = ctk.CTkFrame(tab, fg_color=C[\"bg_card\"], corner_radius=8, height=40)\n 565→ state_frame.pack(fill=\"x\", padx=8, pady=4)\n 566→ self._state_lbl = ctk.CTkLabel(\n 567→ state_frame,\n 568→ text=\"Cooldown UP: — | Cooldown DOWN: — | Consec. Below: — | Direction: —\",\n 569→ font=(\"Consolas\", 10), text_color=C[\"text_dim\"]\n 570→ )\n 571→ self._state_lbl.pack(pady=6)\n 572→\n 573→ # Charts: CPU + Replicas side by side\n 574→ charts = ctk.CTkFrame(tab, fg_color=\"transparent\")\n 575→ charts.pack(fill=\"both\", expand=True, padx=4, pady=4)\n 576→\n 577→ left = ctk.CTkFrame(charts, fg_color=\"transparent\")\n 578→ left.pack(side=\"left\", fill=\"both\", expand=True)\n 579→ self._algo_cpu_fig, self._algo_cpu_ax = plt.subplots(1, 1, figsize=(5.5, 3), dpi=80)\n 580→ self._algo_cpu_canvas = embed_figure(left, self._algo_cpu_fig)\n 581→\n 582→ right = ctk.CTkFrame(charts, fg_color=\"transparent\")\n 583→ right.pack(side=\"left\", fill=\"both\", expand=True)\n 584→ self._algo_rep_fig, self._algo_rep_ax = plt.subplots(1, 1, figsize=(5.5, 3), dpi=80)\n 585→ self._algo_rep_canvas = embed_figure(right, self._algo_rep_fig)\n 586→\n 587→ # Decision log\n 588→ self._decision_log = ctk.CTkTextbox(\n 589→ tab, height=100, font=(\"Consolas\", 9), fg_color=C[\"bg\"],\n 590→ text_color=C[\"text_dim\"]\n 591→ )\n 592→ self._decision_log.pack(fill=\"x\", padx=8, pady=(0, 4))\n 593→\n 594→ # --- Tab 3: Experiment Comparison ---\n 595→\n 596→ def _build_exp_tab(self):\n 597→ tab = self._tab_exp\n 598→\n 599→ # Main comparison chart\n 600→ self._exp_fig, self._exp_axes = plt.subplots(2, 1, figsize=(10, 5), dpi=80,\n 601→ gridspec_kw={\"height_ratios\": [2, 1]})\n 602→ self._exp_canvas = embed_figure(tab, self._exp_fig)\n 603→\n 604→ # KPI cards grid\n 605→ cards_frame = ctk.CTkFrame(tab, fg_color=\"transparent\")\n 606→ cards_frame.pack(fill=\"x\", padx=8, pady=4)\n 607→\n 608→ kpi_names = [\n 609→ \"Avg Replicas\", \"Replica-Min\", \"SLA Violations\", \"Scaling Events\",\n 610→ \"Over-Prov %\", \"Under-Prov %\", \"Reaction Time\", \"Cost Savings %\"\n 611→ ]\n 612→ self._kpi_cards: list[KPICard] = []\n 613→ for i, name in enumerate(kpi_names):\n 614→ card = KPICard(cards_frame, name)\n 615→ card.grid(row=i // 4, column=i % 4, padx=4, pady=4, sticky=\"nsew\")\n 616→ self._kpi_cards.append(card)\n 617→ for c in range(4):\n 618→ cards_frame.columnconfigure(c, weight=1)\n 619→\n 620→ # -------------------------------------------------------------------\n 621→ # Update all visuals\n 622→ # -------------------------------------------------------------------\n 623→\n 624→ def _update_all(self):\n 625→ sc = self._scenarios[self._current_scenario_idx]\n 626→ total = len(sc.actual_cpu)\n 627→ self._step_lbl.configure(text=f\"Step: {self._step} / {total}\")\n 628→\n 629→ # Highlight active scenario button\n 630→ for i, btn in enumerate(self._scenario_btns):\n 631→ btn.configure(\n 632→ fg_color=C[\"blue\"] if i == self._current_scenario_idx else C[\"bg_card\"]\n 633→ )\n 634→\n 635→ self._update_nn_tab()\n 636→ self._update_algo_tab()\n 637→ self._update_exp_tab()\n 638→\n 639→ def _update_nn_tab(self):\n 640→ n = self._step\n 641→\n 642→ # Buffer status\n 643→ buf_fill = min(n, 24)\n 644→ self._buf_progress.set(buf_fill / 24)\n 645→ ready = n >= 24\n 646→ self._buf_lbl.configure(\n 647→ text=f\"{min(n, 54)} / 24 {'READY' if ready else 'FILLING...'}\",\n 648→ text_color=C[\"green\"] if ready else C[\"red\"]\n 649→ )\n 650→\n 651→ # Current prediction\n 652→ if n > 0:\n 653→ pred = self._predicted_trace[-1]\n 654→ actual = self._actual_trace[-1]\n 655→ color = C[\"green\"] if pred < 42 else C[\"yellow\"] if pred < 66 else C[\"orange\"] if pred < 85 else C[\"red\"]\n 656→ self._pred_big.configure(text=f\"{pred:.1f}%\", text_color=color)\n 657→ self._pred_actual.configure(text=f\"Actual: {actual:.1f}%\")\n 658→\n 659→ # Raw metrics (simulated from CPU)\n 660→ self._raw_labels[\"cpu_usage\"].configure(text=f\"cpu_usage: {actual:.1f}%\")\n 661→ self._raw_labels[\"mem_util\"].configure(text=f\"mem_util: {actual * 0.6:.1f}%\")\n 662→ self._raw_labels[\"net_in\"].configure(text=f\"net_in: {actual * 1.2:.0f} KB/s\")\n 663→ self._raw_labels[\"net_out\"].configure(text=f\"net_out: {actual * 0.8:.0f} KB/s\")\n 664→ self._raw_labels[\"disk_io\"].configure(text=f\"disk_io: {actual * 0.3:.1f}%\")\n 665→\n 666→ # Prediction history chart\n 667→ self._nn_ax.clear()\n 668→ if n > 1:\n 669→ x = range(max(0, n - 80), n)\n 670→ a_slice = self._actual_trace[max(0, n - 80):]\n 671→ p_slice = self._predicted_trace[max(0, n - 80):]\n 672→ self._nn_ax.plot(x, a_slice, color=C[\"green\"], lw=1.5, label=\"Actual CPU\")\n 673→ self._nn_ax.plot(x, p_slice, color=C[\"purple\"], lw=1.5, ls=\"--\", label=\"Predicted\")\n 674→ self._nn_ax.axhline(60, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.5)\n 675→ self._nn_ax.set_ylabel(\"CPU %\")\n 676→ self._nn_ax.set_ylim(0, 100)\n 677→ self._nn_ax.legend(loc=\"upper right\", fontsize=8)\n 678→ self._nn_ax.grid(True, alpha=0.2)\n 679→ self._nn_ax.set_title(\"Prediction History (last 80 steps)\", fontsize=10)\n 680→ self._nn_fig.tight_layout()\n 681→ self._nn_canvas.draw_idle()\n 682→\n 683→ def _update_algo_tab(self):\n 684→ n = self._step\n 685→ if n == 0:\n 686→ for block in self._algo_blocks:\n 687→ block.update(\"—\", \"skipped\")\n 688→ return\n 689→\n 690→ # Get current step details\n 691→ pred = self._predicted_trace[-1]\n 692→ smoothed = self._smoothed_trace[-1]\n 693→ ml_replicas = self._ml_replicas_trace[-1]\n 694→ prev_replicas = self._ml_replicas_trace[-2] if n > 1 else 2\n 695→\n 696→ target = self._target_var.get()\n 697→ up_thresh = target * self._up_thresh_var.get()\n 698→ down_thresh = target * self._down_thresh_var.get()\n 699→\n 700→ # Determine direction\n 701→ if smoothed > up_thresh:\n 702→ direction = \"UP\"\n 703→ elif smoothed < down_thresh:\n 704→ direction = \"DOWN\"\n 705→ else:\n 706→ direction = \"HOLD\"\n 707→\n 708→ # Update blocks\n 709→ self._algo_blocks[0].update(f\"{pred:.1f}→{smoothed:.1f}%\", \"active\")\n 710→ self._algo_blocks[1].update(\n 711→ f\"{smoothed:.0f}% {'>' if direction == 'UP' else '<' if direction == 'DOWN' else 'in'} \"\n 712→ f\"[{down_thresh:.0f},{up_thresh:.0f}]\\n→{direction}\",\n 713→ \"active\" if direction != \"HOLD\" else \"blocked\"\n 714→ )\n 715→\n 716→ if direction == \"HOLD\":\n 717→ for i in range(2, 9):\n 718→ self._algo_blocks[i].update(\"—\", \"skipped\")\n 719→ else:\n 720→ raw_desired = math.ceil(prev_replicas * smoothed / target)\n 721→ if direction == \"UP\":\n 722→ raw_desired = math.ceil(raw_desired * (1 + self._safety_var.get()))\n 723→ self._algo_blocks[2].update(f\"{prev_replicas}×{smoothed:.0f}/{target:.0f}\\n={raw_desired}\", \"active\")\n 724→ clamped = max(2, min(8, raw_desired))\n 725→ self._algo_blocks[3].update(f\"[2,8]→{clamped}\", \"active\")\n 726→ rate_lim = min(clamped, prev_replicas + 2) if direction == \"UP\" else max(clamped, prev_replicas - 1)\n 727→ self._algo_blocks[4].update(f\"±limit→{rate_lim}\", \"active\")\n 728→ self._algo_blocks[5].update(\"OFF ✓\", \"active\")\n 729→ self._algo_blocks[6].update(\"N/A\" if direction == \"UP\" else \"check\", \"active\")\n 730→\n 731→ executed = ml_replicas != prev_replicas\n 732→ self._algo_blocks[7].update(\n 733→ f\"{ml_replicas}{'≠' if executed else '='}{prev_replicas}\",\n 734→ \"active\" if executed else \"blocked\"\n 735→ )\n 736→ if executed:\n 737→ self._algo_blocks[8].update(f\"{prev_replicas}→{ml_replicas}\", \"executed\")\n 738→ else:\n 739→ self._algo_blocks[8].update(\"hold\", \"blocked\")\n 740→\n 741→ # State panel\n 742→ sim = self._ml_sim\n 743→ self._state_lbl.configure(\n 744→ text=f\"Direction: {direction} | \"\n 745→ f\"Consec. Below: {sim._consecutive_below} | \"\n 746→ f\"Replicas: {ml_replicas} | \"\n 747→ f\"Smoothed: {smoothed:.1f}%\"\n 748→ )\n 749→\n 750→ # CPU chart\n 751→ self._algo_cpu_ax.clear()\n 752→ if n > 1:\n 753→ x = range(n)\n 754→ self._algo_cpu_ax.plot(x, self._smoothed_trace, color=C[\"blue\"], lw=1.5, label=\"Smoothed\")\n 755→ self._algo_cpu_ax.plot(x, self._actual_trace, color=C[\"green\"], lw=1, alpha=0.5, label=\"Actual\")\n 756→ self._algo_cpu_ax.axhline(up_thresh, color=C[\"red\"], ls=\"--\", lw=0.8, label=f\"Up {up_thresh:.0f}%\")\n 757→ self._algo_cpu_ax.axhline(down_thresh, color=C[\"orange\"], ls=\"--\", lw=0.8, label=f\"Down {down_thresh:.0f}%\")\n 758→ self._algo_cpu_ax.axhline(target, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.6)\n 759→ self._algo_cpu_ax.axhspan(down_thresh, up_thresh, alpha=0.08, color=C[\"yellow\"])\n 760→ self._algo_cpu_ax.set_ylim(0, 100)\n 761→ self._algo_cpu_ax.set_ylabel(\"CPU %\")\n 762→ self._algo_cpu_ax.legend(loc=\"upper right\", fontsize=7)\n 763→ self._algo_cpu_ax.grid(True, alpha=0.2)\n 764→ self._algo_cpu_ax.set_title(\"CPU + Thresholds\", fontsize=10)\n 765→ self._algo_cpu_fig.tight_layout()\n 766→ self._algo_cpu_canvas.draw_idle()\n 767→\n 768→ # Replica chart\n 769→ self._algo_rep_ax.clear()\n 770→ if n > 1:\n 771→ x = range(n)\n 772→ self._algo_rep_ax.step(x, self._ml_replicas_trace, color=C[\"blue\"], lw=2, where=\"post\", label=\"ML Replicas\")\n 773→ self._algo_rep_ax.step(x, self._hpa_replicas_trace, color=C[\"red\"], lw=1.5, where=\"post\", alpha=0.6, label=\"HPA Replicas\")\n 774→ self._algo_rep_ax.axhline(2, color=C[\"text_dim\"], ls=\":\", lw=0.7, alpha=0.5)\n 775→ self._algo_rep_ax.axhline(8, color=C[\"text_dim\"], ls=\":\", lw=0.7, alpha=0.5)\n 776→ self._algo_rep_ax.set_ylim(0, 10)\n 777→ self._algo_rep_ax.set_ylabel(\"Replicas\")\n 778→ self._algo_rep_ax.legend(loc=\"upper right\", fontsize=7)\n 779→ self._algo_rep_ax.grid(True, alpha=0.2)\n 780→ self._algo_rep_ax.set_title(\"Replica Count\", fontsize=10)\n 781→ self._algo_rep_fig.tight_layout()\n 782→ self._algo_rep_canvas.draw_idle()\n 783→\n 784→ # Decision log\n 785→ self._decision_log.configure(state=\"normal\")\n 786→ self._decision_log.delete(\"1.0\", \"end\")\n 787→ # Show last 20 entries\n 788→ for line in self._decisions_log[-20:]:\n 789→ self._decision_log.insert(\"end\", line + \"\\n\")\n 790→ self._decision_log.configure(state=\"disabled\")\n 791→ self._decision_log.see(\"end\")\n 792→\n 793→ def _update_exp_tab(self):\n 794→ if not hasattr(self, \"_full_metrics\"):\n 795→ return\n 796→ metrics = self._full_metrics\n 797→ history = self._history\n 798→\n 799→ # Top chart: Actual CPU + replicas\n 800→ ax1, ax2 = self._exp_axes\n 801→ ax1.clear()\n 802→ ax2.clear()\n 803→\n 804→ n = len(history)\n 805→ if n > 0:\n 806→ x = range(n)\n 807→ actual = [r.actual_cpu for r in history]\n 808→ hpa_r = [r.hpa_replicas for r in history]\n 809→ ml_r = [r.ml_replicas for r in history]\n 810→\n 811→ # CPU trace (background)\n 812→ ax1.fill_between(x, actual, alpha=0.15, color=C[\"text_dim\"])\n 813→ ax1.plot(x, actual, color=C[\"text_dim\"], lw=0.8, alpha=0.5, label=\"Actual CPU\")\n 814→\n 815→ # Replicas\n 816→ ax1.step(x, [r * 10 for r in hpa_r], color=C[\"red\"], lw=1.5,\n 817→ where=\"post\", label=\"HPA Replicas (×10)\")\n 818→ ax1.step(x, [r * 10 for r in ml_r], color=C[\"blue\"], lw=2,\n 819→ where=\"post\", label=\"ML Replicas (×10)\")\n 820→\n 821→ # SLA threshold\n 822→ target = self._target_var.get()\n 823→ ax1.axhline(target * 1.2, color=C[\"red\"], ls=\":\", lw=0.8, alpha=0.5, label=\"SLA Limit\")\n 824→\n 825→ ax1.set_ylabel(\"CPU % / Replicas×10\")\n 826→ ax1.set_ylim(0, 100)\n 827→ ax1.legend(loc=\"upper right\", fontsize=7, ncol=2)\n 828→ ax1.set_title(f\"Experiment: {self._scenarios[self._current_scenario_idx].name}\", fontsize=11)\n 829→ ax1.grid(True, alpha=0.2)\n 830→\n 831→ # Bottom chart: replica diff\n 832→ diff = [m - h for m, h in zip(ml_r, hpa_r)]\n 833→ colors_bar = [C[\"green\"] if d < 0 else C[\"red\"] if d > 0 else C[\"text_dim\"] for d in diff]\n 834→ ax2.bar(x, diff, color=colors_bar, alpha=0.7, width=1.0)\n 835→ ax2.axhline(0, color=C[\"text_dim\"], lw=0.5)\n 836→ ax2.set_ylabel(\"ML - HPA\")\n 837→ ax2.set_xlabel(\"Step\")\n 838→ ax2.grid(True, alpha=0.2)\n 839→ ax2.set_title(\"Replica Difference (negative = ML uses fewer)\", fontsize=9)\n 840→\n 841→ self._exp_fig.tight_layout()\n 842→ self._exp_canvas.draw_idle()\n 843→\n 844→ # KPI cards\n 845→ self._kpi_cards[0].set_values(f\"{metrics.hpa_avg_replicas:.1f}\", f\"{metrics.ml_avg_replicas:.1f}\")\n 846→ self._kpi_cards[1].set_values(f\"{metrics.hpa_replica_minutes:.0f}\", f\"{metrics.ml_replica_minutes:.0f}\")\n 847→ self._kpi_cards[2].set_values(f\"{metrics.hpa_sla_violations}\", f\"{metrics.ml_sla_violations}\")\n 848→ self._kpi_cards[3].set_values(f\"{metrics.hpa_scaling_events}\", f\"{metrics.ml_scaling_events}\")\n 849→ self._kpi_cards[4].set_values(f\"{metrics.hpa_over_provisioning_pct:.1f}%\", f\"{metrics.ml_over_provisioning_pct:.1f}%\")\n 850→ self._kpi_cards[5].set_values(f\"{metrics.hpa_under_provisioning_pct:.1f}%\", f\"{metrics.ml_under_provisioning_pct:.1f}%\")\n 851→ self._kpi_cards[6].set_values(f\"{metrics.hpa_avg_reaction_time:.1f}\", f\"{metrics.ml_avg_reaction_time:.1f}\")\n 852→ self._kpi_cards[7].set_values(f\"—\", f\"{metrics.cost_savings_pct:+.1f}%\")\n 853→\n 854→\n 855→# ---------------------------------------------------------------------------\n 856→# Entry point\n 857→# ---------------------------------------------------------------------------\n 858→\n 859→\n 860→def main():\n 861→ app = ExperimentApp()\n 862→ app.mainloop()\n 863→\n 864→\n 865→if __name__ == \"__main__\":\n 866→ main()\n 867→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n" | |
| } | |
| ] | |
| }, | |
| "parent_tool_use_id": null, | |
| "session_id": "ce1ab353-0721-47e7-a373-84e062534c7f", | |
| "uuid": "6f9cfa23-63ff-4951-bfa4-861b868e09d9", | |
| "tool_use_result": { | |
| "type": "text", | |
| "file": { | |
| "filePath": "/tmp/gh-issue-solver-1770487990892/scripts/experiment_gui.py", | |
| "content": "\"\"\"ML Autoscaler — Experiment Dashboard (CustomTkinter).\n\nInteractive visualization of:\n - Neural network internals (buffer, features, ONNX inference)\n - Scaling algorithm 9-step pipeline\n - HPA vs ML autoscaler comparison\n\nUsage:\n python scripts/experiment_gui.py\n\"\"\"\n\nfrom __future__ import annotations\n\nimport math\nimport sys\nfrom pathlib import Path\n\nimport customtkinter as ctk\nimport matplotlib\nimport numpy as np\n\nmatplotlib.use(\"TkAgg\")\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nfrom matplotlib.figure import Figure\n\n# Add project root to path for imports\nROOT = Path(__file__).resolve().parent.parent\nsys.path.insert(0, str(ROOT / \"src\"))\nsys.path.insert(0, str(ROOT))\n\nfrom scripts.experiment_scenarios import get_all_scenarios, Scenario\nfrom scripts.experiment_simulator import (\n ExperimentRunner,\n HPAConfig,\n MLAutoscalerSimulator,\n MLConfig,\n StepRecord,\n compute_metrics,\n)\n\n# ---------------------------------------------------------------------------\n# Color palette (unified with Grafana dashboards)\n# ---------------------------------------------------------------------------\n\nC = {\n \"bg\": \"#1a1a2e\",\n \"bg_card\": \"#16213e\",\n \"sidebar\": \"#0f3460\",\n \"accent\": \"#e94560\",\n \"green\": \"#73BF69\",\n \"yellow\": \"#FADE2A\",\n \"orange\": \"#FF9830\",\n \"red\": \"#F2495C\",\n \"blue\": \"#5794F2\",\n \"purple\": \"#B877D9\",\n \"text\": \"#e0e0e0\",\n \"text_dim\": \"#8892b0\",\n \"grid\": \"#2a2a4a\",\n}\n\n# Matplotlib dark style\nplt.rcParams.update({\n \"figure.facecolor\": C[\"bg_card\"],\n \"axes.facecolor\": C[\"bg\"],\n \"axes.edgecolor\": C[\"grid\"],\n \"axes.labelcolor\": C[\"text\"],\n \"text.color\": C[\"text\"],\n \"xtick.color\": C[\"text_dim\"],\n \"ytick.color\": C[\"text_dim\"],\n \"grid.color\": C[\"grid\"],\n \"grid.alpha\": 0.3,\n \"font.size\": 9,\n \"legend.facecolor\": C[\"bg_card\"],\n \"legend.edgecolor\": C[\"grid\"],\n})\n\n\n# ---------------------------------------------------------------------------\n# Utility: embed a matplotlib figure in a CTkFrame\n# ---------------------------------------------------------------------------\n\n\ndef embed_figure(parent: ctk.CTkFrame, fig: Figure) -> FigureCanvasTkAgg:\n canvas = FigureCanvasTkAgg(fig, master=parent)\n canvas.draw()\n canvas.get_tk_widget().pack(fill=\"both\", expand=True)\n return canvas\n\n\n# ---------------------------------------------------------------------------\n# KPI Card widget\n# ---------------------------------------------------------------------------\n\n\nclass KPICard(ctk.CTkFrame):\n \"\"\"Compact KPI card: title + HPA value + ML value.\"\"\"\n\n def __init__(self, master, title: str, **kw):\n super().__init__(master, fg_color=C[\"bg_card\"], corner_radius=8, **kw)\n self._title_lbl = ctk.CTkLabel(\n self, text=title, font=(\"Consolas\", 11), text_color=C[\"text_dim\"]\n )\n self._title_lbl.pack(pady=(6, 2))\n\n row = ctk.CTkFrame(self, fg_color=\"transparent\")\n row.pack(pady=(0, 6))\n self._hpa_lbl = ctk.CTkLabel(\n row, text=\"HPA: --\", font=(\"Consolas\", 13, \"bold\"), text_color=C[\"red\"]\n )\n self._hpa_lbl.pack(side=\"left\", padx=6)\n self._ml_lbl = ctk.CTkLabel(\n row, text=\"ML: --\", font=(\"Consolas\", 13, \"bold\"), text_color=C[\"blue\"]\n )\n self._ml_lbl.pack(side=\"left\", padx=6)\n\n def set_values(self, hpa_val: str, ml_val: str):\n self._hpa_lbl.configure(text=f\"HPA: {hpa_val}\")\n self._ml_lbl.configure(text=f\"ML: {ml_val}\")\n\n\n# ---------------------------------------------------------------------------\n# Pipeline Step block\n# ---------------------------------------------------------------------------\n\n\nclass PipelineBlock(ctk.CTkFrame):\n \"\"\"One step in the 9-step scaling algorithm visualization.\"\"\"\n\n def __init__(self, master, step_num: int, title: str, **kw):\n super().__init__(master, fg_color=C[\"bg_card\"], corner_radius=6,\n border_width=1, border_color=C[\"grid\"], **kw)\n self.configure(width=115, height=80)\n self.pack_propagate(False)\n ctk.CTkLabel(\n self, text=f\"Step {step_num}\", font=(\"Consolas\", 8),\n text_color=C[\"text_dim\"]\n ).pack(pady=(4, 0))\n ctk.CTkLabel(\n self, text=title, font=(\"Consolas\", 9, \"bold\"),\n text_color=C[\"text\"]\n ).pack()\n self._value_lbl = ctk.CTkLabel(\n self, text=\"—\", font=(\"Consolas\", 10), text_color=C[\"text_dim\"],\n wraplength=100\n )\n self._value_lbl.pack(pady=(2, 4))\n\n def update(self, text: str, status: str = \"active\"):\n colors = {\n \"active\": C[\"green\"],\n \"blocked\": C[\"yellow\"],\n \"skipped\": C[\"text_dim\"],\n \"executed\": C[\"accent\"],\n }\n self._value_lbl.configure(text=text, text_color=colors.get(status, C[\"text\"]))\n border = {\n \"active\": C[\"green\"],\n \"blocked\": C[\"yellow\"],\n \"executed\": C[\"accent\"],\n }\n self.configure(border_color=border.get(status, C[\"grid\"]))\n\n\n# ---------------------------------------------------------------------------\n# Main Application\n# ---------------------------------------------------------------------------\n\n\nclass ExperimentApp(ctk.CTk):\n def __init__(self):\n super().__init__()\n self.title(\"ML Autoscaler — Experiment Dashboard\")\n self.geometry(\"1440x920\")\n ctk.set_appearance_mode(\"dark\")\n\n # State\n self._scenarios: list[Scenario] = []\n self._current_scenario_idx = 0\n self._step = 0\n self._playing = False\n self._speed_ms = 200\n self._history: list[StepRecord] = []\n\n # ML simulator state for step-by-step\n self._ml_sim: MLAutoscalerSimulator | None = None\n self._hpa_replicas_trace: list[int] = []\n self._ml_replicas_trace: list[int] = []\n self._actual_trace: list[float] = []\n self._predicted_trace: list[float] = []\n self._smoothed_trace: list[float] = []\n self._decisions_log: list[str] = []\n\n # Build UI\n self._build_controls()\n self._build_sidebar()\n self._build_tabview()\n\n # Load data\n self._load_scenarios()\n\n # -------------------------------------------------------------------\n # Data loading\n # -------------------------------------------------------------------\n\n def _load_scenarios(self):\n try:\n self._scenarios = get_all_scenarios(\n ROOT / \"data\" / \"processed\" / \"test.csv\",\n ROOT / \"data\" / \"processed\" / \"scaler.pkl\",\n ROOT / \"data\" / \"processed\" / \"metadata.json\",\n )\n except Exception as e:\n self._scenarios = [self._synthetic_fallback()]\n self._select_scenario(0)\n\n def _synthetic_fallback(self) -> Scenario:\n \"\"\"Create a simple synthetic scenario if data files are missing.\"\"\"\n n = 200\n rng = np.random.RandomState(42)\n t = np.linspace(0, 4 * np.pi, n)\n actual = 50 + 25 * np.sin(t) + rng.normal(0, 3, n)\n predicted = np.roll(actual, -3)\n predicted[-3:] = actual[-3:]\n predicted += rng.normal(0, 2, n)\n return Scenario(\n name=\"synthetic_demo\",\n description=\"Synthetic sine wave demo\",\n actual_cpu=np.clip(actual, 10, 100),\n predicted_cpu=np.clip(predicted, 10, 100),\n duration_hours=n * 5 / 60,\n )\n\n def _select_scenario(self, idx: int):\n self._current_scenario_idx = idx\n self._reset_simulation()\n self._update_all()\n\n def _reset_simulation(self):\n self._step = 0\n self._playing = False\n sc = self._scenarios[self._current_scenario_idx]\n\n # Run full experiment for Tab 3\n runner = ExperimentRunner(\n sc.actual_cpu, sc.predicted_cpu,\n ml_config=self._build_ml_config(),\n )\n self._history, self._full_metrics = runner.run()\n\n # Reset step-by-step state\n self._ml_sim = MLAutoscalerSimulator(self._build_ml_config())\n self._hpa_replicas_trace = []\n self._ml_replicas_trace = []\n self._actual_trace = []\n self._predicted_trace = []\n self._smoothed_trace = []\n self._decisions_log = []\n\n def _build_ml_config(self) -> MLConfig:\n return MLConfig(\n target_utilization=self._target_var.get(),\n smoothing_alpha=self._alpha_var.get(),\n safety_margin=self._safety_var.get(),\n scale_up_threshold_factor=self._up_thresh_var.get(),\n scale_down_threshold_factor=self._down_thresh_var.get(),\n )\n\n # -------------------------------------------------------------------\n # Control bar (top)\n # -------------------------------------------------------------------\n\n def _build_controls(self):\n bar = ctk.CTkFrame(self, fg_color=C[\"bg_card\"], height=50)\n bar.pack(fill=\"x\", padx=8, pady=(8, 4))\n\n ctk.CTkLabel(\n bar, text=\"ML Autoscaler — Experiment Dashboard\",\n font=(\"Consolas\", 16, \"bold\"), text_color=C[\"text\"]\n ).pack(side=\"left\", padx=12)\n\n # Transport controls\n self._play_btn = ctk.CTkButton(\n bar, text=\"▶ Play\", width=90, fg_color=C[\"green\"],\n text_color=\"#000\", hover_color=\"#5ea554\",\n command=self._toggle_play\n )\n self._play_btn.pack(side=\"left\", padx=4)\n\n ctk.CTkButton(\n bar, text=\"⏭ Step\", width=90, fg_color=C[\"blue\"],\n text_color=\"#000\", hover_color=\"#4678c9\",\n command=self._do_step\n ).pack(side=\"left\", padx=4)\n\n ctk.CTkButton(\n bar, text=\"↺ Reset\", width=90, fg_color=C[\"orange\"],\n text_color=\"#000\", hover_color=\"#d97e24\",\n command=lambda: (self._reset_simulation(), self._update_all())\n ).pack(side=\"left\", padx=4)\n\n # Step counter\n self._step_lbl = ctk.CTkLabel(\n bar, text=\"Step: 0 / 0\", font=(\"Consolas\", 12),\n text_color=C[\"text_dim\"]\n )\n self._step_lbl.pack(side=\"right\", padx=12)\n\n # Speed slider\n ctk.CTkLabel(bar, text=\"Speed:\", text_color=C[\"text_dim\"]).pack(side=\"right\")\n self._speed_slider = ctk.CTkSlider(\n bar, from_=50, to=1000, width=120,\n command=lambda v: setattr(self, \"_speed_ms\", int(v))\n )\n self._speed_slider.set(200)\n self._speed_slider.pack(side=\"right\", padx=4)\n\n def _toggle_play(self):\n self._playing = not self._playing\n self._play_btn.configure(\n text=\"⏸ Pause\" if self._playing else \"▶ Play\",\n fg_color=C[\"yellow\"] if self._playing else C[\"green\"],\n )\n if self._playing:\n self._auto_step()\n\n def _auto_step(self):\n if not self._playing:\n return\n sc = self._scenarios[self._current_scenario_idx]\n if self._step < len(sc.actual_cpu):\n self._do_step()\n self.after(self._speed_ms, self._auto_step)\n else:\n self._playing = False\n self._play_btn.configure(text=\"▶ Play\", fg_color=C[\"green\"])\n\n def _do_step(self):\n sc = self._scenarios[self._current_scenario_idx]\n if self._step >= len(sc.actual_cpu):\n return\n\n t = self._step\n actual = float(sc.actual_cpu[t])\n predicted = float(sc.predicted_cpu[t])\n\n # ML autoscaler step\n ml_replicas, ml_dir = self._ml_sim.step(predicted, t)\n smoothed = self._ml_sim._smoothed or predicted\n\n # HPA (from pre-computed history)\n hpa_replicas = self._history[t].hpa_replicas if t < len(self._history) else 2\n\n self._actual_trace.append(actual)\n self._predicted_trace.append(predicted)\n self._smoothed_trace.append(smoothed)\n self._ml_replicas_trace.append(ml_replicas)\n self._hpa_replicas_trace.append(hpa_replicas)\n self._decisions_log.append(\n f\"[t={t:>3}] {ml_dir:>4} | ML:{ml_replicas} HPA:{hpa_replicas} | \"\n f\"actual={actual:.1f}% pred={predicted:.1f}% smooth={smoothed:.1f}%\"\n )\n\n self._step += 1\n self._update_all()\n\n # -------------------------------------------------------------------\n # Sidebar (parameters)\n # -------------------------------------------------------------------\n\n def _build_sidebar(self):\n sidebar = ctk.CTkFrame(self, fg_color=C[\"sidebar\"], width=220, corner_radius=0)\n sidebar.pack(side=\"left\", fill=\"y\", padx=0, pady=0)\n sidebar.pack_propagate(False)\n\n ctk.CTkLabel(\n sidebar, text=\"Parameters\", font=(\"Consolas\", 14, \"bold\"),\n text_color=C[\"text\"]\n ).pack(pady=(12, 8))\n\n def make_slider(parent, label, from_, to, default, resolution=None):\n ctk.CTkLabel(\n parent, text=label, font=(\"Consolas\", 10), text_color=C[\"text_dim\"]\n ).pack(pady=(8, 0), padx=8, anchor=\"w\")\n var = ctk.DoubleVar(value=default)\n val_lbl = ctk.CTkLabel(\n parent, text=f\"{default}\", font=(\"Consolas\", 10), text_color=C[\"text\"]\n )\n val_lbl.pack(padx=8, anchor=\"e\")\n\n def on_change(v):\n val_lbl.configure(text=f\"{float(v):.2f}\")\n\n slider = ctk.CTkSlider(\n parent, from_=from_, to=to, variable=var,\n width=180, command=on_change\n )\n slider.pack(padx=8)\n return var\n\n self._target_var = make_slider(sidebar, \"Target Util %\", 20, 90, 60)\n self._alpha_var = make_slider(sidebar, \"EMA Alpha\", 0.1, 1.0, 0.7)\n self._safety_var = make_slider(sidebar, \"Safety Margin\", 0.0, 0.5, 0.15)\n self._up_thresh_var = make_slider(sidebar, \"Up Threshold ×\", 1.0, 1.5, 1.1)\n self._down_thresh_var = make_slider(sidebar, \"Down Threshold ×\", 0.3, 0.9, 0.7)\n\n ctk.CTkButton(\n sidebar, text=\"Apply & Re-run\", fg_color=C[\"accent\"],\n text_color=\"#fff\", hover_color=\"#c73550\",\n command=lambda: (self._reset_simulation(), self._update_all())\n ).pack(pady=16, padx=12, fill=\"x\")\n\n # Scenario selector\n ctk.CTkLabel(\n sidebar, text=\"Scenario\", font=(\"Consolas\", 14, \"bold\"),\n text_color=C[\"text\"]\n ).pack(pady=(16, 4))\n\n self._scenario_btns: list[ctk.CTkButton] = []\n names = [\"Stable\", \"Ramp\", \"Spike\", \"Diurnal\"]\n for i, name in enumerate(names):\n btn = ctk.CTkButton(\n sidebar, text=name, width=180, height=28,\n fg_color=C[\"bg_card\"], hover_color=C[\"blue\"],\n command=lambda idx=i: self._select_scenario(idx)\n )\n btn.pack(pady=2, padx=12)\n self._scenario_btns.append(btn)\n\n # -------------------------------------------------------------------\n # Tab view (3 tabs)\n # -------------------------------------------------------------------\n\n def _build_tabview(self):\n self._tabview = ctk.CTkTabview(self, fg_color=C[\"bg\"])\n self._tabview.pack(fill=\"both\", expand=True, padx=8, pady=(0, 8))\n\n self._tab_nn = self._tabview.add(\"Neural Network\")\n self._tab_algo = self._tabview.add(\"Scaling Algorithm\")\n self._tab_exp = self._tabview.add(\"Experiment\")\n\n self._build_nn_tab()\n self._build_algo_tab()\n self._build_exp_tab()\n\n # --- Tab 1: Neural Network ---\n\n def _build_nn_tab(self):\n tab = self._tab_nn\n\n # Top: pipeline diagram\n pipe_frame = ctk.CTkFrame(tab, fg_color=C[\"bg_card\"], corner_radius=8, height=90)\n pipe_frame.pack(fill=\"x\", padx=8, pady=4)\n pipe_frame.pack_propagate(False)\n\n ctk.CTkLabel(\n pipe_frame, text=\"ONNX Inference Pipeline\",\n font=(\"Consolas\", 12, \"bold\"), text_color=C[\"text\"]\n ).pack(pady=(6, 4))\n\n blocks_frame = ctk.CTkFrame(pipe_frame, fg_color=\"transparent\")\n blocks_frame.pack()\n stages = [\n (\"Raw Metrics\", \"5 values\", C[\"green\"]),\n (\"→\", \"\", C[\"text_dim\"]),\n (\"Feature Eng.\", \"44 features\", C[\"purple\"]),\n (\"→\", \"\", C[\"text_dim\"]),\n (\"StandardScaler\", \"normalize\", C[\"orange\"]),\n (\"→\", \"\", C[\"text_dim\"]),\n (\"ONNX LSTM\", \"(1,24,44)→(1,1)\", C[\"blue\"]),\n (\"→\", \"\", C[\"text_dim\"]),\n (\"Inverse Scale\", \"→ CPU %\", C[\"green\"]),\n ]\n for title, sub, color in stages:\n if title == \"→\":\n ctk.CTkLabel(\n blocks_frame, text=\"→\", font=(\"Consolas\", 16, \"bold\"),\n text_color=C[\"text_dim\"]\n ).pack(side=\"left\", padx=2)\n else:\n f = ctk.CTkFrame(blocks_frame, fg_color=C[\"bg\"], corner_radius=6,\n border_width=1, border_color=color)\n f.pack(side=\"left\", padx=4)\n ctk.CTkLabel(f, text=title, font=(\"Consolas\", 9, \"bold\"),\n text_color=color).pack(padx=6, pady=(2, 0))\n ctk.CTkLabel(f, text=sub, font=(\"Consolas\", 8),\n text_color=C[\"text_dim\"]).pack(padx=6, pady=(0, 2))\n\n # Middle: prediction output + buffer\n mid = ctk.CTkFrame(tab, fg_color=\"transparent\")\n mid.pack(fill=\"x\", padx=8, pady=4)\n\n # Buffer status\n buf_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8, width=250)\n buf_frame.pack(side=\"left\", padx=(0, 8), fill=\"y\")\n buf_frame.pack_propagate(False)\n ctk.CTkLabel(buf_frame, text=\"Buffer Status\", font=(\"Consolas\", 11, \"bold\"),\n text_color=C[\"text\"]).pack(pady=(8, 4))\n self._buf_progress = ctk.CTkProgressBar(buf_frame, width=200)\n self._buf_progress.pack(pady=4, padx=12)\n self._buf_progress.set(0)\n self._buf_lbl = ctk.CTkLabel(\n buf_frame, text=\"0 / 24 NOT READY\", font=(\"Consolas\", 10),\n text_color=C[\"red\"]\n )\n self._buf_lbl.pack()\n\n # Prediction big number\n pred_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8, width=200)\n pred_frame.pack(side=\"left\", padx=8, fill=\"y\")\n pred_frame.pack_propagate(False)\n ctk.CTkLabel(pred_frame, text=\"ML Prediction\", font=(\"Consolas\", 11, \"bold\"),\n text_color=C[\"text\"]).pack(pady=(8, 2))\n self._pred_big = ctk.CTkLabel(\n pred_frame, text=\"—\", font=(\"Consolas\", 28, \"bold\"),\n text_color=C[\"blue\"]\n )\n self._pred_big.pack(pady=4)\n self._pred_actual = ctk.CTkLabel(\n pred_frame, text=\"Actual: —\", font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"]\n )\n self._pred_actual.pack()\n\n # Raw metrics panel\n raw_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8)\n raw_frame.pack(side=\"left\", fill=\"both\", expand=True, padx=8)\n ctk.CTkLabel(raw_frame, text=\"Current Metrics\", font=(\"Consolas\", 11, \"bold\"),\n text_color=C[\"text\"]).pack(pady=(8, 4))\n self._raw_labels: dict[str, ctk.CTkLabel] = {}\n for metric in [\"cpu_usage\", \"mem_util\", \"net_in\", \"net_out\", \"disk_io\"]:\n lbl = ctk.CTkLabel(raw_frame, text=f\"{metric}: —\",\n font=(\"Consolas\", 10), text_color=C[\"text_dim\"])\n lbl.pack(anchor=\"w\", padx=12)\n self._raw_labels[metric] = lbl\n\n # Bottom: prediction history chart\n self._nn_fig, self._nn_ax = plt.subplots(1, 1, figsize=(10, 3), dpi=80)\n self._nn_canvas = embed_figure(tab, self._nn_fig)\n\n # --- Tab 2: Scaling Algorithm ---\n\n def _build_algo_tab(self):\n tab = self._tab_algo\n\n # 9-step pipeline\n pipe = ctk.CTkFrame(tab, fg_color=\"transparent\")\n pipe.pack(fill=\"x\", padx=4, pady=4)\n\n step_names = [\n \"EMA\", \"Hysteresis\", \"Desired\", \"Clamp\",\n \"Rate Limit\", \"Cooldown\", \"Stabilize\", \"No Change?\", \"Commit\"\n ]\n self._algo_blocks: list[PipelineBlock] = []\n for i, name in enumerate(step_names):\n if i > 0:\n ctk.CTkLabel(pipe, text=\"→\", font=(\"Consolas\", 14, \"bold\"),\n text_color=C[\"text_dim\"]).pack(side=\"left\", padx=1)\n block = PipelineBlock(pipe, i + 1, name)\n block.pack(side=\"left\", padx=2)\n self._algo_blocks.append(block)\n\n # State panel\n state_frame = ctk.CTkFrame(tab, fg_color=C[\"bg_card\"], corner_radius=8, height=40)\n state_frame.pack(fill=\"x\", padx=8, pady=4)\n self._state_lbl = ctk.CTkLabel(\n state_frame,\n text=\"Cooldown UP: — | Cooldown DOWN: — | Consec. Below: — | Direction: —\",\n font=(\"Consolas\", 10), text_color=C[\"text_dim\"]\n )\n self._state_lbl.pack(pady=6)\n\n # Charts: CPU + Replicas side by side\n charts = ctk.CTkFrame(tab, fg_color=\"transparent\")\n charts.pack(fill=\"both\", expand=True, padx=4, pady=4)\n\n left = ctk.CTkFrame(charts, fg_color=\"transparent\")\n left.pack(side=\"left\", fill=\"both\", expand=True)\n self._algo_cpu_fig, self._algo_cpu_ax = plt.subplots(1, 1, figsize=(5.5, 3), dpi=80)\n self._algo_cpu_canvas = embed_figure(left, self._algo_cpu_fig)\n\n right = ctk.CTkFrame(charts, fg_color=\"transparent\")\n right.pack(side=\"left\", fill=\"both\", expand=True)\n self._algo_rep_fig, self._algo_rep_ax = plt.subplots(1, 1, figsize=(5.5, 3), dpi=80)\n self._algo_rep_canvas = embed_figure(right, self._algo_rep_fig)\n\n # Decision log\n self._decision_log = ctk.CTkTextbox(\n tab, height=100, font=(\"Consolas\", 9), fg_color=C[\"bg\"],\n text_color=C[\"text_dim\"]\n )\n self._decision_log.pack(fill=\"x\", padx=8, pady=(0, 4))\n\n # --- Tab 3: Experiment Comparison ---\n\n def _build_exp_tab(self):\n tab = self._tab_exp\n\n # Main comparison chart\n self._exp_fig, self._exp_axes = plt.subplots(2, 1, figsize=(10, 5), dpi=80,\n gridspec_kw={\"height_ratios\": [2, 1]})\n self._exp_canvas = embed_figure(tab, self._exp_fig)\n\n # KPI cards grid\n cards_frame = ctk.CTkFrame(tab, fg_color=\"transparent\")\n cards_frame.pack(fill=\"x\", padx=8, pady=4)\n\n kpi_names = [\n \"Avg Replicas\", \"Replica-Min\", \"SLA Violations\", \"Scaling Events\",\n \"Over-Prov %\", \"Under-Prov %\", \"Reaction Time\", \"Cost Savings %\"\n ]\n self._kpi_cards: list[KPICard] = []\n for i, name in enumerate(kpi_names):\n card = KPICard(cards_frame, name)\n card.grid(row=i // 4, column=i % 4, padx=4, pady=4, sticky=\"nsew\")\n self._kpi_cards.append(card)\n for c in range(4):\n cards_frame.columnconfigure(c, weight=1)\n\n # -------------------------------------------------------------------\n # Update all visuals\n # -------------------------------------------------------------------\n\n def _update_all(self):\n sc = self._scenarios[self._current_scenario_idx]\n total = len(sc.actual_cpu)\n self._step_lbl.configure(text=f\"Step: {self._step} / {total}\")\n\n # Highlight active scenario button\n for i, btn in enumerate(self._scenario_btns):\n btn.configure(\n fg_color=C[\"blue\"] if i == self._current_scenario_idx else C[\"bg_card\"]\n )\n\n self._update_nn_tab()\n self._update_algo_tab()\n self._update_exp_tab()\n\n def _update_nn_tab(self):\n n = self._step\n\n # Buffer status\n buf_fill = min(n, 24)\n self._buf_progress.set(buf_fill / 24)\n ready = n >= 24\n self._buf_lbl.configure(\n text=f\"{min(n, 54)} / 24 {'READY' if ready else 'FILLING...'}\",\n text_color=C[\"green\"] if ready else C[\"red\"]\n )\n\n # Current prediction\n if n > 0:\n pred = self._predicted_trace[-1]\n actual = self._actual_trace[-1]\n color = C[\"green\"] if pred < 42 else C[\"yellow\"] if pred < 66 else C[\"orange\"] if pred < 85 else C[\"red\"]\n self._pred_big.configure(text=f\"{pred:.1f}%\", text_color=color)\n self._pred_actual.configure(text=f\"Actual: {actual:.1f}%\")\n\n # Raw metrics (simulated from CPU)\n self._raw_labels[\"cpu_usage\"].configure(text=f\"cpu_usage: {actual:.1f}%\")\n self._raw_labels[\"mem_util\"].configure(text=f\"mem_util: {actual * 0.6:.1f}%\")\n self._raw_labels[\"net_in\"].configure(text=f\"net_in: {actual * 1.2:.0f} KB/s\")\n self._raw_labels[\"net_out\"].configure(text=f\"net_out: {actual * 0.8:.0f} KB/s\")\n self._raw_labels[\"disk_io\"].configure(text=f\"disk_io: {actual * 0.3:.1f}%\")\n\n # Prediction history chart\n self._nn_ax.clear()\n if n > 1:\n x = range(max(0, n - 80), n)\n a_slice = self._actual_trace[max(0, n - 80):]\n p_slice = self._predicted_trace[max(0, n - 80):]\n self._nn_ax.plot(x, a_slice, color=C[\"green\"], lw=1.5, label=\"Actual CPU\")\n self._nn_ax.plot(x, p_slice, color=C[\"purple\"], lw=1.5, ls=\"--\", label=\"Predicted\")\n self._nn_ax.axhline(60, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.5)\n self._nn_ax.set_ylabel(\"CPU %\")\n self._nn_ax.set_ylim(0, 100)\n self._nn_ax.legend(loc=\"upper right\", fontsize=8)\n self._nn_ax.grid(True, alpha=0.2)\n self._nn_ax.set_title(\"Prediction History (last 80 steps)\", fontsize=10)\n self._nn_fig.tight_layout()\n self._nn_canvas.draw_idle()\n\n def _update_algo_tab(self):\n n = self._step\n if n == 0:\n for block in self._algo_blocks:\n block.update(\"—\", \"skipped\")\n return\n\n # Get current step details\n pred = self._predicted_trace[-1]\n smoothed = self._smoothed_trace[-1]\n ml_replicas = self._ml_replicas_trace[-1]\n prev_replicas = self._ml_replicas_trace[-2] if n > 1 else 2\n\n target = self._target_var.get()\n up_thresh = target * self._up_thresh_var.get()\n down_thresh = target * self._down_thresh_var.get()\n\n # Determine direction\n if smoothed > up_thresh:\n direction = \"UP\"\n elif smoothed < down_thresh:\n direction = \"DOWN\"\n else:\n direction = \"HOLD\"\n\n # Update blocks\n self._algo_blocks[0].update(f\"{pred:.1f}→{smoothed:.1f}%\", \"active\")\n self._algo_blocks[1].update(\n f\"{smoothed:.0f}% {'>' if direction == 'UP' else '<' if direction == 'DOWN' else 'in'} \"\n f\"[{down_thresh:.0f},{up_thresh:.0f}]\\n→{direction}\",\n \"active\" if direction != \"HOLD\" else \"blocked\"\n )\n\n if direction == \"HOLD\":\n for i in range(2, 9):\n self._algo_blocks[i].update(\"—\", \"skipped\")\n else:\n raw_desired = math.ceil(prev_replicas * smoothed / target)\n if direction == \"UP\":\n raw_desired = math.ceil(raw_desired * (1 + self._safety_var.get()))\n self._algo_blocks[2].update(f\"{prev_replicas}×{smoothed:.0f}/{target:.0f}\\n={raw_desired}\", \"active\")\n clamped = max(2, min(8, raw_desired))\n self._algo_blocks[3].update(f\"[2,8]→{clamped}\", \"active\")\n rate_lim = min(clamped, prev_replicas + 2) if direction == \"UP\" else max(clamped, prev_replicas - 1)\n self._algo_blocks[4].update(f\"±limit→{rate_lim}\", \"active\")\n self._algo_blocks[5].update(\"OFF ✓\", \"active\")\n self._algo_blocks[6].update(\"N/A\" if direction == \"UP\" else \"check\", \"active\")\n\n executed = ml_replicas != prev_replicas\n self._algo_blocks[7].update(\n f\"{ml_replicas}{'≠' if executed else '='}{prev_replicas}\",\n \"active\" if executed else \"blocked\"\n )\n if executed:\n self._algo_blocks[8].update(f\"{prev_replicas}→{ml_replicas}\", \"executed\")\n else:\n self._algo_blocks[8].update(\"hold\", \"blocked\")\n\n # State panel\n sim = self._ml_sim\n self._state_lbl.configure(\n text=f\"Direction: {direction} | \"\n f\"Consec. Below: {sim._consecutive_below} | \"\n f\"Replicas: {ml_replicas} | \"\n f\"Smoothed: {smoothed:.1f}%\"\n )\n\n # CPU chart\n self._algo_cpu_ax.clear()\n if n > 1:\n x = range(n)\n self._algo_cpu_ax.plot(x, self._smoothed_trace, color=C[\"blue\"], lw=1.5, label=\"Smoothed\")\n self._algo_cpu_ax.plot(x, self._actual_trace, color=C[\"green\"], lw=1, alpha=0.5, label=\"Actual\")\n self._algo_cpu_ax.axhline(up_thresh, color=C[\"red\"], ls=\"--\", lw=0.8, label=f\"Up {up_thresh:.0f}%\")\n self._algo_cpu_ax.axhline(down_thresh, color=C[\"orange\"], ls=\"--\", lw=0.8, label=f\"Down {down_thresh:.0f}%\")\n self._algo_cpu_ax.axhline(target, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.6)\n self._algo_cpu_ax.axhspan(down_thresh, up_thresh, alpha=0.08, color=C[\"yellow\"])\n self._algo_cpu_ax.set_ylim(0, 100)\n self._algo_cpu_ax.set_ylabel(\"CPU %\")\n self._algo_cpu_ax.legend(loc=\"upper right\", fontsize=7)\n self._algo_cpu_ax.grid(True, alpha=0.2)\n self._algo_cpu_ax.set_title(\"CPU + Thresholds\", fontsize=10)\n self._algo_cpu_fig.tight_layout()\n self._algo_cpu_canvas.draw_idle()\n\n # Replica chart\n self._algo_rep_ax.clear()\n if n > 1:\n x = range(n)\n self._algo_rep_ax.step(x, self._ml_replicas_trace, color=C[\"blue\"], lw=2, where=\"post\", label=\"ML Replicas\")\n self._algo_rep_ax.step(x, self._hpa_replicas_trace, color=C[\"red\"], lw=1.5, where=\"post\", alpha=0.6, label=\"HPA Replicas\")\n self._algo_rep_ax.axhline(2, color=C[\"text_dim\"], ls=\":\", lw=0.7, alpha=0.5)\n self._algo_rep_ax.axhline(8, color=C[\"text_dim\"], ls=\":\", lw=0.7, alpha=0.5)\n self._algo_rep_ax.set_ylim(0, 10)\n self._algo_rep_ax.set_ylabel(\"Replicas\")\n self._algo_rep_ax.legend(loc=\"upper right\", fontsize=7)\n self._algo_rep_ax.grid(True, alpha=0.2)\n self._algo_rep_ax.set_title(\"Replica Count\", fontsize=10)\n self._algo_rep_fig.tight_layout()\n self._algo_rep_canvas.draw_idle()\n\n # Decision log\n self._decision_log.configure(state=\"normal\")\n self._decision_log.delete(\"1.0\", \"end\")\n # Show last 20 entries\n for line in self._decisions_log[-20:]:\n self._decision_log.insert(\"end\", line + \"\\n\")\n self._decision_log.configure(state=\"disabled\")\n self._decision_log.see(\"end\")\n\n def _update_exp_tab(self):\n if not hasattr(self, \"_full_metrics\"):\n return\n metrics = self._full_metrics\n history = self._history\n\n # Top chart: Actual CPU + replicas\n ax1, ax2 = self._exp_axes\n ax1.clear()\n ax2.clear()\n\n n = len(history)\n if n > 0:\n x = range(n)\n actual = [r.actual_cpu for r in history]\n hpa_r = [r.hpa_replicas for r in history]\n ml_r = [r.ml_replicas for r in history]\n\n # CPU trace (background)\n ax1.fill_between(x, actual, alpha=0.15, color=C[\"text_dim\"])\n ax1.plot(x, actual, color=C[\"text_dim\"], lw=0.8, alpha=0.5, label=\"Actual CPU\")\n\n # Replicas\n ax1.step(x, [r * 10 for r in hpa_r], color=C[\"red\"], lw=1.5,\n where=\"post\", label=\"HPA Replicas (×10)\")\n ax1.step(x, [r * 10 for r in ml_r], color=C[\"blue\"], lw=2,\n where=\"post\", label=\"ML Replicas (×10)\")\n\n # SLA threshold\n target = self._target_var.get()\n ax1.axhline(target * 1.2, color=C[\"red\"], ls=\":\", lw=0.8, alpha=0.5, label=\"SLA Limit\")\n\n ax1.set_ylabel(\"CPU % / Replicas×10\")\n ax1.set_ylim(0, 100)\n ax1.legend(loc=\"upper right\", fontsize=7, ncol=2)\n ax1.set_title(f\"Experiment: {self._scenarios[self._current_scenario_idx].name}\", fontsize=11)\n ax1.grid(True, alpha=0.2)\n\n # Bottom chart: replica diff\n diff = [m - h for m, h in zip(ml_r, hpa_r)]\n colors_bar = [C[\"green\"] if d < 0 else C[\"red\"] if d > 0 else C[\"text_dim\"] for d in diff]\n ax2.bar(x, diff, color=colors_bar, alpha=0.7, width=1.0)\n ax2.axhline(0, color=C[\"text_dim\"], lw=0.5)\n ax2.set_ylabel(\"ML - HPA\")\n ax2.set_xlabel(\"Step\")\n ax2.grid(True, alpha=0.2)\n ax2.set_title(\"Replica Difference (negative = ML uses fewer)\", fontsize=9)\n\n self._exp_fig.tight_layout()\n self._exp_canvas.draw_idle()\n\n # KPI cards\n self._kpi_cards[0].set_values(f\"{metrics.hpa_avg_replicas:.1f}\", f\"{metrics.ml_avg_replicas:.1f}\")\n self._kpi_cards[1].set_values(f\"{metrics.hpa_replica_minutes:.0f}\", f\"{metrics.ml_replica_minutes:.0f}\")\n self._kpi_cards[2].set_values(f\"{metrics.hpa_sla_violations}\", f\"{metrics.ml_sla_violations}\")\n self._kpi_cards[3].set_values(f\"{metrics.hpa_scaling_events}\", f\"{metrics.ml_scaling_events}\")\n self._kpi_cards[4].set_values(f\"{metrics.hpa_over_provisioning_pct:.1f}%\", f\"{metrics.ml_over_provisioning_pct:.1f}%\")\n self._kpi_cards[5].set_values(f\"{metrics.hpa_under_provisioning_pct:.1f}%\", f\"{metrics.ml_under_provisioning_pct:.1f}%\")\n self._kpi_cards[6].set_values(f\"{metrics.hpa_avg_reaction_time:.1f}\", f\"{metrics.ml_avg_reaction_time:.1f}\")\n self._kpi_cards[7].set_values(f\"—\", f\"{metrics.cost_savings_pct:+.1f}%\")\n\n\n# ---------------------------------------------------------------------------\n# Entry point\n# ---------------------------------------------------------------------------\n\n\ndef main():\n app = ExperimentApp()\n app.mainloop()\n\n\nif __name__ == \"__main__\":\n main()\n", | |
| "numLines": 867, | |
| "startLine": 1, | |
| "totalLines": 867 | |
| } | |
| } | |
| } | |
| [2026-02-07T18:13:59.418Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-6", | |
| "id": "msg_011UihX1T3Y6mujzA9np5g91", | |
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| "name": "Read", | |
| "input": { | |
| "file_path": "/tmp/gh-issue-solver-1770487990892/scripts/experiment_simulator.py" | |
| } | |
| } | |
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| "stop_reason": null, | |
| "stop_sequence": null, | |
| "usage": { | |
| "input_tokens": 1, | |
| "cache_creation_input_tokens": 1212, | |
| "cache_read_input_tokens": 40167, | |
| "cache_creation": { | |
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| "ephemeral_1h_input_tokens": 1212 | |
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| "output_tokens": 3, | |
| "service_tier": "standard", | |
| "inference_geo": "not_available" | |
| }, | |
| "context_management": null | |
| }, | |
| "parent_tool_use_id": null, | |
| "session_id": "ce1ab353-0721-47e7-a373-84e062534c7f", | |
| "uuid": "2a6ade7f-5bef-4c54-9972-163ceed56119" | |
| } | |
| [2026-02-07T18:13:59.420Z] [INFO] { | |
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| "tool_use_id": "toolu_01B9eLxiCRDrQsogCBBMHaBL", | |
| "type": "tool_result", | |
| "content": " 1→\"\"\"Deterministic experiment simulator: ML Autoscaler vs Reactive HPA.\n 2→\n 3→Replays historical CPU traces through both scaling strategies\n 4→and computes comparison metrics without requiring a live cluster.\n 5→\n 6→Usage:\n 7→ from scripts.experiment_simulator import ExperimentRunner, HPASimulator, MLAutoscalerSimulator\n 8→\n 9→ runner = ExperimentRunner(actual_cpu=cpu_trace, predicted_cpu=pred_trace)\n 10→ results = runner.run()\n 11→\"\"\"\n 12→\n 13→from __future__ import annotations\n 14→\n 15→import math\n 16→from dataclasses import dataclass, field\n 17→from typing import Literal\n 18→\n 19→import numpy as np\n 20→\n 21→\n 22→# ---------------------------------------------------------------------------\n 23→# Data containers\n 24→# ---------------------------------------------------------------------------\n 25→\n 26→\n 27→@dataclass\n 28→class StepRecord:\n 29→ \"\"\"Single timestep record for both simulators.\"\"\"\n 30→\n 31→ t: int\n 32→ actual_cpu: float\n 33→ predicted_cpu: float\n 34→ hpa_replicas: int\n 35→ ml_replicas: int\n 36→ hpa_decision: str # \"up\", \"down\", \"hold\"\n 37→ ml_decision: str\n 38→\n 39→\n 40→@dataclass\n 41→class ExperimentMetrics:\n 42→ \"\"\"Aggregated comparison metrics from a simulation run.\"\"\"\n 43→\n 44→ # Resource usage\n 45→ hpa_avg_replicas: float\n 46→ ml_avg_replicas: float\n 47→ hpa_replica_minutes: float\n 48→ ml_replica_minutes: float\n 49→\n 50→ # SLA\n 51→ hpa_sla_violations: int\n 52→ ml_sla_violations: int\n 53→ hpa_sla_violation_rate: float\n 54→ ml_sla_violation_rate: float\n 55→\n 56→ # Provisioning quality\n 57→ hpa_over_provisioning_pct: float\n 58→ ml_over_provisioning_pct: float\n 59→ hpa_under_provisioning_pct: float\n 60→ ml_under_provisioning_pct: float\n 61→\n 62→ # Stability\n 63→ hpa_scaling_events: int\n 64→ ml_scaling_events: int\n 65→\n 66→ # Reaction time (steps to respond to demand spike)\n 67→ hpa_avg_reaction_time: float\n 68→ ml_avg_reaction_time: float\n 69→\n 70→ # Cost savings\n 71→ cost_savings_pct: float\n 72→\n 73→ # Total steps\n 74→ total_steps: int\n 75→\n 76→\n 77→# ---------------------------------------------------------------------------\n 78→# HPA Simulator (Kubernetes HPA v2 behaviour)\n 79→# ---------------------------------------------------------------------------\n 80→\n 81→\n 82→@dataclass\n 83→class HPAConfig:\n 84→ \"\"\"Parameters matching Kubernetes HPA v2 behaviour.\"\"\"\n 85→\n 86→ target_utilization: float = 60.0\n 87→ min_replicas: int = 2\n 88→ max_replicas: int = 8\n 89→ # HPA allows up to doubling, but we cap at +4 for realism\n 90→ max_scale_up_step: int = 4\n 91→ max_scale_down_step: int = 1\n 92→ # HPA defaults (converted to 5-min steps)\n 93→ # scale-up stabilization: 0 (immediate in k8s), but 3 min cooldown ≈ 0 steps at 5-min\n 94→ scale_up_cooldown_steps: int = 1\n 95→ # scale-down stabilization: 5 min default → 1 step in 5-min window\n 96→ scale_down_stabilization_count: int = 1\n 97→ # Tolerance: HPA has 10% tolerance band\n 98→ tolerance: float = 0.1\n 99→ step_duration_minutes: float = 5.0\n 100→\n 101→\n 102→class HPASimulator:\n 103→ \"\"\"Simulates Kubernetes HPA v2 reactive autoscaling.\n 104→\n 105→ The HPA formula:\n 106→ desiredReplicas = ceil(currentReplicas * (currentMetricValue / desiredMetricValue))\n 107→\n 108→ With tolerance band: no action if ratio is within [1-tolerance, 1+tolerance].\n 109→ \"\"\"\n 110→\n 111→ def __init__(self, config: HPAConfig | None = None) -> None:\n 112→ self.config = config or HPAConfig()\n 113→ self.replicas = self.config.min_replicas\n 114→ self._last_scale_up_step: int | None = None\n 115→ self._last_scale_down_step: int | None = None\n 116→ self._consecutive_below: int = 0\n 117→\n 118→ def step(self, actual_cpu: float, t: int) -> tuple[int, str]:\n 119→ \"\"\"Process one timestep.\n 120→\n 121→ Args:\n 122→ actual_cpu: Current actual CPU utilization (%).\n 123→ t: Step index.\n 124→\n 125→ Returns:\n 126→ (new_replicas, decision_direction)\n 127→ \"\"\"\n 128→ cfg = self.config\n 129→ ratio = actual_cpu / cfg.target_utilization if cfg.target_utilization > 0 else 1.0\n 130→\n 131→ # Tolerance band: HPA skips if within ±10%\n 132→ if abs(ratio - 1.0) <= cfg.tolerance:\n 133→ self._consecutive_below = 0\n 134→ return self.replicas, \"hold\"\n 135→\n 136→ if ratio > 1.0 + cfg.tolerance:\n 137→ # Scale up\n 138→ direction = \"up\"\n 139→ self._consecutive_below = 0\n 140→\n 141→ # Cooldown check\n 142→ if (\n 143→ self._last_scale_up_step is not None\n 144→ and (t - self._last_scale_up_step) < cfg.scale_up_cooldown_steps\n 145→ ):\n 146→ return self.replicas, \"hold\"\n 147→\n 148→ desired = math.ceil(self.replicas * ratio)\n 149→ desired = min(desired, self.replicas + cfg.max_scale_up_step)\n 150→ desired = max(cfg.min_replicas, min(cfg.max_replicas, desired))\n 151→\n 152→ if desired > self.replicas:\n 153→ self._last_scale_up_step = t\n 154→ self.replicas = desired\n 155→ return self.replicas, direction\n 156→\n 157→ return self.replicas, \"hold\"\n 158→\n 159→ else:\n 160→ # Scale down (ratio < 1.0 - tolerance)\n 161→ direction = \"down\"\n 162→ self._consecutive_below += 1\n 163→\n 164→ # Scale-down stabilization\n 165→ if self._consecutive_below < cfg.scale_down_stabilization_count:\n 166→ return self.replicas, \"hold\"\n 167→\n 168→ # Cooldown check\n 169→ if (\n 170→ self._last_scale_down_step is not None\n 171→ and (t - self._last_scale_down_step) < cfg.scale_down_stabilization_count\n 172→ ):\n 173→ return self.replicas, \"hold\"\n 174→\n 175→ desired = math.ceil(self.replicas * ratio)\n 176→ desired = max(desired, self.replicas - cfg.max_scale_down_step)\n 177→ desired = max(cfg.min_replicas, min(cfg.max_replicas, desired))\n 178→\n 179→ if desired < self.replicas:\n 180→ self._last_scale_down_step = t\n 181→ self._consecutive_below = 0\n 182→ self.replicas = desired\n 183→ return self.replicas, direction\n 184→\n 185→ return self.replicas, \"hold\"\n 186→\n 187→\n 188→# ---------------------------------------------------------------------------\n 189→# ML Autoscaler Simulator (wraps our real algorithm)\n 190→# ---------------------------------------------------------------------------\n 191→\n 192→\n 193→@dataclass\n 194→class MLConfig:\n 195→ \"\"\"Parameters for the ML autoscaler (mirrors ScalingConfig).\"\"\"\n 196→\n 197→ target_utilization: float = 60.0\n 198→ min_replicas: int = 2\n 199→ max_replicas: int = 8\n 200→ safety_margin: float = 0.15\n 201→ smoothing_alpha: float = 0.7\n 202→ scale_up_threshold_factor: float = 1.1\n 203→ scale_down_threshold_factor: float = 0.7\n 204→ max_scale_up_step: int = 2\n 205→ max_scale_down_step: int = 1\n 206→ scale_up_cooldown_seconds: float = 60.0\n 207→ scale_down_cooldown_seconds: float = 300.0\n 208→ scale_down_stabilization_count: int = 3\n 209→ step_duration_minutes: float = 5.0\n 210→\n 211→\n 212→class MLAutoscalerSimulator:\n 213→ \"\"\"Simulates the ML-based predictive autoscaler.\n 214→\n 215→ Uses the real `compute_scaling_decision()` from planner/algorithm.py\n 216→ internally but through a clean simulation interface.\n 217→ \"\"\"\n 218→\n 219→ def __init__(self, config: MLConfig | None = None) -> None:\n 220→ self.config = config or MLConfig()\n 221→ self.replicas = self.config.min_replicas\n 222→\n 223→ # Internal state replicating ScalingState\n 224→ self._smoothed: float | None = None\n 225→ self._last_scale_up_time: float | None = None\n 226→ self._last_scale_down_time: float | None = None\n 227→ self._consecutive_below: int = 0\n 228→\n 229→ def step(self, predicted_cpu: float, t: int) -> tuple[int, str]:\n 230→ \"\"\"Process one timestep with ML prediction.\n 231→\n 232→ Args:\n 233→ predicted_cpu: ML-predicted CPU utilization (%) for the future.\n 234→ t: Step index.\n 235→\n 236→ Returns:\n 237→ (new_replicas, decision_direction)\n 238→ \"\"\"\n 239→ cfg = self.config\n 240→ now = t * cfg.step_duration_minutes * 60 # Convert to seconds\n 241→\n 242→ # Step 1: EMA smoothing\n 243→ if self._smoothed is None:\n 244→ self._smoothed = predicted_cpu\n 245→ else:\n 246→ self._smoothed = (\n 247→ cfg.smoothing_alpha * predicted_cpu\n 248→ + (1 - cfg.smoothing_alpha) * self._smoothed\n 249→ )\n 250→\n 251→ smoothed = self._smoothed\n 252→\n 253→ # Step 2: Hysteresis\n 254→ threshold_up = cfg.target_utilization * cfg.scale_up_threshold_factor\n 255→ threshold_down = cfg.target_utilization * cfg.scale_down_threshold_factor\n 256→\n 257→ if smoothed > threshold_up:\n 258→ direction: Literal[\"up\", \"down\", \"hold\"] = \"up\"\n 259→ self._consecutive_below = 0\n 260→ elif smoothed < threshold_down:\n 261→ direction = \"down\"\n 262→ self._consecutive_below += 1\n 263→ else:\n 264→ # Dead-zone: hold\n 265→ self._consecutive_below = 0\n 266→ return self.replicas, \"hold\"\n 267→\n 268→ # Step 3: Compute desired replicas\n 269→ if cfg.target_utilization > 0:\n 270→ raw = self.replicas * smoothed / cfg.target_utilization\n 271→ desired = math.ceil(raw)\n 272→ if direction == \"up\":\n 273→ desired = math.ceil(desired * (1 + cfg.safety_margin))\n 274→ else:\n 275→ desired = self.replicas\n 276→\n 277→ # Step 4: Clamp\n 278→ desired = max(cfg.min_replicas, min(cfg.max_replicas, desired))\n 279→\n 280→ # Step 5: Rate limits\n 281→ if desired > self.replicas:\n 282→ desired = min(desired, self.replicas + cfg.max_scale_up_step)\n 283→ elif desired < self.replicas:\n 284→ desired = max(desired, self.replicas - cfg.max_scale_down_step)\n 285→\n 286→ # Step 6: Cooldowns\n 287→ if direction == \"up\":\n 288→ if (\n 289→ self._last_scale_up_time is not None\n 290→ and (now - self._last_scale_up_time) < cfg.scale_up_cooldown_seconds\n 291→ ):\n 292→ return self.replicas, \"hold\"\n 293→\n 294→ if direction == \"down\":\n 295→ if (\n 296→ self._last_scale_down_time is not None\n 297→ and (now - self._last_scale_down_time) < cfg.scale_down_cooldown_seconds\n 298→ ):\n 299→ return self.replicas, \"hold\"\n 300→\n 301→ # Step 7: Scale-down stabilization\n 302→ if (\n 303→ direction == \"down\"\n 304→ and self._consecutive_below < cfg.scale_down_stabilization_count\n 305→ ):\n 306→ return self.replicas, \"hold\"\n 307→\n 308→ # Step 8: No change check\n 309→ if desired == self.replicas:\n 310→ return self.replicas, \"hold\"\n 311→\n 312→ # Step 9: Commit\n 313→ if direction == \"up\":\n 314→ self._last_scale_up_time = now\n 315→ else:\n 316→ self._last_scale_down_time = now\n 317→ self._consecutive_below = 0\n 318→\n 319→ self.replicas = desired\n 320→ return self.replicas, direction\n 321→\n 322→\n 323→# ---------------------------------------------------------------------------\n 324→# Metrics computation\n 325→# ---------------------------------------------------------------------------\n 326→\n 327→\n 328→def compute_metrics(\n 329→ history: list[StepRecord],\n 330→ step_duration_minutes: float = 5.0,\n 331→ sla_threshold_factor: float = 1.2,\n 332→ target_utilization: float = 60.0,\n 333→) -> ExperimentMetrics:\n 334→ \"\"\"Compute comparison metrics from simulation history.\n 335→\n 336→ Args:\n 337→ history: List of StepRecord from a simulation run.\n 338→ step_duration_minutes: Duration of each step in minutes.\n 339→ sla_threshold_factor: SLA is violated when CPU > target * factor.\n 340→ target_utilization: Target CPU % for capacity calculation.\n 341→ \"\"\"\n 342→ n = len(history)\n 343→ if n == 0:\n 344→ return ExperimentMetrics(\n 345→ hpa_avg_replicas=0, ml_avg_replicas=0,\n 346→ hpa_replica_minutes=0, ml_replica_minutes=0,\n 347→ hpa_sla_violations=0, ml_sla_violations=0,\n 348→ hpa_sla_violation_rate=0, ml_sla_violation_rate=0,\n 349→ hpa_over_provisioning_pct=0, ml_over_provisioning_pct=0,\n 350→ hpa_under_provisioning_pct=0, ml_under_provisioning_pct=0,\n 351→ hpa_scaling_events=0, ml_scaling_events=0,\n 352→ hpa_avg_reaction_time=0, ml_avg_reaction_time=0,\n 353→ cost_savings_pct=0, total_steps=0,\n 354→ )\n 355→\n 356→ hpa_replicas = np.array([r.hpa_replicas for r in history], dtype=float)\n 357→ ml_replicas = np.array([r.ml_replicas for r in history], dtype=float)\n 358→ actual_cpu = np.array([r.actual_cpu for r in history], dtype=float)\n 359→\n 360→ # 1-2: Average replicas and replica-minutes\n 361→ hpa_avg = float(hpa_replicas.mean())\n 362→ ml_avg = float(ml_replicas.mean())\n 363→ hpa_rm = float(hpa_replicas.sum() * step_duration_minutes)\n 364→ ml_rm = float(ml_replicas.sum() * step_duration_minutes)\n 365→\n 366→ # 3: SLA violations\n 367→ # CPU per replica: actual / replicas. SLA violated if per-replica > threshold\n 368→ sla_threshold = target_utilization * sla_threshold_factor\n 369→\n 370→ # Model: total demand = actual_cpu * base_replicas (assume demand is independent of scaling)\n 371→ # Per-replica utilization = total_demand / replicas\n 372→ # For simplicity: if actual_cpu > sla_threshold, we consider it a violation\n 373→ # when replicas aren't enough to bring it below threshold\n 374→ # Effective per-replica CPU with n replicas: actual_cpu * (initial_replicas / n)\n 375→ # We use a simpler model: SLA violated when actual > threshold regardless of replicas\n 376→ # Actually more realistic: we treat actual_cpu as the \"demand signal\" and check\n 377→ # if the provisioned capacity (replicas * target) covers it\n 378→ hpa_capacity = hpa_replicas * target_utilization / 2 # capacity per 2 base replicas\n 379→ ml_capacity = ml_replicas * target_utilization / 2\n 380→\n 381→ # Simplified: demand is actual_cpu scaled to total cluster\n 382→ # For comparison fairness: SLA violated if actual_cpu > target * sla_factor\n 383→ # AND replicas are insufficient\n 384→ hpa_sla = int(np.sum(actual_cpu > sla_threshold))\n 385→ ml_sla = int(np.sum(actual_cpu > sla_threshold))\n 386→\n 387→ # More nuanced: SLA violation depends on replicas\n 388→ # Effective utilization = actual_cpu * (base / current_replicas)\n 389→ # where base=2 (min replicas that the CPU % is relative to)\n 390→ base_replicas = 2.0 # CPU readings are relative to this baseline\n 391→ hpa_effective = actual_cpu * base_replicas / hpa_replicas\n 392→ ml_effective = actual_cpu * base_replicas / ml_replicas\n 393→\n 394→ hpa_sla = int(np.sum(hpa_effective > sla_threshold))\n 395→ ml_sla = int(np.sum(ml_effective > sla_threshold))\n 396→\n 397→ hpa_sla_rate = hpa_sla / n\n 398→ ml_sla_rate = ml_sla / n\n 399→\n 400→ # 4-5: Over/under-provisioning\n 401→ # Capacity: replicas * target_utilization / base_replicas\n 402→ hpa_cap = hpa_replicas * target_utilization / base_replicas\n 403→ ml_cap = ml_replicas * target_utilization / base_replicas\n 404→\n 405→ hpa_over = float(np.mean(np.maximum(0, hpa_cap - actual_cpu) / np.maximum(hpa_cap, 1)))\n 406→ ml_over = float(np.mean(np.maximum(0, ml_cap - actual_cpu) / np.maximum(ml_cap, 1)))\n 407→\n 408→ hpa_under = float(np.mean(\n 409→ np.maximum(0, actual_cpu - hpa_cap) / np.maximum(actual_cpu, 1)\n 410→ ))\n 411→ ml_under = float(np.mean(\n 412→ np.maximum(0, actual_cpu - ml_cap) / np.maximum(actual_cpu, 1)\n 413→ ))\n 414→\n 415→ # 6: Scaling events (replica changes)\n 416→ hpa_events = int(np.sum(np.diff(hpa_replicas) != 0))\n 417→ ml_events = int(np.sum(np.diff(ml_replicas) != 0))\n 418→\n 419→ # 7: Reaction time (steps to respond to demand spike)\n 420→ hpa_rt = _compute_reaction_times(actual_cpu, hpa_replicas, target_utilization, base_replicas)\n 421→ ml_rt = _compute_reaction_times(actual_cpu, ml_replicas, target_utilization, base_replicas)\n 422→\n 423→ hpa_avg_rt = float(np.mean(hpa_rt)) if len(hpa_rt) > 0 else 0.0\n 424→ ml_avg_rt = float(np.mean(ml_rt)) if len(ml_rt) > 0 else 0.0\n 425→\n 426→ # 8: Cost savings\n 427→ cost_savings = (hpa_rm - ml_rm) / hpa_rm * 100 if hpa_rm > 0 else 0.0\n 428→\n 429→ return ExperimentMetrics(\n 430→ hpa_avg_replicas=round(hpa_avg, 2),\n 431→ ml_avg_replicas=round(ml_avg, 2),\n 432→ hpa_replica_minutes=round(hpa_rm, 1),\n 433→ ml_replica_minutes=round(ml_rm, 1),\n 434→ hpa_sla_violations=hpa_sla,\n 435→ ml_sla_violations=ml_sla,\n 436→ hpa_sla_violation_rate=round(hpa_sla_rate, 4),\n 437→ ml_sla_violation_rate=round(ml_sla_rate, 4),\n 438→ hpa_over_provisioning_pct=round(hpa_over * 100, 2),\n 439→ ml_over_provisioning_pct=round(ml_over * 100, 2),\n 440→ hpa_under_provisioning_pct=round(hpa_under * 100, 2),\n 441→ ml_under_provisioning_pct=round(ml_under * 100, 2),\n 442→ hpa_scaling_events=hpa_events,\n 443→ ml_scaling_events=ml_events,\n 444→ hpa_avg_reaction_time=round(hpa_avg_rt, 2),\n 445→ ml_avg_reaction_time=round(ml_avg_rt, 2),\n 446→ cost_savings_pct=round(cost_savings, 2),\n 447→ total_steps=n,\n 448→ )\n 449→\n 450→\n 451→def _compute_reaction_times(\n 452→ actual_cpu: np.ndarray,\n 453→ replicas: np.ndarray,\n 454→ target: float,\n 455→ base_replicas: float,\n 456→) -> list[float]:\n 457→ \"\"\"Compute reaction times: steps from spike onset to adequate provisioning.\n 458→\n 459→ A spike is when actual_cpu crosses above target.\n 460→ Adequate provisioning is when effective utilization drops below target.\n 461→ \"\"\"\n 462→ reaction_times: list[float] = []\n 463→ spike_start: int | None = None\n 464→\n 465→ for t in range(len(actual_cpu)):\n 466→ effective = actual_cpu[t] * base_replicas / replicas[t]\n 467→\n 468→ if actual_cpu[t] > target and spike_start is None:\n 469→ spike_start = t\n 470→\n 471→ if spike_start is not None and effective <= target:\n 472→ reaction_times.append(float(t - spike_start))\n 473→ spike_start = None\n 474→\n 475→ return reaction_times\n 476→\n 477→\n 478→# ---------------------------------------------------------------------------\n 479→# Experiment runner\n 480→# ---------------------------------------------------------------------------\n 481→\n 482→\n 483→class ExperimentRunner:\n 484→ \"\"\"Runs both simulators on the same CPU trace and collects results.\n 485→\n 486→ Args:\n 487→ actual_cpu: Array of actual CPU utilization (%) at each timestep.\n 488→ predicted_cpu: Array of ML-predicted CPU utilization (%) for each step.\n 489→ hpa_config: HPA simulator configuration.\n 490→ ml_config: ML autoscaler configuration.\n 491→ step_duration_minutes: Duration of each simulation step.\n 492→ \"\"\"\n 493→\n 494→ def __init__(\n 495→ self,\n 496→ actual_cpu: list[float] | np.ndarray,\n 497→ predicted_cpu: list[float] | np.ndarray,\n 498→ hpa_config: HPAConfig | None = None,\n 499→ ml_config: MLConfig | None = None,\n 500→ step_duration_minutes: float = 5.0,\n 501→ ) -> None:\n 502→ self.actual_cpu = np.asarray(actual_cpu, dtype=float)\n 503→ self.predicted_cpu = np.asarray(predicted_cpu, dtype=float)\n 504→ assert len(self.actual_cpu) == len(self.predicted_cpu), (\n 505→ f\"Length mismatch: actual={len(self.actual_cpu)}, \"\n 506→ f\"predicted={len(self.predicted_cpu)}\"\n 507→ )\n 508→ self.hpa_config = hpa_config or HPAConfig(step_duration_minutes=step_duration_minutes)\n 509→ self.ml_config = ml_config or MLConfig(step_duration_minutes=step_duration_minutes)\n 510→ self.step_duration = step_duration_minutes\n 511→\n 512→ def run(self) -> tuple[list[StepRecord], ExperimentMetrics]:\n 513→ \"\"\"Execute the simulation.\n 514→\n 515→ Returns:\n 516→ (history, metrics) — step-by-step records and aggregated metrics.\n 517→ \"\"\"\n 518→ hpa = HPASimulator(self.hpa_config)\n 519→ ml = MLAutoscalerSimulator(self.ml_config)\n 520→ history: list[StepRecord] = []\n 521→\n 522→ for t in range(len(self.actual_cpu)):\n 523→ actual = float(self.actual_cpu[t])\n 524→ predicted = float(self.predicted_cpu[t])\n 525→\n 526→ hpa_replicas, hpa_dir = hpa.step(actual, t)\n 527→ ml_replicas, ml_dir = ml.step(predicted, t)\n 528→\n 529→ history.append(StepRecord(\n 530→ t=t,\n 531→ actual_cpu=actual,\n 532→ predicted_cpu=predicted,\n 533→ hpa_replicas=hpa_replicas,\n 534→ ml_replicas=ml_replicas,\n 535→ hpa_decision=hpa_dir,\n 536→ ml_decision=ml_dir,\n 537→ ))\n 538→\n 539→ metrics = compute_metrics(\n 540→ history,\n 541→ step_duration_minutes=self.step_duration,\n 542→ target_utilization=self.hpa_config.target_utilization,\n 543→ )\n 544→\n 545→ return history, metrics\n 546→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n" | |
| } | |
| ] | |
| }, | |
| "parent_tool_use_id": null, | |
| "session_id": "ce1ab353-0721-47e7-a373-84e062534c7f", | |
| "uuid": "40ff8a35-3cdc-42c0-bea0-4c8188b224bb", | |
| "tool_use_result": { | |
| "type": "text", | |
| "file": { | |
| "filePath": "/tmp/gh-issue-solver-1770487990892/scripts/experiment_simulator.py", | |
| "content": "\"\"\"Deterministic experiment simulator: ML Autoscaler vs Reactive HPA.\n\nReplays historical CPU traces through both scaling strategies\nand computes comparison metrics without requiring a live cluster.\n\nUsage:\n from scripts.experiment_simulator import ExperimentRunner, HPASimulator, MLAutoscalerSimulator\n\n runner = ExperimentRunner(actual_cpu=cpu_trace, predicted_cpu=pred_trace)\n results = runner.run()\n\"\"\"\n\nfrom __future__ import annotations\n\nimport math\nfrom dataclasses import dataclass, field\nfrom typing import Literal\n\nimport numpy as np\n\n\n# ---------------------------------------------------------------------------\n# Data containers\n# ---------------------------------------------------------------------------\n\n\n@dataclass\nclass StepRecord:\n \"\"\"Single timestep record for both simulators.\"\"\"\n\n t: int\n actual_cpu: float\n predicted_cpu: float\n hpa_replicas: int\n ml_replicas: int\n hpa_decision: str # \"up\", \"down\", \"hold\"\n ml_decision: str\n\n\n@dataclass\nclass ExperimentMetrics:\n \"\"\"Aggregated comparison metrics from a simulation run.\"\"\"\n\n # Resource usage\n hpa_avg_replicas: float\n ml_avg_replicas: float\n hpa_replica_minutes: float\n ml_replica_minutes: float\n\n # SLA\n hpa_sla_violations: int\n ml_sla_violations: int\n hpa_sla_violation_rate: float\n ml_sla_violation_rate: float\n\n # Provisioning quality\n hpa_over_provisioning_pct: float\n ml_over_provisioning_pct: float\n hpa_under_provisioning_pct: float\n ml_under_provisioning_pct: float\n\n # Stability\n hpa_scaling_events: int\n ml_scaling_events: int\n\n # Reaction time (steps to respond to demand spike)\n hpa_avg_reaction_time: float\n ml_avg_reaction_time: float\n\n # Cost savings\n cost_savings_pct: float\n\n # Total steps\n total_steps: int\n\n\n# ---------------------------------------------------------------------------\n# HPA Simulator (Kubernetes HPA v2 behaviour)\n# ---------------------------------------------------------------------------\n\n\n@dataclass\nclass HPAConfig:\n \"\"\"Parameters matching Kubernetes HPA v2 behaviour.\"\"\"\n\n target_utilization: float = 60.0\n min_replicas: int = 2\n max_replicas: int = 8\n # HPA allows up to doubling, but we cap at +4 for realism\n max_scale_up_step: int = 4\n max_scale_down_step: int = 1\n # HPA defaults (converted to 5-min steps)\n # scale-up stabilization: 0 (immediate in k8s), but 3 min cooldown ≈ 0 steps at 5-min\n scale_up_cooldown_steps: int = 1\n # scale-down stabilization: 5 min default → 1 step in 5-min window\n scale_down_stabilization_count: int = 1\n # Tolerance: HPA has 10% tolerance band\n tolerance: float = 0.1\n step_duration_minutes: float = 5.0\n\n\nclass HPASimulator:\n \"\"\"Simulates Kubernetes HPA v2 reactive autoscaling.\n\n The HPA formula:\n desiredReplicas = ceil(currentReplicas * (currentMetricValue / desiredMetricValue))\n\n With tolerance band: no action if ratio is within [1-tolerance, 1+tolerance].\n \"\"\"\n\n def __init__(self, config: HPAConfig | None = None) -> None:\n self.config = config or HPAConfig()\n self.replicas = self.config.min_replicas\n self._last_scale_up_step: int | None = None\n self._last_scale_down_step: int | None = None\n self._consecutive_below: int = 0\n\n def step(self, actual_cpu: float, t: int) -> tuple[int, str]:\n \"\"\"Process one timestep.\n\n Args:\n actual_cpu: Current actual CPU utilization (%).\n t: Step index.\n\n Returns:\n (new_replicas, decision_direction)\n \"\"\"\n cfg = self.config\n ratio = actual_cpu / cfg.target_utilization if cfg.target_utilization > 0 else 1.0\n\n # Tolerance band: HPA skips if within ±10%\n if abs(ratio - 1.0) <= cfg.tolerance:\n self._consecutive_below = 0\n return self.replicas, \"hold\"\n\n if ratio > 1.0 + cfg.tolerance:\n # Scale up\n direction = \"up\"\n self._consecutive_below = 0\n\n # Cooldown check\n if (\n self._last_scale_up_step is not None\n and (t - self._last_scale_up_step) < cfg.scale_up_cooldown_steps\n ):\n return self.replicas, \"hold\"\n\n desired = math.ceil(self.replicas * ratio)\n desired = min(desired, self.replicas + cfg.max_scale_up_step)\n desired = max(cfg.min_replicas, min(cfg.max_replicas, desired))\n\n if desired > self.replicas:\n self._last_scale_up_step = t\n self.replicas = desired\n return self.replicas, direction\n\n return self.replicas, \"hold\"\n\n else:\n # Scale down (ratio < 1.0 - tolerance)\n direction = \"down\"\n self._consecutive_below += 1\n\n # Scale-down stabilization\n if self._consecutive_below < cfg.scale_down_stabilization_count:\n return self.replicas, \"hold\"\n\n # Cooldown check\n if (\n self._last_scale_down_step is not None\n and (t - self._last_scale_down_step) < cfg.scale_down_stabilization_count\n ):\n return self.replicas, \"hold\"\n\n desired = math.ceil(self.replicas * ratio)\n desired = max(desired, self.replicas - cfg.max_scale_down_step)\n desired = max(cfg.min_replicas, min(cfg.max_replicas, desired))\n\n if desired < self.replicas:\n self._last_scale_down_step = t\n self._consecutive_below = 0\n self.replicas = desired\n return self.replicas, direction\n\n return self.replicas, \"hold\"\n\n\n# ---------------------------------------------------------------------------\n# ML Autoscaler Simulator (wraps our real algorithm)\n# ---------------------------------------------------------------------------\n\n\n@dataclass\nclass MLConfig:\n \"\"\"Parameters for the ML autoscaler (mirrors ScalingConfig).\"\"\"\n\n target_utilization: float = 60.0\n min_replicas: int = 2\n max_replicas: int = 8\n safety_margin: float = 0.15\n smoothing_alpha: float = 0.7\n scale_up_threshold_factor: float = 1.1\n scale_down_threshold_factor: float = 0.7\n max_scale_up_step: int = 2\n max_scale_down_step: int = 1\n scale_up_cooldown_seconds: float = 60.0\n scale_down_cooldown_seconds: float = 300.0\n scale_down_stabilization_count: int = 3\n step_duration_minutes: float = 5.0\n\n\nclass MLAutoscalerSimulator:\n \"\"\"Simulates the ML-based predictive autoscaler.\n\n Uses the real `compute_scaling_decision()` from planner/algorithm.py\n internally but through a clean simulation interface.\n \"\"\"\n\n def __init__(self, config: MLConfig | None = None) -> None:\n self.config = config or MLConfig()\n self.replicas = self.config.min_replicas\n\n # Internal state replicating ScalingState\n self._smoothed: float | None = None\n self._last_scale_up_time: float | None = None\n self._last_scale_down_time: float | None = None\n self._consecutive_below: int = 0\n\n def step(self, predicted_cpu: float, t: int) -> tuple[int, str]:\n \"\"\"Process one timestep with ML prediction.\n\n Args:\n predicted_cpu: ML-predicted CPU utilization (%) for the future.\n t: Step index.\n\n Returns:\n (new_replicas, decision_direction)\n \"\"\"\n cfg = self.config\n now = t * cfg.step_duration_minutes * 60 # Convert to seconds\n\n # Step 1: EMA smoothing\n if self._smoothed is None:\n self._smoothed = predicted_cpu\n else:\n self._smoothed = (\n cfg.smoothing_alpha * predicted_cpu\n + (1 - cfg.smoothing_alpha) * self._smoothed\n )\n\n smoothed = self._smoothed\n\n # Step 2: Hysteresis\n threshold_up = cfg.target_utilization * cfg.scale_up_threshold_factor\n threshold_down = cfg.target_utilization * cfg.scale_down_threshold_factor\n\n if smoothed > threshold_up:\n direction: Literal[\"up\", \"down\", \"hold\"] = \"up\"\n self._consecutive_below = 0\n elif smoothed < threshold_down:\n direction = \"down\"\n self._consecutive_below += 1\n else:\n # Dead-zone: hold\n self._consecutive_below = 0\n return self.replicas, \"hold\"\n\n # Step 3: Compute desired replicas\n if cfg.target_utilization > 0:\n raw = self.replicas * smoothed / cfg.target_utilization\n desired = math.ceil(raw)\n if direction == \"up\":\n desired = math.ceil(desired * (1 + cfg.safety_margin))\n else:\n desired = self.replicas\n\n # Step 4: Clamp\n desired = max(cfg.min_replicas, min(cfg.max_replicas, desired))\n\n # Step 5: Rate limits\n if desired > self.replicas:\n desired = min(desired, self.replicas + cfg.max_scale_up_step)\n elif desired < self.replicas:\n desired = max(desired, self.replicas - cfg.max_scale_down_step)\n\n # Step 6: Cooldowns\n if direction == \"up\":\n if (\n self._last_scale_up_time is not None\n and (now - self._last_scale_up_time) < cfg.scale_up_cooldown_seconds\n ):\n return self.replicas, \"hold\"\n\n if direction == \"down\":\n if (\n self._last_scale_down_time is not None\n and (now - self._last_scale_down_time) < cfg.scale_down_cooldown_seconds\n ):\n return self.replicas, \"hold\"\n\n # Step 7: Scale-down stabilization\n if (\n direction == \"down\"\n and self._consecutive_below < cfg.scale_down_stabilization_count\n ):\n return self.replicas, \"hold\"\n\n # Step 8: No change check\n if desired == self.replicas:\n return self.replicas, \"hold\"\n\n # Step 9: Commit\n if direction == \"up\":\n self._last_scale_up_time = now\n else:\n self._last_scale_down_time = now\n self._consecutive_below = 0\n\n self.replicas = desired\n return self.replicas, direction\n\n\n# ---------------------------------------------------------------------------\n# Metrics computation\n# ---------------------------------------------------------------------------\n\n\ndef compute_metrics(\n history: list[StepRecord],\n step_duration_minutes: float = 5.0,\n sla_threshold_factor: float = 1.2,\n target_utilization: float = 60.0,\n) -> ExperimentMetrics:\n \"\"\"Compute comparison metrics from simulation history.\n\n Args:\n history: List of StepRecord from a simulation run.\n step_duration_minutes: Duration of each step in minutes.\n sla_threshold_factor: SLA is violated when CPU > target * factor.\n target_utilization: Target CPU % for capacity calculation.\n \"\"\"\n n = len(history)\n if n == 0:\n return ExperimentMetrics(\n hpa_avg_replicas=0, ml_avg_replicas=0,\n hpa_replica_minutes=0, ml_replica_minutes=0,\n hpa_sla_violations=0, ml_sla_violations=0,\n hpa_sla_violation_rate=0, ml_sla_violation_rate=0,\n hpa_over_provisioning_pct=0, ml_over_provisioning_pct=0,\n hpa_under_provisioning_pct=0, ml_under_provisioning_pct=0,\n hpa_scaling_events=0, ml_scaling_events=0,\n hpa_avg_reaction_time=0, ml_avg_reaction_time=0,\n cost_savings_pct=0, total_steps=0,\n )\n\n hpa_replicas = np.array([r.hpa_replicas for r in history], dtype=float)\n ml_replicas = np.array([r.ml_replicas for r in history], dtype=float)\n actual_cpu = np.array([r.actual_cpu for r in history], dtype=float)\n\n # 1-2: Average replicas and replica-minutes\n hpa_avg = float(hpa_replicas.mean())\n ml_avg = float(ml_replicas.mean())\n hpa_rm = float(hpa_replicas.sum() * step_duration_minutes)\n ml_rm = float(ml_replicas.sum() * step_duration_minutes)\n\n # 3: SLA violations\n # CPU per replica: actual / replicas. SLA violated if per-replica > threshold\n sla_threshold = target_utilization * sla_threshold_factor\n\n # Model: total demand = actual_cpu * base_replicas (assume demand is independent of scaling)\n # Per-replica utilization = total_demand / replicas\n # For simplicity: if actual_cpu > sla_threshold, we consider it a violation\n # when replicas aren't enough to bring it below threshold\n # Effective per-replica CPU with n replicas: actual_cpu * (initial_replicas / n)\n # We use a simpler model: SLA violated when actual > threshold regardless of replicas\n # Actually more realistic: we treat actual_cpu as the \"demand signal\" and check\n # if the provisioned capacity (replicas * target) covers it\n hpa_capacity = hpa_replicas * target_utilization / 2 # capacity per 2 base replicas\n ml_capacity = ml_replicas * target_utilization / 2\n\n # Simplified: demand is actual_cpu scaled to total cluster\n # For comparison fairness: SLA violated if actual_cpu > target * sla_factor\n # AND replicas are insufficient\n hpa_sla = int(np.sum(actual_cpu > sla_threshold))\n ml_sla = int(np.sum(actual_cpu > sla_threshold))\n\n # More nuanced: SLA violation depends on replicas\n # Effective utilization = actual_cpu * (base / current_replicas)\n # where base=2 (min replicas that the CPU % is relative to)\n base_replicas = 2.0 # CPU readings are relative to this baseline\n hpa_effective = actual_cpu * base_replicas / hpa_replicas\n ml_effective = actual_cpu * base_replicas / ml_replicas\n\n hpa_sla = int(np.sum(hpa_effective > sla_threshold))\n ml_sla = int(np.sum(ml_effective > sla_threshold))\n\n hpa_sla_rate = hpa_sla / n\n ml_sla_rate = ml_sla / n\n\n # 4-5: Over/under-provisioning\n # Capacity: replicas * target_utilization / base_replicas\n hpa_cap = hpa_replicas * target_utilization / base_replicas\n ml_cap = ml_replicas * target_utilization / base_replicas\n\n hpa_over = float(np.mean(np.maximum(0, hpa_cap - actual_cpu) / np.maximum(hpa_cap, 1)))\n ml_over = float(np.mean(np.maximum(0, ml_cap - actual_cpu) / np.maximum(ml_cap, 1)))\n\n hpa_under = float(np.mean(\n np.maximum(0, actual_cpu - hpa_cap) / np.maximum(actual_cpu, 1)\n ))\n ml_under = float(np.mean(\n np.maximum(0, actual_cpu - ml_cap) / np.maximum(actual_cpu, 1)\n ))\n\n # 6: Scaling events (replica changes)\n hpa_events = int(np.sum(np.diff(hpa_replicas) != 0))\n ml_events = int(np.sum(np.diff(ml_replicas) != 0))\n\n # 7: Reaction time (steps to respond to demand spike)\n hpa_rt = _compute_reaction_times(actual_cpu, hpa_replicas, target_utilization, base_replicas)\n ml_rt = _compute_reaction_times(actual_cpu, ml_replicas, target_utilization, base_replicas)\n\n hpa_avg_rt = float(np.mean(hpa_rt)) if len(hpa_rt) > 0 else 0.0\n ml_avg_rt = float(np.mean(ml_rt)) if len(ml_rt) > 0 else 0.0\n\n # 8: Cost savings\n cost_savings = (hpa_rm - ml_rm) / hpa_rm * 100 if hpa_rm > 0 else 0.0\n\n return ExperimentMetrics(\n hpa_avg_replicas=round(hpa_avg, 2),\n ml_avg_replicas=round(ml_avg, 2),\n hpa_replica_minutes=round(hpa_rm, 1),\n ml_replica_minutes=round(ml_rm, 1),\n hpa_sla_violations=hpa_sla,\n ml_sla_violations=ml_sla,\n hpa_sla_violation_rate=round(hpa_sla_rate, 4),\n ml_sla_violation_rate=round(ml_sla_rate, 4),\n hpa_over_provisioning_pct=round(hpa_over * 100, 2),\n ml_over_provisioning_pct=round(ml_over * 100, 2),\n hpa_under_provisioning_pct=round(hpa_under * 100, 2),\n ml_under_provisioning_pct=round(ml_under * 100, 2),\n hpa_scaling_events=hpa_events,\n ml_scaling_events=ml_events,\n hpa_avg_reaction_time=round(hpa_avg_rt, 2),\n ml_avg_reaction_time=round(ml_avg_rt, 2),\n cost_savings_pct=round(cost_savings, 2),\n total_steps=n,\n )\n\n\ndef _compute_reaction_times(\n actual_cpu: np.ndarray,\n replicas: np.ndarray,\n target: float,\n base_replicas: float,\n) -> list[float]:\n \"\"\"Compute reaction times: steps from spike onset to adequate provisioning.\n\n A spike is when actual_cpu crosses above target.\n Adequate provisioning is when effective utilization drops below target.\n \"\"\"\n reaction_times: list[float] = []\n spike_start: int | None = None\n\n for t in range(len(actual_cpu)):\n effective = actual_cpu[t] * base_replicas / replicas[t]\n\n if actual_cpu[t] > target and spike_start is None:\n spike_start = t\n\n if spike_start is not None and effective <= target:\n reaction_times.append(float(t - spike_start))\n spike_start = None\n\n return reaction_times\n\n\n# ---------------------------------------------------------------------------\n# Experiment runner\n# ---------------------------------------------------------------------------\n\n\nclass ExperimentRunner:\n \"\"\"Runs both simulators on the same CPU trace and collects results.\n\n Args:\n actual_cpu: Array of actual CPU utilization (%) at each timestep.\n predicted_cpu: Array of ML-predicted CPU utilization (%) for each step.\n hpa_config: HPA simulator configuration.\n ml_config: ML autoscaler configuration.\n step_duration_minutes: Duration of each simulation step.\n \"\"\"\n\n def __init__(\n self,\n actual_cpu: list[float] | np.ndarray,\n predicted_cpu: list[float] | np.ndarray,\n hpa_config: HPAConfig | None = None,\n ml_config: MLConfig | None = None,\n step_duration_minutes: float = 5.0,\n ) -> None:\n self.actual_cpu = np.asarray(actual_cpu, dtype=float)\n self.predicted_cpu = np.asarray(predicted_cpu, dtype=float)\n assert len(self.actual_cpu) == len(self.predicted_cpu), (\n f\"Length mismatch: actual={len(self.actual_cpu)}, \"\n f\"predicted={len(self.predicted_cpu)}\"\n )\n self.hpa_config = hpa_config or HPAConfig(step_duration_minutes=step_duration_minutes)\n self.ml_config = ml_config or MLConfig(step_duration_minutes=step_duration_minutes)\n self.step_duration = step_duration_minutes\n\n def run(self) -> tuple[list[StepRecord], ExperimentMetrics]:\n \"\"\"Execute the simulation.\n\n Returns:\n (history, metrics) — step-by-step records and aggregated metrics.\n \"\"\"\n hpa = HPASimulator(self.hpa_config)\n ml = MLAutoscalerSimulator(self.ml_config)\n history: list[StepRecord] = []\n\n for t in range(len(self.actual_cpu)):\n actual = float(self.actual_cpu[t])\n predicted = float(self.predicted_cpu[t])\n\n hpa_replicas, hpa_dir = hpa.step(actual, t)\n ml_replicas, ml_dir = ml.step(predicted, t)\n\n history.append(StepRecord(\n t=t,\n actual_cpu=actual,\n predicted_cpu=predicted,\n hpa_replicas=hpa_replicas,\n ml_replicas=ml_replicas,\n hpa_decision=hpa_dir,\n ml_decision=ml_dir,\n ))\n\n metrics = compute_metrics(\n history,\n step_duration_minutes=self.step_duration,\n target_utilization=self.hpa_config.target_utilization,\n )\n\n return history, metrics\n", | |
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| "content": " 1→[tool.poetry]\n 2→name = \"k8s-ml-predictive-autoscaling\"\n 3→version = \"0.1.0\"\n 4→description = \"Predictive autoscaling research environment for Kubernetes with ML-driven forecasting.\"\n 5→authors = [\"VisageDvachevsky <mr.echxo@gmail.com>\"]\n 6→license = \"MIT\"\n 7→readme = \"README.md\"\n 8→packages = [\n 9→ { include = \"k8s_ml_predictive_autoscaling\", from = \"src\" }\n 10→]\n 11→keywords = [\"kubernetes\", \"prometheus\", \"autoscaling\", \"ml\", \"fastapi\"]\n 12→\n 13→[tool.poetry.dependencies]\n 14→python = \"^3.11\"\n 15→fastapi = \"^0.111.0\"\n 16→uvicorn = { extras = [\"standard\"], version = \"^0.30.0\" }\n 17→prometheus-client = \"^0.20.0\"\n 18→pydantic = \"^2.7.0\"\n 19→pydantic-settings = \"^2.2.1\"\n 20→httpx = \"^0.27.0\"\n 21→PyYAML = \"^6.0.1\"\n 22→pandas = \"^2.2.0\"\n 23→numpy = \"^1.26.0\"\n 24→scikit-learn = \"^1.4.0\"\n 25→joblib = \"^1.4.2\"\n 26→torch = \"^2.0.0\"\n 27→prophet = \"^1.1.0\"\n 28→scipy = \"^1.12.0\"\n 29→onnx = \"^1.16.0\"\n 30→onnxruntime = \"^1.18.0\"\n 31→kubernetes = {version = \"^29.0.0\", optional = true}\n 32→\n 33→[tool.poetry.group.dev.dependencies]\n 34→pytest = \"^8.2.0\"\n 35→pytest-asyncio = \"^0.23.6\"\n 36→pytest-cov = \"^4.1.0\"\n 37→black = { extras = [\"jupyter\"], version = \"^24.4.0\" }\n 38→flake8 = \"^7.0.0\"\n 39→mypy = \"^1.10.0\"\n 40→isort = \"^5.13.0\"\n 41→types-requests = \"^2.32.0\"\n 42→pandas-stubs = \"^2.1.0.240807\"\n 43→types-PyYAML = \"^6.0.12.20240917\"\n 44→locust = \"^2.30.0\"\n 45→matplotlib = \"^3.8.0\"\n 46→seaborn = \"^0.13.0\"\n 47→customtkinter = \"^5.2.0\"\n 48→\n 49→[build-system]\n 50→requires = [\"poetry-core>=1.8.0\"]\n 51→build-backend = \"poetry.core.masonry.api\"\n 52→\n 53→[tool.black]\n 54→target-version = [\"py311\"]\n 55→line-length = 100\n 56→\n 57→[tool.isort]\n 58→profile = \"black\"\n 59→line_length = 100\n 60→\n 61→[tool.pytest.ini_options]\n 62→testpaths = [\"tests\"]\n 63→pythonpath = [\"src\"]\n 64→addopts = \"-ra\"\n 65→\n 66→[tool.mypy]\n 67→python_version = \"3.11\"\n 68→warn_return_any = true\n 69→warn_unused_configs = true\n 70→disallow_untyped_defs = true\n 71→ignore_missing_imports = false\n 72→strict_optional = true\n 73→mypy_path = \"src\"\n 74→packages = [\"k8s_ml_predictive_autoscaling\"]\n 75→\n 76→[[tool.mypy.overrides]]\n 77→module = [\"joblib\", \"sklearn.*\", \"torch\", \"torch.*\", \"prophet\", \"scipy\", \"scipy.*\", \"onnx\", \"onnxruntime\", \"onnxruntime.*\", \"kubernetes\", \"kubernetes.*\"]\n 78→ignore_missing_imports = true\n 79→\n 80→[tool.coverage.run]\n 81→branch = true\n 82→source = [\"k8s_ml_predictive_autoscaling\"]\n 83→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n" | |
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| "content": "[tool.poetry]\nname = \"k8s-ml-predictive-autoscaling\"\nversion = \"0.1.0\"\ndescription = \"Predictive autoscaling research environment for Kubernetes with ML-driven forecasting.\"\nauthors = [\"VisageDvachevsky <mr.echxo@gmail.com>\"]\nlicense = \"MIT\"\nreadme = \"README.md\"\npackages = [\n { include = \"k8s_ml_predictive_autoscaling\", from = \"src\" }\n]\nkeywords = [\"kubernetes\", \"prometheus\", \"autoscaling\", \"ml\", \"fastapi\"]\n\n[tool.poetry.dependencies]\npython = \"^3.11\"\nfastapi = \"^0.111.0\"\nuvicorn = { extras = [\"standard\"], version = \"^0.30.0\" }\nprometheus-client = \"^0.20.0\"\npydantic = \"^2.7.0\"\npydantic-settings = \"^2.2.1\"\nhttpx = \"^0.27.0\"\nPyYAML = \"^6.0.1\"\npandas = \"^2.2.0\"\nnumpy = \"^1.26.0\"\nscikit-learn = \"^1.4.0\"\njoblib = \"^1.4.2\"\ntorch = \"^2.0.0\"\nprophet = \"^1.1.0\"\nscipy = \"^1.12.0\"\nonnx = \"^1.16.0\"\nonnxruntime = \"^1.18.0\"\nkubernetes = {version = \"^29.0.0\", optional = true}\n\n[tool.poetry.group.dev.dependencies]\npytest = \"^8.2.0\"\npytest-asyncio = \"^0.23.6\"\npytest-cov = \"^4.1.0\"\nblack = { extras = [\"jupyter\"], version = \"^24.4.0\" }\nflake8 = \"^7.0.0\"\nmypy = \"^1.10.0\"\nisort = \"^5.13.0\"\ntypes-requests = \"^2.32.0\"\npandas-stubs = \"^2.1.0.240807\"\ntypes-PyYAML = \"^6.0.12.20240917\"\nlocust = \"^2.30.0\"\nmatplotlib = \"^3.8.0\"\nseaborn = \"^0.13.0\"\ncustomtkinter = \"^5.2.0\"\n\n[build-system]\nrequires = [\"poetry-core>=1.8.0\"]\nbuild-backend = \"poetry.core.masonry.api\"\n\n[tool.black]\ntarget-version = [\"py311\"]\nline-length = 100\n\n[tool.isort]\nprofile = \"black\"\nline_length = 100\n\n[tool.pytest.ini_options]\ntestpaths = [\"tests\"]\npythonpath = [\"src\"]\naddopts = \"-ra\"\n\n[tool.mypy]\npython_version = \"3.11\"\nwarn_return_any = true\nwarn_unused_configs = true\ndisallow_untyped_defs = true\nignore_missing_imports = false\nstrict_optional = true\nmypy_path = \"src\"\npackages = [\"k8s_ml_predictive_autoscaling\"]\n\n[[tool.mypy.overrides]]\nmodule = [\"joblib\", \"sklearn.*\", \"torch\", \"torch.*\", \"prophet\", \"scipy\", \"scipy.*\", \"onnx\", \"onnxruntime\", \"onnxruntime.*\", \"kubernetes\", \"kubernetes.*\"]\nignore_missing_imports = true\n\n[tool.coverage.run]\nbranch = true\nsource = [\"k8s_ml_predictive_autoscaling\"]\n", | |
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| [2026-02-07T18:14:02.981Z] [INFO] { | |
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| "text": "Now let me also read the experiment scenarios file and the contributing guidelines." | |
| } | |
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| [2026-02-07T18:14:04.052Z] [INFO] { | |
| "type": "assistant", | |
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| "model": "claude-opus-4-6", | |
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| "file_path": "/tmp/gh-issue-solver-1770487990892/scripts/experiment_scenarios.py" | |
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| "uuid": "3e4cc02c-f2f5-49ad-a250-cd243a3cb5b1" | |
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| [2026-02-07T18:14:04.058Z] [INFO] { | |
| "type": "user", | |
| "message": { | |
| "role": "user", | |
| "content": [ | |
| { | |
| "tool_use_id": "toolu_01WSigxt9iKLgdv2XZ6ns6vA", | |
| "type": "tool_result", | |
| "content": " 1→\"\"\"Load scenarios for experiment simulation.\n 2→\n 3→Generates 4 distinct workload patterns from real data and synthetic profiles,\n 4→each producing (actual_cpu, predicted_cpu) trace pairs for the simulator.\n 5→\n 6→Usage:\n 7→ from scripts.experiment_scenarios import get_all_scenarios, Scenario\n 8→ scenarios = get_all_scenarios(\"data/processed/test.csv\", \"data/processed/scaler.pkl\")\n 9→\"\"\"\n 10→\n 11→from __future__ import annotations\n 12→\n 13→import json\n 14→import math\n 15→from dataclasses import dataclass\n 16→from pathlib import Path\n 17→\n 18→import joblib\n 19→import numpy as np\n 20→import pandas as pd\n 21→\n 22→\n 23→@dataclass\n 24→class Scenario:\n 25→ \"\"\"A named experiment scenario with CPU traces.\"\"\"\n 26→\n 27→ name: str\n 28→ description: str\n 29→ actual_cpu: np.ndarray # shape (N,) — actual CPU % (original scale)\n 30→ predicted_cpu: np.ndarray # shape (N,) — predicted CPU % (original scale)\n 31→ duration_hours: float\n 32→\n 33→ @property\n 34→ def steps(self) -> int:\n 35→ return len(self.actual_cpu)\n 36→\n 37→\n 38→def _load_test_data(\n 39→ test_csv_path: str | Path,\n 40→ scaler_path: str | Path,\n 41→ metadata_path: str | Path | None = None,\n 42→) -> tuple[np.ndarray, np.ndarray, float, float]:\n 43→ \"\"\"Load test.csv and inverse-transform CPU columns to original scale.\n 44→\n 45→ Returns:\n 46→ (actual_cpu, target_cpu_t3, scaler_mean, scaler_scale)\n 47→ \"\"\"\n 48→ df = pd.read_csv(test_csv_path)\n 49→ scaler = joblib.load(scaler_path)\n 50→\n 51→ # Determine CPU index in scaler\n 52→ if metadata_path is not None:\n 53→ with open(metadata_path) as f:\n 54→ meta = json.load(f)\n 55→ scaler_cols = meta[\"scaler_feature_columns\"]\n 56→ else:\n 57→ # Fallback: cpu_usage is always first\n 58→ scaler_cols = [\"cpu_usage\"]\n 59→\n 60→ cpu_idx = scaler_cols.index(\"cpu_usage\")\n 61→ cpu_mean = scaler.mean_[cpu_idx]\n 62→ cpu_scale = scaler.scale_[cpu_idx]\n 63→\n 64→ # Inverse transform: original = scaled * scale + mean\n 65→ actual = df[\"cpu_usage\"].values * cpu_scale + cpu_mean\n 66→ target = df[\"target_cpu_usage_t+3\"].values * cpu_scale + cpu_mean\n 67→\n 68→ return actual, target, cpu_mean, cpu_scale\n 69→\n 70→\n 71→def _add_prediction_noise(\n 72→ actual: np.ndarray,\n 73→ target_t3: np.ndarray,\n 74→ noise_std: float = 2.0,\n 75→ seed: int = 42,\n 76→) -> np.ndarray:\n 77→ \"\"\"Generate predicted CPU by shifting target_t+3 with small noise.\n 78→\n 79→ The LSTM model achieves R²=0.89, so predictions are close to reality\n 80→ but not perfect. We simulate this by using target_t+3 (the actual\n 81→ future value) with small Gaussian noise added.\n 82→ \"\"\"\n 83→ rng = np.random.RandomState(seed)\n 84→ noise = rng.normal(0, noise_std, size=len(target_t3))\n 85→ predicted = target_t3 + noise\n 86→ return np.clip(predicted, 0, 100)\n 87→\n 88→\n 89→# ---------------------------------------------------------------------------\n 90→# Scenario generators\n 91→# ---------------------------------------------------------------------------\n 92→\n 93→\n 94→def scenario_stable(\n 95→ actual_full: np.ndarray,\n 96→ target_full: np.ndarray,\n 97→) -> Scenario:\n 98→ \"\"\"Scenario 1: Stable workload (~45-55% CPU).\n 99→\n 100→ Select a segment with low variance from the real data.\n 101→ \"\"\"\n 102→ window = 50\n 103→ n = len(actual_full)\n 104→ best_start = 0\n 105→ best_var = float(\"inf\")\n 106→\n 107→ # Find the most stable 300-step window\n 108→ target_len = 300\n 109→ for start in range(0, n - target_len, window):\n 110→ segment = actual_full[start : start + target_len]\n 111→ var = float(np.var(segment))\n 112→ if var < best_var:\n 113→ best_var = var\n 114→ best_start = start\n 115→\n 116→ actual = actual_full[best_start : best_start + target_len].copy()\n 117→ predicted = _add_prediction_noise(\n 118→ actual, target_full[best_start : best_start + target_len], noise_std=1.5, seed=100\n 119→ )\n 120→\n 121→ return Scenario(\n 122→ name=\"stable_workload\",\n 123→ description=\"Stable workload with low variance (~45-55% CPU). \"\n 124→ \"Tests steady-state behavior and resource efficiency.\",\n 125→ actual_cpu=actual,\n 126→ predicted_cpu=predicted,\n 127→ duration_hours=target_len * 5 / 60,\n 128→ )\n 129→\n 130→\n 131→def scenario_gradual_ramp(\n 132→ actual_full: np.ndarray,\n 133→ target_full: np.ndarray,\n 134→) -> Scenario:\n 135→ \"\"\"Scenario 2: Gradual ramp from low to high load.\n 136→\n 137→ Synthetic ramp: 30% → 85% over 400 steps with realistic noise.\n 138→ \"\"\"\n 139→ n_steps = 400\n 140→ rng = np.random.RandomState(200)\n 141→\n 142→ # Create ramp\n 143→ t = np.linspace(0, 1, n_steps)\n 144→ base_ramp = 30 + 55 * t # 30% → 85%\n 145→\n 146→ # Add realistic noise from actual data distribution\n 147→ noise = rng.normal(0, 3, size=n_steps)\n 148→ actual = np.clip(base_ramp + noise, 10, 100)\n 149→\n 150→ # Predicted: ramp shifted forward by 3 steps (15 min lookahead)\n 151→ predicted_base = np.roll(base_ramp, -3)\n 152→ predicted_base[-3:] = base_ramp[-1] # Extend last value\n 153→ predicted_noise = rng.normal(0, 2.5, size=n_steps)\n 154→ predicted = np.clip(predicted_base + predicted_noise, 10, 100)\n 155→\n 156→ return Scenario(\n 157→ name=\"gradual_ramp\",\n 158→ description=\"Gradual load increase from 30% to 85% CPU over ~33 hours. \"\n 159→ \"Tests proactive scaling during sustained growth.\",\n 160→ actual_cpu=actual,\n 161→ predicted_cpu=predicted,\n 162→ duration_hours=n_steps * 5 / 60,\n 163→ )\n 164→\n 165→\n 166→def scenario_spike_pattern(\n 167→ actual_full: np.ndarray,\n 168→ target_full: np.ndarray,\n 169→) -> Scenario:\n 170→ \"\"\"Scenario 3: Spike pattern with sudden load increases.\n 171→\n 172→ 3 spikes: 40% → 85% → 40%, each lasting ~30 steps.\n 173→ This is where ML prediction shines — it can anticipate spikes.\n 174→ \"\"\"\n 175→ n_steps = 350\n 176→ rng = np.random.RandomState(300)\n 177→\n 178→ # Base: moderate load with 3 spikes\n 179→ actual = np.full(n_steps, 42.0)\n 180→ noise = rng.normal(0, 3, size=n_steps)\n 181→\n 182→ # Spike 1: steps 50-80 (sharp)\n 183→ for i in range(50, 80):\n 184→ progress = (i - 50) / 30\n 185→ if progress < 0.2:\n 186→ actual[i] = 42 + (85 - 42) * (progress / 0.2) # Sharp rise\n 187→ elif progress < 0.7:\n 188→ actual[i] = 85 # Sustained peak\n 189→ else:\n 190→ actual[i] = 85 - (85 - 42) * ((progress - 0.7) / 0.3) # Gradual decline\n 191→\n 192→ # Spike 2: steps 140-185 (gradual buildup)\n 193→ for i in range(130, 185):\n 194→ progress = (i - 130) / 55\n 195→ if progress < 0.4:\n 196→ actual[i] = 42 + (90 - 42) * (progress / 0.4)\n 197→ elif progress < 0.7:\n 198→ actual[i] = 90\n 199→ else:\n 200→ actual[i] = 90 - (90 - 42) * ((progress - 0.7) / 0.3)\n 201→\n 202→ # Spike 3: steps 250-290 (double spike)\n 203→ for i in range(250, 270):\n 204→ progress = (i - 250) / 20\n 205→ actual[i] = 42 + (78 - 42) * math.sin(progress * math.pi)\n 206→ for i in range(275, 300):\n 207→ progress = (i - 275) / 25\n 208→ actual[i] = 42 + (92 - 42) * math.sin(progress * math.pi)\n 209→\n 210→ actual = np.clip(actual + noise, 10, 100)\n 211→\n 212→ # Predicted: ML model sees 3 steps ahead (the model's lookahead)\n 213→ predicted = np.roll(actual, -3)\n 214→ predicted[-3:] = actual[-1]\n 215→ pred_noise = rng.normal(0, 3.0, size=n_steps)\n 216→ predicted = np.clip(predicted + pred_noise, 10, 100)\n 217→\n 218→ return Scenario(\n 219→ name=\"spike_pattern\",\n 220→ description=\"Three load spikes (42% → 85-92% → 42%) testing reactive vs \"\n 221→ \"predictive response to sudden demand changes.\",\n 222→ actual_cpu=actual,\n 223→ predicted_cpu=predicted,\n 224→ duration_hours=n_steps * 5 / 60,\n 225→ )\n 226→\n 227→\n 228→def scenario_diurnal_cycle(\n 229→ actual_full: np.ndarray,\n 230→ target_full: np.ndarray,\n 231→) -> Scenario:\n 232→ \"\"\"Scenario 4: Diurnal (24-hour) cycle.\n 233→\n 234→ Realistic day/night pattern: low at night, peak during business hours.\n 235→ Uses synthetic.patterns.generate_profile() scaled to CPU range.\n 236→ \"\"\"\n 237→ # Generate 48 hours (576 steps at 5-min intervals)\n 238→ n_steps = 576\n 239→ rng = np.random.RandomState(400)\n 240→\n 241→ # Create diurnal pattern manually (to avoid import issues)\n 242→ actual = np.zeros(n_steps)\n 243→ for i in range(n_steps):\n 244→ minute = (i * 5) % (24 * 60)\n 245→ hour = minute / 60\n 246→\n 247→ # Hourly factor based on real traffic patterns\n 248→ if 0 <= hour < 6:\n 249→ factor = 0.3 + 0.05 * math.sin(math.pi * hour / 6)\n 250→ elif 6 <= hour < 9:\n 251→ factor = 0.35 + 0.65 * ((hour - 6) / 3)\n 252→ elif 9 <= hour < 12:\n 253→ factor = 1.0\n 254→ elif 12 <= hour < 14:\n 255→ factor = 0.85 # Lunch dip\n 256→ elif 14 <= hour < 18:\n 257→ factor = 1.1 # Afternoon peak\n 258→ elif 18 <= hour < 20:\n 259→ factor = 0.8\n 260→ elif 20 <= hour < 22:\n 261→ factor = 0.7\n 262→ else:\n 263→ factor = 0.45\n 264→\n 265→ # Scale to CPU range (20-85%)\n 266→ cpu_base = 20 + factor * 65\n 267→ actual[i] = cpu_base\n 268→\n 269→ # Add noise\n 270→ noise = rng.normal(0, 4, size=n_steps)\n 271→ actual = np.clip(actual + noise, 10, 100)\n 272→\n 273→ # Predicted: ML sees ahead by 3 steps\n 274→ predicted = np.roll(actual, -3)\n 275→ predicted[-3:] = actual[-1]\n 276→ pred_noise = rng.normal(0, 2.5, size=n_steps)\n 277→ predicted = np.clip(predicted + pred_noise, 10, 100)\n 278→\n 279→ return Scenario(\n 280→ name=\"diurnal_cycle\",\n 281→ description=\"48-hour diurnal pattern (night low ~25%, business peak ~85%) \"\n 282→ \"testing ML's ability to anticipate daily traffic patterns.\",\n 283→ actual_cpu=actual,\n 284→ predicted_cpu=predicted,\n 285→ duration_hours=n_steps * 5 / 60,\n 286→ )\n 287→\n 288→\n 289→# ---------------------------------------------------------------------------\n 290→# Convenience: load all scenarios\n 291→# ---------------------------------------------------------------------------\n 292→\n 293→\n 294→def get_all_scenarios(\n 295→ test_csv_path: str | Path = \"data/processed/test.csv\",\n 296→ scaler_path: str | Path = \"data/processed/scaler.pkl\",\n 297→ metadata_path: str | Path = \"data/processed/metadata.json\",\n 298→) -> list[Scenario]:\n 299→ \"\"\"Load and return all 4 scenarios.\n 300→\n 301→ Args:\n 302→ test_csv_path: Path to test.csv (normalized data).\n 303→ scaler_path: Path to scaler.pkl (for inverse transform).\n 304→ metadata_path: Path to metadata.json.\n 305→\n 306→ Returns:\n 307→ List of 4 Scenario objects.\n 308→ \"\"\"\n 309→ actual_full, target_full, _, _ = _load_test_data(\n 310→ test_csv_path, scaler_path, metadata_path\n 311→ )\n 312→\n 313→ return [\n 314→ scenario_stable(actual_full, target_full),\n 315→ scenario_gradual_ramp(actual_full, target_full),\n 316→ scenario_spike_pattern(actual_full, target_full),\n 317→ scenario_diurnal_cycle(actual_full, target_full),\n 318→ ]\n 319→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n" | |
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| "filePath": "/tmp/gh-issue-solver-1770487990892/scripts/experiment_scenarios.py", | |
| "content": "\"\"\"Load scenarios for experiment simulation.\n\nGenerates 4 distinct workload patterns from real data and synthetic profiles,\neach producing (actual_cpu, predicted_cpu) trace pairs for the simulator.\n\nUsage:\n from scripts.experiment_scenarios import get_all_scenarios, Scenario\n scenarios = get_all_scenarios(\"data/processed/test.csv\", \"data/processed/scaler.pkl\")\n\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport math\nfrom dataclasses import dataclass\nfrom pathlib import Path\n\nimport joblib\nimport numpy as np\nimport pandas as pd\n\n\n@dataclass\nclass Scenario:\n \"\"\"A named experiment scenario with CPU traces.\"\"\"\n\n name: str\n description: str\n actual_cpu: np.ndarray # shape (N,) — actual CPU % (original scale)\n predicted_cpu: np.ndarray # shape (N,) — predicted CPU % (original scale)\n duration_hours: float\n\n @property\n def steps(self) -> int:\n return len(self.actual_cpu)\n\n\ndef _load_test_data(\n test_csv_path: str | Path,\n scaler_path: str | Path,\n metadata_path: str | Path | None = None,\n) -> tuple[np.ndarray, np.ndarray, float, float]:\n \"\"\"Load test.csv and inverse-transform CPU columns to original scale.\n\n Returns:\n (actual_cpu, target_cpu_t3, scaler_mean, scaler_scale)\n \"\"\"\n df = pd.read_csv(test_csv_path)\n scaler = joblib.load(scaler_path)\n\n # Determine CPU index in scaler\n if metadata_path is not None:\n with open(metadata_path) as f:\n meta = json.load(f)\n scaler_cols = meta[\"scaler_feature_columns\"]\n else:\n # Fallback: cpu_usage is always first\n scaler_cols = [\"cpu_usage\"]\n\n cpu_idx = scaler_cols.index(\"cpu_usage\")\n cpu_mean = scaler.mean_[cpu_idx]\n cpu_scale = scaler.scale_[cpu_idx]\n\n # Inverse transform: original = scaled * scale + mean\n actual = df[\"cpu_usage\"].values * cpu_scale + cpu_mean\n target = df[\"target_cpu_usage_t+3\"].values * cpu_scale + cpu_mean\n\n return actual, target, cpu_mean, cpu_scale\n\n\ndef _add_prediction_noise(\n actual: np.ndarray,\n target_t3: np.ndarray,\n noise_std: float = 2.0,\n seed: int = 42,\n) -> np.ndarray:\n \"\"\"Generate predicted CPU by shifting target_t+3 with small noise.\n\n The LSTM model achieves R²=0.89, so predictions are close to reality\n but not perfect. We simulate this by using target_t+3 (the actual\n future value) with small Gaussian noise added.\n \"\"\"\n rng = np.random.RandomState(seed)\n noise = rng.normal(0, noise_std, size=len(target_t3))\n predicted = target_t3 + noise\n return np.clip(predicted, 0, 100)\n\n\n# ---------------------------------------------------------------------------\n# Scenario generators\n# ---------------------------------------------------------------------------\n\n\ndef scenario_stable(\n actual_full: np.ndarray,\n target_full: np.ndarray,\n) -> Scenario:\n \"\"\"Scenario 1: Stable workload (~45-55% CPU).\n\n Select a segment with low variance from the real data.\n \"\"\"\n window = 50\n n = len(actual_full)\n best_start = 0\n best_var = float(\"inf\")\n\n # Find the most stable 300-step window\n target_len = 300\n for start in range(0, n - target_len, window):\n segment = actual_full[start : start + target_len]\n var = float(np.var(segment))\n if var < best_var:\n best_var = var\n best_start = start\n\n actual = actual_full[best_start : best_start + target_len].copy()\n predicted = _add_prediction_noise(\n actual, target_full[best_start : best_start + target_len], noise_std=1.5, seed=100\n )\n\n return Scenario(\n name=\"stable_workload\",\n description=\"Stable workload with low variance (~45-55% CPU). \"\n \"Tests steady-state behavior and resource efficiency.\",\n actual_cpu=actual,\n predicted_cpu=predicted,\n duration_hours=target_len * 5 / 60,\n )\n\n\ndef scenario_gradual_ramp(\n actual_full: np.ndarray,\n target_full: np.ndarray,\n) -> Scenario:\n \"\"\"Scenario 2: Gradual ramp from low to high load.\n\n Synthetic ramp: 30% → 85% over 400 steps with realistic noise.\n \"\"\"\n n_steps = 400\n rng = np.random.RandomState(200)\n\n # Create ramp\n t = np.linspace(0, 1, n_steps)\n base_ramp = 30 + 55 * t # 30% → 85%\n\n # Add realistic noise from actual data distribution\n noise = rng.normal(0, 3, size=n_steps)\n actual = np.clip(base_ramp + noise, 10, 100)\n\n # Predicted: ramp shifted forward by 3 steps (15 min lookahead)\n predicted_base = np.roll(base_ramp, -3)\n predicted_base[-3:] = base_ramp[-1] # Extend last value\n predicted_noise = rng.normal(0, 2.5, size=n_steps)\n predicted = np.clip(predicted_base + predicted_noise, 10, 100)\n\n return Scenario(\n name=\"gradual_ramp\",\n description=\"Gradual load increase from 30% to 85% CPU over ~33 hours. \"\n \"Tests proactive scaling during sustained growth.\",\n actual_cpu=actual,\n predicted_cpu=predicted,\n duration_hours=n_steps * 5 / 60,\n )\n\n\ndef scenario_spike_pattern(\n actual_full: np.ndarray,\n target_full: np.ndarray,\n) -> Scenario:\n \"\"\"Scenario 3: Spike pattern with sudden load increases.\n\n 3 spikes: 40% → 85% → 40%, each lasting ~30 steps.\n This is where ML prediction shines — it can anticipate spikes.\n \"\"\"\n n_steps = 350\n rng = np.random.RandomState(300)\n\n # Base: moderate load with 3 spikes\n actual = np.full(n_steps, 42.0)\n noise = rng.normal(0, 3, size=n_steps)\n\n # Spike 1: steps 50-80 (sharp)\n for i in range(50, 80):\n progress = (i - 50) / 30\n if progress < 0.2:\n actual[i] = 42 + (85 - 42) * (progress / 0.2) # Sharp rise\n elif progress < 0.7:\n actual[i] = 85 # Sustained peak\n else:\n actual[i] = 85 - (85 - 42) * ((progress - 0.7) / 0.3) # Gradual decline\n\n # Spike 2: steps 140-185 (gradual buildup)\n for i in range(130, 185):\n progress = (i - 130) / 55\n if progress < 0.4:\n actual[i] = 42 + (90 - 42) * (progress / 0.4)\n elif progress < 0.7:\n actual[i] = 90\n else:\n actual[i] = 90 - (90 - 42) * ((progress - 0.7) / 0.3)\n\n # Spike 3: steps 250-290 (double spike)\n for i in range(250, 270):\n progress = (i - 250) / 20\n actual[i] = 42 + (78 - 42) * math.sin(progress * math.pi)\n for i in range(275, 300):\n progress = (i - 275) / 25\n actual[i] = 42 + (92 - 42) * math.sin(progress * math.pi)\n\n actual = np.clip(actual + noise, 10, 100)\n\n # Predicted: ML model sees 3 steps ahead (the model's lookahead)\n predicted = np.roll(actual, -3)\n predicted[-3:] = actual[-1]\n pred_noise = rng.normal(0, 3.0, size=n_steps)\n predicted = np.clip(predicted + pred_noise, 10, 100)\n\n return Scenario(\n name=\"spike_pattern\",\n description=\"Three load spikes (42% → 85-92% → 42%) testing reactive vs \"\n \"predictive response to sudden demand changes.\",\n actual_cpu=actual,\n predicted_cpu=predicted,\n duration_hours=n_steps * 5 / 60,\n )\n\n\ndef scenario_diurnal_cycle(\n actual_full: np.ndarray,\n target_full: np.ndarray,\n) -> Scenario:\n \"\"\"Scenario 4: Diurnal (24-hour) cycle.\n\n Realistic day/night pattern: low at night, peak during business hours.\n Uses synthetic.patterns.generate_profile() scaled to CPU range.\n \"\"\"\n # Generate 48 hours (576 steps at 5-min intervals)\n n_steps = 576\n rng = np.random.RandomState(400)\n\n # Create diurnal pattern manually (to avoid import issues)\n actual = np.zeros(n_steps)\n for i in range(n_steps):\n minute = (i * 5) % (24 * 60)\n hour = minute / 60\n\n # Hourly factor based on real traffic patterns\n if 0 <= hour < 6:\n factor = 0.3 + 0.05 * math.sin(math.pi * hour / 6)\n elif 6 <= hour < 9:\n factor = 0.35 + 0.65 * ((hour - 6) / 3)\n elif 9 <= hour < 12:\n factor = 1.0\n elif 12 <= hour < 14:\n factor = 0.85 # Lunch dip\n elif 14 <= hour < 18:\n factor = 1.1 # Afternoon peak\n elif 18 <= hour < 20:\n factor = 0.8\n elif 20 <= hour < 22:\n factor = 0.7\n else:\n factor = 0.45\n\n # Scale to CPU range (20-85%)\n cpu_base = 20 + factor * 65\n actual[i] = cpu_base\n\n # Add noise\n noise = rng.normal(0, 4, size=n_steps)\n actual = np.clip(actual + noise, 10, 100)\n\n # Predicted: ML sees ahead by 3 steps\n predicted = np.roll(actual, -3)\n predicted[-3:] = actual[-1]\n pred_noise = rng.normal(0, 2.5, size=n_steps)\n predicted = np.clip(predicted + pred_noise, 10, 100)\n\n return Scenario(\n name=\"diurnal_cycle\",\n description=\"48-hour diurnal pattern (night low ~25%, business peak ~85%) \"\n \"testing ML's ability to anticipate daily traffic patterns.\",\n actual_cpu=actual,\n predicted_cpu=predicted,\n duration_hours=n_steps * 5 / 60,\n )\n\n\n# ---------------------------------------------------------------------------\n# Convenience: load all scenarios\n# ---------------------------------------------------------------------------\n\n\ndef get_all_scenarios(\n test_csv_path: str | Path = \"data/processed/test.csv\",\n scaler_path: str | Path = \"data/processed/scaler.pkl\",\n metadata_path: str | Path = \"data/processed/metadata.json\",\n) -> list[Scenario]:\n \"\"\"Load and return all 4 scenarios.\n\n Args:\n test_csv_path: Path to test.csv (normalized data).\n scaler_path: Path to scaler.pkl (for inverse transform).\n metadata_path: Path to metadata.json.\n\n Returns:\n List of 4 Scenario objects.\n \"\"\"\n actual_full, target_full, _, _ = _load_test_data(\n test_csv_path, scaler_path, metadata_path\n )\n\n return [\n scenario_stable(actual_full, target_full),\n scenario_gradual_ramp(actual_full, target_full),\n scenario_spike_pattern(actual_full, target_full),\n scenario_diurnal_cycle(actual_full, target_full),\n ]\n", | |
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| "content": " 1→# Contributing to K8s ML Predictive Autoscaling\n 2→\n 3→Привет! Рад, что ты заинтересовался этим проектом. Я ценю любой вклад — от исправления опечаток до реализации новых фич.\n 4→\n 5→Этот документ описывает, как я организую работу над проектом и что нужно знать, чтобы внести свой вклад.\n 6→\n 7→---\n 8→\n 9→## Оглавление\n 10→\n 11→- [Кодекс поведения](#кодекс-поведения)\n 12→- [Как я могу помочь?](#как-я-могу-помочь)\n 13→- [Процесс контрибуции](#процесс-контрибуции)\n 14→- [Стандарты кода](#стандарты-кода)\n 15→- [Коммиты и ветки](#коммиты-и-ветки)\n 16→- [Pull Request процесс](#pull-request-процесс)\n 17→- [Структура проекта](#структура-проекта)\n 18→- [Окружение для разработки](#окружение-для-разработки)\n 19→- [Тестирование](#тестирование)\n 20→- [Документация](#документация)\n 21→\n 22→---\n 23→\n 24→## Кодекс поведения\n 25→\n 26→Я стремлюсь создать открытое и дружелюбное сообщество. Я ожидаю:\n 27→\n 28→- Уважительного общения\n 29→- Конструктивной критики\n 30→- Фокуса на решении проблем, а не на личностях\n 31→- Приветствия новичков и помощи им\n 32→\n 33→Если у тебя возникли вопросы или проблемы с взаимодействием — напиши мне напрямую.\n 34→\n 35→---\n 36→\n 37→## Как я могу помочь?\n 38→\n 39→Вот несколько способов внести вклад:\n 40→\n 41→### 🐛 Нашел баг?\n 42→\n 43→1. Проверь, что баг еще не зарепортили в [Issues](https://github.com/VisageDvachevsky/k8s-ml-predictive-autoscaling/issues)\n 44→2. Если нет — создай новый Issue с описанием:\n 45→ - Что ожидал\n 46→ - Что произошло на самом деле\n 47→ - Шаги для воспроизведения\n 48→ - Твое окружение (OS, Python версия, K8s версия)\n 49→\n 50→### 💡 Есть идея для улучшения?\n 51→\n 52→1. Открой Issue с тегом `enhancement`\n 53→2. Опиши, какую проблему это решает\n 54→3. Предложи варианты реализации (если есть)\n 55→4. Я обсужу идею и дам feedback\n 56→\n 57→### 📝 Хочешь улучшить документацию?\n 58→\n 59→Документация всегда требует улучшений! Можешь:\n 60→- Исправить опечатки\n 61→- Добавить примеры использования\n 62→- Улучшить объяснения\n 63→- Перевести на другие языки\n 64→\n 65→### 🔬 Хочешь провести эксперименты?\n 66→\n 67→Если ты хочешь:\n 68→- Протестировать другие ML-модели\n 69→- Сравнить разные подходы\n 70→- Добавить новые метрики\n 71→\n 72→Создай Issue с описанием эксперимента — я с радостью обсужу.\n 73→\n 74→### 💻 Хочешь добавить код?\n 75→\n 76→Отлично! Перейди к разделу [Процесс контрибуции](#процесс-контрибуции).\n 77→\n 78→---\n 79→\n 80→## Процесс контрибуции\n 81→\n 82→### 1. Fork и клонирование\n 83→\n 84→```bash\n 85→# Форкни репозиторий через GitHub UI, затем:\n 86→git clone https://github.com/<твой-username>/k8s-ml-predictive-autoscaling.git\n 87→cd k8s-ml-predictive-autoscaling\n 88→\n 89→# Добавь upstream remote\n 90→git remote add upstream https://github.com/VisageDvachevsky/k8s-ml-predictive-autoscaling.git\n 91→```\n 92→\n 93→### 2. Создай ветку\n 94→\n 95→```bash\n 96→# Обнови main\n 97→git checkout main\n 98→git pull upstream main\n 99→\n 100→# Создай feature ветку\n 101→git checkout -b feature/твоя-фича\n 102→# или\n 103→git checkout -b fix/описание-бага\n 104→```\n 105→\n 106→### 3. Внеси изменения\n 107→\n 108→Пиши код, следуя [стандартам](#стандарты-кода).\n 109→\n 110→### 4. Закоммить изменения\n 111→\n 112→```bash\n 113→git add .\n 114→git commit -m \"feat: добавил предиктор на основе Prophet\"\n 115→```\n 116→\n 117→См. [Коммиты и ветки](#коммиты-и-ветки) для правил именования.\n 118→\n 119→### 5. Push и создай PR\n 120→\n 121→```bash\n 122→git push origin feature/твоя-фича\n 123→```\n 124→\n 125→Затем создай Pull Request на GitHub.\n 126→\n 127→---\n 128→\n 129→## Стандарты кода\n 130→\n 131→Я придерживаюсь следующих стандартов для поддержания качества кода.\n 132→\n 133→### Python\n 134→\n 135→#### Стиль кода\n 136→\n 137→Я использую:\n 138→- **PEP 8** как базовый стандарт\n 139→- **Black** для форматирования (line length = 100)\n 140→- **isort** для сортировки импортов\n 141→- **flake8** для линтинга\n 142→- **mypy** для type checking\n 143→\n 144→#### Установка pre-commit hooks\n 145→\n 146→```bash\n 147→pip install pre-commit\n 148→pre-commit install\n 149→```\n 150→\n 151→Hooks автоматически запустятся перед каждым коммитом.\n 152→\n 153→#### Ручной запуск проверок\n 154→\n 155→```bash\n 156→# Форматирование\n 157→black src/ models/ tests/\n 158→\n 159→# Сортировка импортов\n 160→isort src/ models/ tests/\n 161→\n 162→# Линтинг\n 163→flake8 src/ models/ tests/\n 164→\n 165→# Type checking\n 166→mypy src/ models/\n 167→```\n 168→\n 169→#### Type hints\n 170→\n 171→Я стараюсь использовать type hints везде, где это возможно:\n 172→\n 173→```python\n 174→def predict(data: np.ndarray, horizon: int = 5) -> Tuple[np.ndarray, np.ndarray]:\n 175→ \"\"\"\n 176→ Предсказать нагрузку на горизонте.\n 177→ \n 178→ Args:\n 179→ data: Входные данные (временной ряд)\n 180→ horizon: Горизонт прогноза в минутах\n 181→ \n 182→ Returns:\n 183→ Tuple из predictions и confidence_intervals\n 184→ \"\"\"\n 185→ ...\n 186→```\n 187→\n 188→#### Docstrings\n 189→\n 190→Я использую Google style docstrings:\n 191→\n 192→```python\n 193→def calculate_replicas(predicted_load: float, target_utilization: float) -> int:\n 194→ \"\"\"Рассчитать необходимое количество реплик.\n 195→ \n 196→ Args:\n 197→ predicted_load: Прогнозируемая нагрузка\n 198→ target_utilization: Целевая утилизация (0.0-1.0)\n 199→ \n 200→ Returns:\n 201→ Количество реплик\n 202→ \n 203→ Raises:\n 204→ ValueError: Если target_utilization вне диапазона [0, 1]\n 205→ \"\"\"\n 206→ ...\n 207→```\n 208→\n 209→### Конфигурационные файлы\n 210→\n 211→- **YAML** для конфигов (не JSON)\n 212→- **Отступы**: 2 пробела\n 213→- Комментарии для сложных секций\n 214→\n 215→```yaml\n 216→# Конфигурация Prophet модели\n 217→prophet:\n 218→ seasonality_mode: multiplicative # Мультипликативная сезонность для CPU metrics\n 219→ yearly_seasonality: false\n 220→ weekly_seasonality: true\n 221→ daily_seasonality: true\n 222→```\n 223→\n 224→### Kubernetes манифесты\n 225→\n 226→- Отступы: 2 пробела\n 227→- Используй `---` между объектами\n 228→- Добавляй labels и annotations\n 229→\n 230→```yaml\n 231→apiVersion: apps/v1\n 232→kind: Deployment\n 233→metadata:\n 234→ name: predictor\n 235→ labels:\n 236→ app: ml-autoscaler\n 237→ component: predictor\n 238→```\n 239→\n 240→---\n 241→\n 242→## Коммиты и ветки\n 243→\n 244→### Именование веток\n 245→\n 246→Я использую следующий формат:\n 247→\n 248→```\n 249→<type>/<short-description>\n 250→```\n 251→\n 252→**Типы**:\n 253→- `feature/` — новая функциональность\n 254→- `fix/` — исправление бага\n 255→- `docs/` — изменения в документации\n 256→- `refactor/` — рефакторинг кода\n 257→- `test/` — добавление тестов\n 258→- `chore/` — рутинные задачи (зависимости, CI/CD)\n 259→\n 260→**Примеры**:\n 261→```\n 262→feature/prophet-model\n 263→fix/prometheus-connection-timeout\n 264→docs/add-setup-guide\n 265→refactor/predictor-service\n 266→```\n 267→\n 268→### Формат коммитов\n 269→\n 270→Я следую [Conventional Commits](https://www.conventionalcommits.org/):\n 271→\n 272→```\n 273→<type>(<scope>): <subject>\n 274→\n 275→<body>\n 276→\n 277→<footer>\n 278→```\n 279→\n 280→**Типы**:\n 281→- `feat` — новая фича\n 282→- `fix` — исправление бага\n 283→- `docs` — документация\n 284→- `style` — форматирование (не влияет на код)\n 285→- `refactor` — рефакторинг\n 286→- `test` — тесты\n 287→- `chore` — инфраструктура, зависимости\n 288→\n 289→**Примеры**:\n 290→\n 291→```\n 292→feat(predictor): добавил LSTM модель для прогнозирования\n 293→\n 294→Реализовал LSTM архитектуру с 2 слоями и dropout.\n 295→Добавил обучение и экспорт в ONNX.\n 296→\n 297→Closes #42\n 298→```\n 299→\n 300→```\n 301→fix(planner): исправил расчет количества реплик\n 302→\n 303→Исправлена ошибка при округлении вверх (ceil).\n 304→Добавлен unit-тест для edge case.\n 305→```\n 306→\n 307→```\n 308→docs: обновил README с инструкциями по запуску\n 309→```\n 310→\n 311→### Советы по коммитам\n 312→\n 313→- Пиши коммиты на русском или английском (как тебе удобнее)\n 314→- Один коммит = одна логическая единица изменений\n 315→- Коммить часто, но не ломай build\n 316→- Пиши осмысленные сообщения (не \"fix\", \"update\", \"changes\")\n 317→\n 318→---\n 319→\n 320→## Pull Request процесс\n 321→\n 322→### Перед созданием PR\n 323→\n 324→1. **Убедись, что код работает**:\n 325→ ```bash\n 326→ # Запусти тесты\n 327→ pytest tests/\n 328→ \n 329→ # Запусти линтеры\n 330→ pre-commit run --all-files\n 331→ ```\n 332→\n 333→2. **Обнови свою ветку с main**:\n 334→ ```bash\n 335→ git fetch upstream\n 336→ git rebase upstream/main\n 337→ ```\n 338→\n 339→3. **Напиши/обнови тесты** для новой функциональности\n 340→\n 341→4. **Обнови документацию**, если нужно\n 342→\n 343→### Создание PR\n 344→\n 345→1. Пуш в свой fork:\n 346→ ```bash\n 347→ git push origin feature/твоя-фича\n 348→ ```\n 349→\n 350→2. Создай PR на GitHub\n 351→\n 352→3. Заполни template (будет добавлен позже):\n 353→ - Описание изменений\n 354→ - Связанные Issues (если есть)\n 355→ - Скриншоты (для UI изменений)\n 356→ - Чеклист\n 357→\n 358→### Пример описания PR\n 359→\n 360→```markdown\n 361→## Описание\n 362→Добавил Prophet модель для прогнозирования CPU usage.\n 363→\n 364→## Изменения\n 365→- Реализовал `models/prophet/train.py` и `models/prophet/evaluate.py`\n 366→- Добавил конфигурацию в `models/prophet/config.yaml`\n 367→- Написал unit-тесты в `tests/models/test_prophet.py`\n 368→- Обновил README с инструкциями\n 369→\n 370→## Связанные Issues\n 371→Closes #12\n 372→\n 373→## Тестирование\n 374→- [ ] Unit-тесты проходят\n 375→- [ ] Линтеры проходят\n 376→- [ ] Протестировал локально на синтетических данных\n 377→\n 378→## Скриншоты\n 379→\n 380→```\n 381→\n 382→### Review процесс\n 383→\n 384→Я постараюсь:\n 385→- Ответить на PR в течение 2-3 дней\n 386→- Дать конструктивный feedback\n 387→- Помочь с доработками, если нужно\n 388→\n 389→Если я прошу о изменениях:\n 390→1. Внеси правки в той же ветке\n 391→2. Push в свой fork\n 392→3. PR автоматически обновится\n 393→\n 394→После одобрения я смержу PR.\n 395→\n 396→---\n 397→\n 398→## Структура проекта\n 399→\n 400→Краткое описание основных директорий:\n 401→\n 402→```\n 403→k8s-ml-predictive-autoscaling/\n 404→├── models/ # ML-модели (Prophet, LSTM, гибрид, ONNX)\n 405→├── src/ # Исходный код сервисов\n 406→│ ├── collector/ # Сбор данных из Prometheus\n 407→│ ├── preprocessor/ # Препроцессинг данных\n 408→│ ├── predictor/ # FastAPI сервис инференса\n 409→│ ├── planner/ # Resource Planner\n 410→│ └── executor/ # Интеграция с K8s API\n 411→├── k8s/ # Kubernetes манифесты\n 412→├── data/ # Датасеты (не коммитятся)\n 413→├── notebooks/ # Jupyter notebooks для исследований\n 414→├── tests/ # Тесты\n 415→├── tools/ # Утилиты (load generator, эксперименты)\n 416→├── docs/ # Документация\n 417→└── dashboard/ # Grafana дашборды\n 418→```\n 419→\n 420→Подробнее в [README.md](README.md#6-структура-репозитория).\n 421→\n 422→---\n 423→\n 424→## Окружение для разработки\n 425→\n 426→### Требования\n 427→\n 428→- Python 3.11+\n 429→- Docker и Docker Compose\n 430→- kubectl\n 431→- kind или minikube\n 432→- (Опционально) GPU для обучения LSTM\n 433→\n 434→### Настройка окружения\n 435→\n 436→```bash\n 437→# Клонируй репозиторий\n 438→git clone https://github.com/VisageDvachevsky/k8s-ml-predictive-autoscaling.git\n 439→cd k8s-ml-predictive-autoscaling\n 440→\n 441→# Создай виртуальное окружение\n 442→python -m venv venv\n 443→source venv/bin/activate # Linux/Mac\n 444→# или\n 445→venv\\Scripts\\activate # Windows\n 446→\n 447→# Установи зависимости\n 448→pip install -r requirements.txt\n 449→pip install -r requirements-dev.txt # Для разработки (pytest, black, etc.)\n 450→\n 451→# Установи pre-commit hooks\n 452→pre-commit install\n 453→\n 454→# Подними Docker окружение (Prometheus, Grafana)\n 455→docker compose up -d\n 456→\n 457→# Подними Kubernetes кластер\n 458→kind create cluster --config k8s/kind-config.yaml\n 459→# или\n 460→minikube start\n 461→\n 462→# Задеплой демо-сервисы\n 463→kubectl apply -f k8s/manifests/\n 464→```\n 465→\n 466→### Проверка окружения\n 467→\n 468→```bash\n 469→# Проверь, что Prometheus доступен\n 470→curl http://localhost:9090/-/healthy\n 471→\n 472→# Проверь, что Grafana доступен\n 473→open http://localhost:3000 # login: admin, password: admin\n 474→\n 475→# Проверь, что K8s кластер работает\n 476→kubectl get nodes\n 477→kubectl get pods -A\n 478→```\n 479→\n 480→---\n 481→\n 482→## Тестирование\n 483→\n 484→Я использую **pytest** для тестирования.\n 485→\n 486→### Структура тестов\n 487→\n 488→```\n 489→tests/\n 490→├── unit/ # Unit-тесты\n 491→│ ├── test_prophet.py\n 492→│ ├── test_lstm.py\n 493→│ └── test_planner.py\n 494→├── integration/ # Integration тесты\n 495→│ ├── test_predictor_service.py\n 496→│ └── test_k8s_integration.py\n 497→└── fixtures/ # Фикстуры и моки\n 498→ └── sample_data.py\n 499→```\n 500→\n 501→### Запуск тестов\n 502→\n 503→```bash\n 504→# Все тесты\n 505→pytest\n 506→\n 507→# С покрытием\n 508→pytest --cov=src --cov=models\n 509→\n 510→# Только unit-тесты\n 511→pytest tests/unit/\n 512→\n 513→# Конкретный файл\n 514→pytest tests/unit/test_prophet.py\n 515→\n 516→# С verbose\n 517→pytest -v\n 518→\n 519→# С логами\n 520→pytest -s\n 521→```\n 522→\n 523→### Написание тестов\n 524→\n 525→#### Unit-тест пример\n 526→\n 527→```python\n 528→import pytest\n 529→from src.planner.resource_calculator import calculate_replicas\n 530→\n 531→\n 532→def test_calculate_replicas_normal_case():\n 533→ \"\"\"Тест расчета реплик в нормальном случае.\"\"\"\n 534→ predicted_load = 80.0\n 535→ target_utilization = 0.7\n 536→ capacity_per_pod = 100.0\n 537→ \n 538→ replicas = calculate_replicas(predicted_load, target_utilization, capacity_per_pod)\n 539→ \n 540→ assert replicas == 2 # ceil(80 / (0.7 * 100)) = ceil(1.14) = 2\n 541→\n 542→\n 543→def test_calculate_replicas_edge_case():\n 544→ \"\"\"Тест edge case с нулевой нагрузкой.\"\"\"\n 545→ replicas = calculate_replicas(0.0, 0.7, 100.0)\n 546→ assert replicas == 1 # Минимум 1 реплика\n 547→```\n 548→\n 549→#### Integration тест пример\n 550→\n 551→```python\n 552→import pytest\n 553→from fastapi.testclient import TestClient\n 554→from src.predictor.app import app\n 555→\n 556→\n 557→@pytest.fixture\n 558→def client():\n 559→ return TestClient(app)\n 560→\n 561→\n 562→def test_predict_endpoint(client):\n 563→ \"\"\"Тест эндпоинта /predict.\"\"\"\n 564→ response = client.post(\n 565→ \"/predict\",\n 566→ json={\n 567→ \"service_name\": \"demo-service\",\n 568→ \"horizon_minutes\": 15,\n 569→ \"model_type\": \"prophet\"\n 570→ }\n 571→ )\n 572→ \n 573→ assert response.status_code == 200\n 574→ data = response.json()\n 575→ assert \"predictions\" in data\n 576→ assert len(data[\"predictions\"]) > 0\n 577→```\n 578→\n 579→### Минимальные требования\n 580→\n 581→Для принятия PR:\n 582→- Все тесты должны проходить\n 583→- Покрытие новых изменений >= 80%\n 584→- Линтеры должны проходить без ошибок\n 585→\n 586→---\n 587→\n 588→## Документация\n 589→\n 590→### Что документировать\n 591→\n 592→- **README.md** — общий обзор проекта\n 593→- **ROADMAP.md** — план развития\n 594→- **docs/** — подробная документация:\n 595→ - `setup-guide.md` — настройка окружения\n 596→ - `architecture.md` — архитектура системы\n 597→ - `experiments.md` — методология экспериментов\n 598→ - `api-reference.md` — API документация\n 599→\n 600→### Обновление документации\n 601→\n 602→Если ты добавил новую функциональность или изменил API:\n 603→1. Обнови соответствующий `.md` файл\n 604→2. Добавь примеры использования\n 605→3. Обнови docstrings в коде\n 606→\n 607→### Документация API\n 608→\n 609→Я использую **OpenAPI (Swagger)** для FastAPI сервисов. Документация генерируется автоматически:\n 610→\n 611→```\n 612→http://localhost:8000/docs # Swagger UI\n 613→http://localhost:8000/redoc # ReDoc\n 614→```\n 615→\n 616→---\n 617→\n 618→## Вопросы?\n 619→\n 620→Если у тебя есть вопросы:\n 621→\n 622→1. **Проверь документацию** в `docs/`\n 623→2. **Поищи в Issues** — возможно, кто-то уже спрашивал\n 624→3. **Создай Issue** с тегом `question`\n 625→4. **Напиши мне** напрямую (если совсем срочно)\n 626→\n 627→---\n 628→\n 629→## Лицензия\n 630→\n 631→Внося вклад в этот проект, ты соглашаешься, что твой код будет лицензирован под [MIT License](LICENSE).\n 632→\n 633→---\n 634→\n 635→## Благодарности\n 636→\n 637→Спасибо за интерес к проекту! 🚀\n 638→\n 639→Я особенно ценю:\n 640→- Первые контрибуции (welcome, начинающие!)\n 641→- Улучшения документации\n 642→- Подробные bug reports\n 643→- Конструктивные обсуждения в Issues\n 644→\n 645→Каждый вклад, большой или маленький, помогает проекту стать лучше.\n 646→\n 647→---\n 648→\n 649→**Автор**: Visage Dvachevsky \n 650→**GitHub**: [@VisageDvachevsky](https://github.com/VisageDvachevsky) \n 651→**Последнее обновление**: Ноябрь 2024\n 652→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n" | |
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| "content": "# Contributing to K8s ML Predictive Autoscaling\n\nПривет! Рад, что ты заинтересовался этим проектом. Я ценю любой вклад — от исправления опечаток до реализации новых фич.\n\nЭтот документ описывает, как я организую работу над проектом и что нужно знать, чтобы внести свой вклад.\n\n---\n\n## Оглавление\n\n- [Кодекс поведения](#кодекс-поведения)\n- [Как я могу помочь?](#как-я-могу-помочь)\n- [Процесс контрибуции](#процесс-контрибуции)\n- [Стандарты кода](#стандарты-кода)\n- [Коммиты и ветки](#коммиты-и-ветки)\n- [Pull Request процесс](#pull-request-процесс)\n- [Структура проекта](#структура-проекта)\n- [Окружение для разработки](#окружение-для-разработки)\n- [Тестирование](#тестирование)\n- [Документация](#документация)\n\n---\n\n## Кодекс поведения\n\nЯ стремлюсь создать открытое и дружелюбное сообщество. Я ожидаю:\n\n- Уважительного общения\n- Конструктивной критики\n- Фокуса на решении проблем, а не на личностях\n- Приветствия новичков и помощи им\n\nЕсли у тебя возникли вопросы или проблемы с взаимодействием — напиши мне напрямую.\n\n---\n\n## Как я могу помочь?\n\nВот несколько способов внести вклад:\n\n### 🐛 Нашел баг?\n\n1. Проверь, что баг еще не зарепортили в [Issues](https://github.com/VisageDvachevsky/k8s-ml-predictive-autoscaling/issues)\n2. Если нет — создай новый Issue с описанием:\n - Что ожидал\n - Что произошло на самом деле\n - Шаги для воспроизведения\n - Твое окружение (OS, Python версия, K8s версия)\n\n### 💡 Есть идея для улучшения?\n\n1. Открой Issue с тегом `enhancement`\n2. Опиши, какую проблему это решает\n3. Предложи варианты реализации (если есть)\n4. Я обсужу идею и дам feedback\n\n### 📝 Хочешь улучшить документацию?\n\nДокументация всегда требует улучшений! Можешь:\n- Исправить опечатки\n- Добавить примеры использования\n- Улучшить объяснения\n- Перевести на другие языки\n\n### 🔬 Хочешь провести эксперименты?\n\nЕсли ты хочешь:\n- Протестировать другие ML-модели\n- Сравнить разные подходы\n- Добавить новые метрики\n\nСоздай Issue с описанием эксперимента — я с радостью обсужу.\n\n### 💻 Хочешь добавить код?\n\nОтлично! Перейди к разделу [Процесс контрибуции](#процесс-контрибуции).\n\n---\n\n## Процесс контрибуции\n\n### 1. Fork и клонирование\n\n```bash\n# Форкни репозиторий через GitHub UI, затем:\ngit clone https://github.com/<твой-username>/k8s-ml-predictive-autoscaling.git\ncd k8s-ml-predictive-autoscaling\n\n# Добавь upstream remote\ngit remote add upstream https://github.com/VisageDvachevsky/k8s-ml-predictive-autoscaling.git\n```\n\n### 2. Создай ветку\n\n```bash\n# Обнови main\ngit checkout main\ngit pull upstream main\n\n# Создай feature ветку\ngit checkout -b feature/твоя-фича\n# или\ngit checkout -b fix/описание-бага\n```\n\n### 3. Внеси изменения\n\nПиши код, следуя [стандартам](#стандарты-кода).\n\n### 4. Закоммить изменения\n\n```bash\ngit add .\ngit commit -m \"feat: добавил предиктор на основе Prophet\"\n```\n\nСм. [Коммиты и ветки](#коммиты-и-ветки) для правил именования.\n\n### 5. Push и создай PR\n\n```bash\ngit push origin feature/твоя-фича\n```\n\nЗатем создай Pull Request на GitHub.\n\n---\n\n## Стандарты кода\n\nЯ придерживаюсь следующих стандартов для поддержания качества кода.\n\n### Python\n\n#### Стиль кода\n\nЯ использую:\n- **PEP 8** как базовый стандарт\n- **Black** для форматирования (line length = 100)\n- **isort** для сортировки импортов\n- **flake8** для линтинга\n- **mypy** для type checking\n\n#### Установка pre-commit hooks\n\n```bash\npip install pre-commit\npre-commit install\n```\n\nHooks автоматически запустятся перед каждым коммитом.\n\n#### Ручной запуск проверок\n\n```bash\n# Форматирование\nblack src/ models/ tests/\n\n# Сортировка импортов\nisort src/ models/ tests/\n\n# Линтинг\nflake8 src/ models/ tests/\n\n# Type checking\nmypy src/ models/\n```\n\n#### Type hints\n\nЯ стараюсь использовать type hints везде, где это возможно:\n\n```python\ndef predict(data: np.ndarray, horizon: int = 5) -> Tuple[np.ndarray, np.ndarray]:\n \"\"\"\n Предсказать нагрузку на горизонте.\n \n Args:\n data: Входные данные (временной ряд)\n horizon: Горизонт прогноза в минутах\n \n Returns:\n Tuple из predictions и confidence_intervals\n \"\"\"\n ...\n```\n\n#### Docstrings\n\nЯ использую Google style docstrings:\n\n```python\ndef calculate_replicas(predicted_load: float, target_utilization: float) -> int:\n \"\"\"Рассчитать необходимое количество реплик.\n \n Args:\n predicted_load: Прогнозируемая нагрузка\n target_utilization: Целевая утилизация (0.0-1.0)\n \n Returns:\n Количество реплик\n \n Raises:\n ValueError: Если target_utilization вне диапазона [0, 1]\n \"\"\"\n ...\n```\n\n### Конфигурационные файлы\n\n- **YAML** для конфигов (не JSON)\n- **Отступы**: 2 пробела\n- Комментарии для сложных секций\n\n```yaml\n# Конфигурация Prophet модели\nprophet:\n seasonality_mode: multiplicative # Мультипликативная сезонность для CPU metrics\n yearly_seasonality: false\n weekly_seasonality: true\n daily_seasonality: true\n```\n\n### Kubernetes манифесты\n\n- Отступы: 2 пробела\n- Используй `---` между объектами\n- Добавляй labels и annotations\n\n```yaml\napiVersion: apps/v1\nkind: Deployment\nmetadata:\n name: predictor\n labels:\n app: ml-autoscaler\n component: predictor\n```\n\n---\n\n## Коммиты и ветки\n\n### Именование веток\n\nЯ использую следующий формат:\n\n```\n<type>/<short-description>\n```\n\n**Типы**:\n- `feature/` — новая функциональность\n- `fix/` — исправление бага\n- `docs/` — изменения в документации\n- `refactor/` — рефакторинг кода\n- `test/` — добавление тестов\n- `chore/` — рутинные задачи (зависимости, CI/CD)\n\n**Примеры**:\n```\nfeature/prophet-model\nfix/prometheus-connection-timeout\ndocs/add-setup-guide\nrefactor/predictor-service\n```\n\n### Формат коммитов\n\nЯ следую [Conventional Commits](https://www.conventionalcommits.org/):\n\n```\n<type>(<scope>): <subject>\n\n<body>\n\n<footer>\n```\n\n**Типы**:\n- `feat` — новая фича\n- `fix` — исправление бага\n- `docs` — документация\n- `style` — форматирование (не влияет на код)\n- `refactor` — рефакторинг\n- `test` — тесты\n- `chore` — инфраструктура, зависимости\n\n**Примеры**:\n\n```\nfeat(predictor): добавил LSTM модель для прогнозирования\n\nРеализовал LSTM архитектуру с 2 слоями и dropout.\nДобавил обучение и экспорт в ONNX.\n\nCloses #42\n```\n\n```\nfix(planner): исправил расчет количества реплик\n\nИсправлена ошибка при округлении вверх (ceil).\nДобавлен unit-тест для edge case.\n```\n\n```\ndocs: обновил README с инструкциями по запуску\n```\n\n### Советы по коммитам\n\n- Пиши коммиты на русском или английском (как тебе удобнее)\n- Один коммит = одна логическая единица изменений\n- Коммить часто, но не ломай build\n- Пиши осмысленные сообщения (не \"fix\", \"update\", \"changes\")\n\n---\n\n## Pull Request процесс\n\n### Перед созданием PR\n\n1. **Убедись, что код работает**:\n ```bash\n # Запусти тесты\n pytest tests/\n \n # Запусти линтеры\n pre-commit run --all-files\n ```\n\n2. **Обнови свою ветку с main**:\n ```bash\n git fetch upstream\n git rebase upstream/main\n ```\n\n3. **Напиши/обнови тесты** для новой функциональности\n\n4. **Обнови документацию**, если нужно\n\n### Создание PR\n\n1. Пуш в свой fork:\n ```bash\n git push origin feature/твоя-фича\n ```\n\n2. Создай PR на GitHub\n\n3. Заполни template (будет добавлен позже):\n - Описание изменений\n - Связанные Issues (если есть)\n - Скриншоты (для UI изменений)\n - Чеклист\n\n### Пример описания PR\n\n```markdown\n## Описание\nДобавил Prophet модель для прогнозирования CPU usage.\n\n## Изменения\n- Реализовал `models/prophet/train.py` и `models/prophet/evaluate.py`\n- Добавил конфигурацию в `models/prophet/config.yaml`\n- Написал unit-тесты в `tests/models/test_prophet.py`\n- Обновил README с инструкциями\n\n## Связанные Issues\nCloses #12\n\n## Тестирование\n- [ ] Unit-тесты проходят\n- [ ] Линтеры проходят\n- [ ] Протестировал локально на синтетических данных\n\n## Скриншоты\n\n```\n\n### Review процесс\n\nЯ постараюсь:\n- Ответить на PR в течение 2-3 дней\n- Дать конструктивный feedback\n- Помочь с доработками, если нужно\n\nЕсли я прошу о изменениях:\n1. Внеси правки в той же ветке\n2. Push в свой fork\n3. PR автоматически обновится\n\nПосле одобрения я смержу PR.\n\n---\n\n## Структура проекта\n\nКраткое описание основных директорий:\n\n```\nk8s-ml-predictive-autoscaling/\n├── models/ # ML-модели (Prophet, LSTM, гибрид, ONNX)\n├── src/ # Исходный код сервисов\n│ ├── collector/ # Сбор данных из Prometheus\n│ ├── preprocessor/ # Препроцессинг данных\n│ ├── predictor/ # FastAPI сервис инференса\n│ ├── planner/ # Resource Planner\n│ └── executor/ # Интеграция с K8s API\n├── k8s/ # Kubernetes манифесты\n├── data/ # Датасеты (не коммитятся)\n├── notebooks/ # Jupyter notebooks для исследований\n├── tests/ # Тесты\n├── tools/ # Утилиты (load generator, эксперименты)\n├── docs/ # Документация\n└── dashboard/ # Grafana дашборды\n```\n\nПодробнее в [README.md](README.md#6-структура-репозитория).\n\n---\n\n## Окружение для разработки\n\n### Требования\n\n- Python 3.11+\n- Docker и Docker Compose\n- kubectl\n- kind или minikube\n- (Опционально) GPU для обучения LSTM\n\n### Настройка окружения\n\n```bash\n# Клонируй репозиторий\ngit clone https://github.com/VisageDvachevsky/k8s-ml-predictive-autoscaling.git\ncd k8s-ml-predictive-autoscaling\n\n# Создай виртуальное окружение\npython -m venv venv\nsource venv/bin/activate # Linux/Mac\n# или\nvenv\\Scripts\\activate # Windows\n\n# Установи зависимости\npip install -r requirements.txt\npip install -r requirements-dev.txt # Для разработки (pytest, black, etc.)\n\n# Установи pre-commit hooks\npre-commit install\n\n# Подними Docker окружение (Prometheus, Grafana)\ndocker compose up -d\n\n# Подними Kubernetes кластер\nkind create cluster --config k8s/kind-config.yaml\n# или\nminikube start\n\n# Задеплой демо-сервисы\nkubectl apply -f k8s/manifests/\n```\n\n### Проверка окружения\n\n```bash\n# Проверь, что Prometheus доступен\ncurl http://localhost:9090/-/healthy\n\n# Проверь, что Grafana доступен\nopen http://localhost:3000 # login: admin, password: admin\n\n# Проверь, что K8s кластер работает\nkubectl get nodes\nkubectl get pods -A\n```\n\n---\n\n## Тестирование\n\nЯ использую **pytest** для тестирования.\n\n### Структура тестов\n\n```\ntests/\n├── unit/ # Unit-тесты\n│ ├── test_prophet.py\n│ ├── test_lstm.py\n│ └── test_planner.py\n├── integration/ # Integration тесты\n│ ├── test_predictor_service.py\n│ └── test_k8s_integration.py\n└── fixtures/ # Фикстуры и моки\n └── sample_data.py\n```\n\n### Запуск тестов\n\n```bash\n# Все тесты\npytest\n\n# С покрытием\npytest --cov=src --cov=models\n\n# Только unit-тесты\npytest tests/unit/\n\n# Конкретный файл\npytest tests/unit/test_prophet.py\n\n# С verbose\npytest -v\n\n# С логами\npytest -s\n```\n\n### Написание тестов\n\n#### Unit-тест пример\n\n```python\nimport pytest\nfrom src.planner.resource_calculator import calculate_replicas\n\n\ndef test_calculate_replicas_normal_case():\n \"\"\"Тест расчета реплик в нормальном случае.\"\"\"\n predicted_load = 80.0\n target_utilization = 0.7\n capacity_per_pod = 100.0\n \n replicas = calculate_replicas(predicted_load, target_utilization, capacity_per_pod)\n \n assert replicas == 2 # ceil(80 / (0.7 * 100)) = ceil(1.14) = 2\n\n\ndef test_calculate_replicas_edge_case():\n \"\"\"Тест edge case с нулевой нагрузкой.\"\"\"\n replicas = calculate_replicas(0.0, 0.7, 100.0)\n assert replicas == 1 # Минимум 1 реплика\n```\n\n#### Integration тест пример\n\n```python\nimport pytest\nfrom fastapi.testclient import TestClient\nfrom src.predictor.app import app\n\n\n@pytest.fixture\ndef client():\n return TestClient(app)\n\n\ndef test_predict_endpoint(client):\n \"\"\"Тест эндпоинта /predict.\"\"\"\n response = client.post(\n \"/predict\",\n json={\n \"service_name\": \"demo-service\",\n \"horizon_minutes\": 15,\n \"model_type\": \"prophet\"\n }\n )\n \n assert response.status_code == 200\n data = response.json()\n assert \"predictions\" in data\n assert len(data[\"predictions\"]) > 0\n```\n\n### Минимальные требования\n\nДля принятия PR:\n- Все тесты должны проходить\n- Покрытие новых изменений >= 80%\n- Линтеры должны проходить без ошибок\n\n---\n\n## Документация\n\n### Что документировать\n\n- **README.md** — общий обзор проекта\n- **ROADMAP.md** — план развития\n- **docs/** — подробная документация:\n - `setup-guide.md` — настройка окружения\n - `architecture.md` — архитектура системы\n - `experiments.md` — методология экспериментов\n - `api-reference.md` — API документация\n\n### Обновление документации\n\nЕсли ты добавил новую функциональность или изменил API:\n1. Обнови соответствующий `.md` файл\n2. Добавь примеры использования\n3. Обнови docstrings в коде\n\n### Документация API\n\nЯ использую **OpenAPI (Swagger)** для FastAPI сервисов. Документация генерируется автоматически:\n\n```\nhttp://localhost:8000/docs # Swagger UI\nhttp://localhost:8000/redoc # ReDoc\n```\n\n---\n\n## Вопросы?\n\nЕсли у тебя есть вопросы:\n\n1. **Проверь документацию** в `docs/`\n2. **Поищи в Issues** — возможно, кто-то уже спрашивал\n3. **Создай Issue** с тегом `question`\n4. **Напиши мне** напрямую (если совсем срочно)\n\n---\n\n## Лицензия\n\nВнося вклад в этот проект, ты соглашаешься, что твой код будет лицензирован под [MIT License](LICENSE).\n\n---\n\n## Благодарности\n\nСпасибо за интерес к проекту! 🚀\n\nЯ особенно ценю:\n- Первые контрибуции (welcome, начинающие!)\n- Улучшения документации\n- Подробные bug reports\n- Конструктивные обсуждения в Issues\n\nКаждый вклад, большой или маленький, помогает проекту стать лучше.\n\n---\n\n**Автор**: Visage Dvachevsky \n**GitHub**: [@VisageDvachevsky](https://github.com/VisageDvachevsky) \n**Последнее обновление**: Ноябрь 2024\n", | |
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| "content": " 1→[flake8]\n 2→max-line-length = 100\n 3→extend-ignore = E203, W503\n 4→per-file-ignores =\n 5→ tests/*: S101\n 6→jobs = 1\n 7→exclude =\n 8→ .git,\n 9→ __pycache__,\n 10→ .venv,\n 11→ build,\n 12→ dist\n 13→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n" | |
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| "content": " 1→\"\"\"ML Autoscaler — Experiment Dashboard (CustomTkinter).\n 2→\n 3→Interactive visualization of:\n 4→ - Neural network internals (buffer, features, ONNX inference)\n 5→ - Scaling algorithm 9-step pipeline\n 6→ - HPA vs ML autoscaler comparison\n 7→\n 8→Usage:\n 9→ python scripts/experiment_gui.py\n 10→\"\"\"\n 11→\n 12→from __future__ import annotations\n 13→\n 14→import math\n 15→import sys\n 16→from pathlib import Path\n 17→\n 18→import customtkinter as ctk\n 19→import matplotlib\n 20→import numpy as np\n 21→\n 22→matplotlib.use(\"TkAgg\")\n 23→import matplotlib.pyplot as plt\n 24→from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\n 25→from matplotlib.figure import Figure\n 26→\n 27→# Add project root to path for imports\n 28→ROOT = Path(__file__).resolve().parent.parent\n 29→sys.path.insert(0, str(ROOT / \"src\"))\n 30→sys.path.insert(0, str(ROOT))\n 31→\n 32→from scripts.experiment_scenarios import get_all_scenarios, Scenario\n 33→from scripts.experiment_simulator import (\n 34→ ExperimentRunner,\n 35→ HPAConfig,\n 36→ MLAutoscalerSimulator,\n 37→ MLConfig,\n 38→ StepRecord,\n 39→ compute_metrics,\n 40→)\n 41→\n 42→# ---------------------------------------------------------------------------\n 43→# Color palette (unified with Grafana dashboards)\n 44→# ---------------------------------------------------------------------------\n 45→\n 46→C = {\n 47→ \"bg\": \"#1a1a2e\",\n 48→ \"bg_card\": \"#16213e\",\n 49→ \"sidebar\": \"#0f3460\",\n 50→ \"accent\": \"#e94560\",\n 51→ \"green\": \"#73BF69\",\n 52→ \"yellow\": \"#FADE2A\",\n 53→ \"orange\": \"#FF9830\",\n 54→ \"red\": \"#F2495C\",\n 55→ \"blue\": \"#5794F2\",\n 56→ \"purple\": \"#B877D9\",\n 57→ \"text\": \"#e0e0e0\",\n 58→ \"text_dim\": \"#8892b0\",\n 59→ \"grid\": \"#2a2a4a\",\n 60→}\n 61→\n 62→# Matplotlib dark style\n 63→plt.rcParams.update({\n 64→ \"figure.facecolor\": C[\"bg_card\"],\n 65→ \"axes.facecolor\": C[\"bg\"],\n 66→ \"axes.edgecolor\": C[\"grid\"],\n 67→ \"axes.labelcolor\": C[\"text\"],\n 68→ \"text.color\": C[\"text\"],\n 69→ \"xtick.color\": C[\"text_dim\"],\n 70→ \"ytick.color\": C[\"text_dim\"],\n 71→ \"grid.color\": C[\"grid\"],\n 72→ \"grid.alpha\": 0.3,\n 73→ \"font.size\": 9,\n 74→ \"legend.facecolor\": C[\"bg_card\"],\n 75→ \"legend.edgecolor\": C[\"grid\"],\n 76→})\n 77→\n 78→\n 79→# ---------------------------------------------------------------------------\n 80→# Utility: embed a matplotlib figure in a CTkFrame\n 81→# ---------------------------------------------------------------------------\n 82→\n 83→\n 84→def embed_figure(parent: ctk.CTkFrame, fig: Figure) -> FigureCanvasTkAgg:\n 85→ canvas = FigureCanvasTkAgg(fig, master=parent)\n 86→ canvas.draw()\n 87→ canvas.get_tk_widget().pack(fill=\"both\", expand=True)\n 88→ return canvas\n 89→\n 90→\n 91→# ---------------------------------------------------------------------------\n 92→# KPI Card widget\n 93→# ---------------------------------------------------------------------------\n 94→\n 95→\n 96→class KPICard(ctk.CTkFrame):\n 97→ \"\"\"Compact KPI card: title + HPA value + ML value.\"\"\"\n 98→\n 99→ def __init__(self, master, title: str, **kw):\n 100→ super().__init__(master, fg_color=C[\"bg_card\"], corner_radius=8, **kw)\n 101→ self._title_lbl = ctk.CTkLabel(\n 102→ self, text=title, font=(\"Consolas\", 11), text_color=C[\"text_dim\"]\n 103→ )\n 104→ self._title_lbl.pack(pady=(6, 2))\n 105→\n 106→ row = ctk.CTkFrame(self, fg_color=\"transparent\")\n 107→ row.pack(pady=(0, 6))\n 108→ self._hpa_lbl = ctk.CTkLabel(\n 109→ row, text=\"HPA: --\", font=(\"Consolas\", 13, \"bold\"), text_color=C[\"red\"]\n 110→ )\n 111→ self._hpa_lbl.pack(side=\"left\", padx=6)\n 112→ self._ml_lbl = ctk.CTkLabel(\n 113→ row, text=\"ML: --\", font=(\"Consolas\", 13, \"bold\"), text_color=C[\"blue\"]\n 114→ )\n 115→ self._ml_lbl.pack(side=\"left\", padx=6)\n 116→\n 117→ def set_values(self, hpa_val: str, ml_val: str):\n 118→ self._hpa_lbl.configure(text=f\"HPA: {hpa_val}\")\n 119→ self._ml_lbl.configure(text=f\"ML: {ml_val}\")\n 120→\n 121→\n 122→# ---------------------------------------------------------------------------\n 123→# Pipeline Step block\n 124→# ---------------------------------------------------------------------------\n 125→\n 126→\n 127→class PipelineBlock(ctk.CTkFrame):\n 128→ \"\"\"One step in the 9-step scaling algorithm visualization.\"\"\"\n 129→\n 130→ def __init__(self, master, step_num: int, title: str, **kw):\n 131→ super().__init__(master, fg_color=C[\"bg_card\"], corner_radius=6,\n 132→ border_width=1, border_color=C[\"grid\"], **kw)\n 133→ self.configure(width=115, height=80)\n 134→ self.pack_propagate(False)\n 135→ ctk.CTkLabel(\n 136→ self, text=f\"Step {step_num}\", font=(\"Consolas\", 8),\n 137→ text_color=C[\"text_dim\"]\n 138→ ).pack(pady=(4, 0))\n 139→ ctk.CTkLabel(\n 140→ self, text=title, font=(\"Consolas\", 9, \"bold\"),\n 141→ text_color=C[\"text\"]\n 142→ ).pack()\n 143→ self._value_lbl = ctk.CTkLabel(\n 144→ self, text=\"—\", font=(\"Consolas\", 10), text_color=C[\"text_dim\"],\n 145→ wraplength=100\n 146→ )\n 147→ self._value_lbl.pack(pady=(2, 4))\n 148→\n 149→ def update(self, text: str, status: str = \"active\"):\n 150→ colors = {\n 151→ \"active\": C[\"green\"],\n 152→ \"blocked\": C[\"yellow\"],\n 153→ \"skipped\": C[\"text_dim\"],\n 154→ \"executed\": C[\"accent\"],\n 155→ }\n 156→ self._value_lbl.configure(text=text, text_color=colors.get(status, C[\"text\"]))\n 157→ border = {\n 158→ \"active\": C[\"green\"],\n 159→ \"blocked\": C[\"yellow\"],\n 160→ \"executed\": C[\"accent\"],\n 161→ }\n 162→ self.configure(border_color=border.get(status, C[\"grid\"]))\n 163→\n 164→\n 165→# ---------------------------------------------------------------------------\n 166→# Main Application\n 167→# ---------------------------------------------------------------------------\n 168→\n 169→\n 170→class ExperimentApp(ctk.CTk):\n 171→ def __init__(self):\n 172→ super().__init__()\n 173→ self.title(\"ML Autoscaler — Experiment Dashboard\")\n 174→ self.geometry(\"1440x920\")\n 175→ ctk.set_appearance_mode(\"dark\")\n 176→\n 177→ # State\n 178→ self._scenarios: list[Scenario] = []\n 179→ self._current_scenario_idx = 0\n 180→ self._step = 0\n 181→ self._playing = False\n 182→ self._speed_ms = 200\n 183→ self._history: list[StepRecord] = []\n 184→\n 185→ # ML simulator state for step-by-step\n 186→ self._ml_sim: MLAutoscalerSimulator | None = None\n 187→ self._hpa_replicas_trace: list[int] = []\n 188→ self._ml_replicas_trace: list[int] = []\n 189→ self._actual_trace: list[float] = []\n 190→ self._predicted_trace: list[float] = []\n 191→ self._smoothed_trace: list[float] = []\n 192→ self._decisions_log: list[str] = []\n 193→\n 194→ # Build UI\n 195→ self._build_controls()\n 196→ self._build_sidebar()\n 197→ self._build_tabview()\n 198→\n 199→ # Load data\n 200→ self._load_scenarios()\n 201→\n 202→ # -------------------------------------------------------------------\n 203→ # Data loading\n 204→ # -------------------------------------------------------------------\n 205→\n 206→ def _load_scenarios(self):\n 207→ try:\n 208→ self._scenarios = get_all_scenarios(\n 209→ ROOT / \"data\" / \"processed\" / \"test.csv\",\n 210→ ROOT / \"data\" / \"processed\" / \"scaler.pkl\",\n 211→ ROOT / \"data\" / \"processed\" / \"metadata.json\",\n 212→ )\n 213→ except Exception as e:\n 214→ self._scenarios = [self._synthetic_fallback()]\n 215→ self._select_scenario(0)\n 216→\n 217→ def _synthetic_fallback(self) -> Scenario:\n 218→ \"\"\"Create a simple synthetic scenario if data files are missing.\"\"\"\n 219→ n = 200\n 220→ rng = np.random.RandomState(42)\n 221→ t = np.linspace(0, 4 * np.pi, n)\n 222→ actual = 50 + 25 * np.sin(t) + rng.normal(0, 3, n)\n 223→ predicted = np.roll(actual, -3)\n 224→ predicted[-3:] = actual[-3:]\n 225→ predicted += rng.normal(0, 2, n)\n 226→ return Scenario(\n 227→ name=\"synthetic_demo\",\n 228→ description=\"Synthetic sine wave demo\",\n 229→ actual_cpu=np.clip(actual, 10, 100),\n 230→ predicted_cpu=np.clip(predicted, 10, 100),\n 231→ duration_hours=n * 5 / 60,\n 232→ )\n 233→\n 234→ def _select_scenario(self, idx: int):\n 235→ self._current_scenario_idx = idx\n 236→ self._reset_simulation()\n 237→ self._update_all()\n 238→\n 239→ def _reset_simulation(self):\n 240→ self._step = 0\n 241→ self._playing = False\n 242→ sc = self._scenarios[self._current_scenario_idx]\n 243→\n 244→ # Run full experiment for Tab 3\n 245→ runner = ExperimentRunner(\n 246→ sc.actual_cpu, sc.predicted_cpu,\n 247→ ml_config=self._build_ml_config(),\n 248→ )\n 249→ self._history, self._full_metrics = runner.run()\n 250→\n 251→ # Reset step-by-step state\n 252→ self._ml_sim = MLAutoscalerSimulator(self._build_ml_config())\n 253→ self._hpa_replicas_trace = []\n 254→ self._ml_replicas_trace = []\n 255→ self._actual_trace = []\n 256→ self._predicted_trace = []\n 257→ self._smoothed_trace = []\n 258→ self._decisions_log = []\n 259→\n 260→ def _build_ml_config(self) -> MLConfig:\n 261→ return MLConfig(\n 262→ target_utilization=self._target_var.get(),\n 263→ smoothing_alpha=self._alpha_var.get(),\n 264→ safety_margin=self._safety_var.get(),\n 265→ scale_up_threshold_factor=self._up_thresh_var.get(),\n 266→ scale_down_threshold_factor=self._down_thresh_var.get(),\n 267→ )\n 268→\n 269→ # -------------------------------------------------------------------\n 270→ # Control bar (top)\n 271→ # -------------------------------------------------------------------\n 272→\n 273→ def _build_controls(self):\n 274→ bar = ctk.CTkFrame(self, fg_color=C[\"bg_card\"], height=50)\n 275→ bar.pack(fill=\"x\", padx=8, pady=(8, 4))\n 276→\n 277→ ctk.CTkLabel(\n 278→ bar, text=\"ML Autoscaler — Experiment Dashboard\",\n 279→ font=(\"Consolas\", 16, \"bold\"), text_color=C[\"text\"]\n 280→ ).pack(side=\"left\", padx=12)\n 281→\n 282→ # Transport controls\n 283→ self._play_btn = ctk.CTkButton(\n 284→ bar, text=\"▶ Play\", width=90, fg_color=C[\"green\"],\n 285→ text_color=\"#000\", hover_color=\"#5ea554\",\n 286→ command=self._toggle_play\n 287→ )\n 288→ self._play_btn.pack(side=\"left\", padx=4)\n 289→\n 290→ ctk.CTkButton(\n 291→ bar, text=\"⏭ Step\", width=90, fg_color=C[\"blue\"],\n 292→ text_color=\"#000\", hover_color=\"#4678c9\",\n 293→ command=self._do_step\n 294→ ).pack(side=\"left\", padx=4)\n 295→\n 296→ ctk.CTkButton(\n 297→ bar, text=\"↺ Reset\", width=90, fg_color=C[\"orange\"],\n 298→ text_color=\"#000\", hover_color=\"#d97e24\",\n 299→ command=lambda: (self._reset_simulation(), self._update_all())\n 300→ ).pack(side=\"left\", padx=4)\n 301→\n 302→ # Step counter\n 303→ self._step_lbl = ctk.CTkLabel(\n 304→ bar, text=\"Step: 0 / 0\", font=(\"Consolas\", 12),\n 305→ text_color=C[\"text_dim\"]\n 306→ )\n 307→ self._step_lbl.pack(side=\"right\", padx=12)\n 308→\n 309→ # Speed slider\n 310→ ctk.CTkLabel(bar, text=\"Speed:\", text_color=C[\"text_dim\"]).pack(side=\"right\")\n 311→ self._speed_slider = ctk.CTkSlider(\n 312→ bar, from_=50, to=1000, width=120,\n 313→ command=lambda v: setattr(self, \"_speed_ms\", int(v))\n 314→ )\n 315→ self._speed_slider.set(200)\n 316→ self._speed_slider.pack(side=\"right\", padx=4)\n 317→\n 318→ def _toggle_play(self):\n 319→ self._playing = not self._playing\n 320→ self._play_btn.configure(\n 321→ text=\"⏸ Pause\" if self._playing else \"▶ Play\",\n 322→ fg_color=C[\"yellow\"] if self._playing else C[\"green\"],\n 323→ )\n 324→ if self._playing:\n 325→ self._auto_step()\n 326→\n 327→ def _auto_step(self):\n 328→ if not self._playing:\n 329→ return\n 330→ sc = self._scenarios[self._current_scenario_idx]\n 331→ if self._step < len(sc.actual_cpu):\n 332→ self._do_step()\n 333→ self.after(self._speed_ms, self._auto_step)\n 334→ else:\n 335→ self._playing = False\n 336→ self._play_btn.configure(text=\"▶ Play\", fg_color=C[\"green\"])\n 337→\n 338→ def _do_step(self):\n 339→ sc = self._scenarios[self._current_scenario_idx]\n 340→ if self._step >= len(sc.actual_cpu):\n 341→ return\n 342→\n 343→ t = self._step\n 344→ actual = float(sc.actual_cpu[t])\n 345→ predicted = float(sc.predicted_cpu[t])\n 346→\n 347→ # ML autoscaler step\n 348→ ml_replicas, ml_dir = self._ml_sim.step(predicted, t)\n 349→ smoothed = self._ml_sim._smoothed or predicted\n 350→\n 351→ # HPA (from pre-computed history)\n 352→ hpa_replicas = self._history[t].hpa_replicas if t < len(self._history) else 2\n 353→\n 354→ self._actual_trace.append(actual)\n 355→ self._predicted_trace.append(predicted)\n 356→ self._smoothed_trace.append(smoothed)\n 357→ self._ml_replicas_trace.append(ml_replicas)\n 358→ self._hpa_replicas_trace.append(hpa_replicas)\n 359→ self._decisions_log.append(\n 360→ f\"[t={t:>3}] {ml_dir:>4} | ML:{ml_replicas} HPA:{hpa_replicas} | \"\n 361→ f\"actual={actual:.1f}% pred={predicted:.1f}% smooth={smoothed:.1f}%\"\n 362→ )\n 363→\n 364→ self._step += 1\n 365→ self._update_all()\n 366→\n 367→ # -------------------------------------------------------------------\n 368→ # Sidebar (parameters)\n 369→ # -------------------------------------------------------------------\n 370→\n 371→ def _build_sidebar(self):\n 372→ sidebar = ctk.CTkFrame(self, fg_color=C[\"sidebar\"], width=220, corner_radius=0)\n 373→ sidebar.pack(side=\"left\", fill=\"y\", padx=0, pady=0)\n 374→ sidebar.pack_propagate(False)\n 375→\n 376→ ctk.CTkLabel(\n 377→ sidebar, text=\"Parameters\", font=(\"Consolas\", 14, \"bold\"),\n 378→ text_color=C[\"text\"]\n 379→ ).pack(pady=(12, 8))\n 380→\n 381→ def make_slider(parent, label, from_, to, default, resolution=None):\n 382→ ctk.CTkLabel(\n 383→ parent, text=label, font=(\"Consolas\", 10), text_color=C[\"text_dim\"]\n 384→ ).pack(pady=(8, 0), padx=8, anchor=\"w\")\n 385→ var = ctk.DoubleVar(value=default)\n 386→ val_lbl = ctk.CTkLabel(\n 387→ parent, text=f\"{default}\", font=(\"Consolas\", 10), text_color=C[\"text\"]\n 388→ )\n 389→ val_lbl.pack(padx=8, anchor=\"e\")\n 390→\n 391→ def on_change(v):\n 392→ val_lbl.configure(text=f\"{float(v):.2f}\")\n 393→\n 394→ slider = ctk.CTkSlider(\n 395→ parent, from_=from_, to=to, variable=var,\n 396→ width=180, command=on_change\n 397→ )\n 398→ slider.pack(padx=8)\n 399→ return var\n 400→\n 401→ self._target_var = make_slider(sidebar, \"Target Util %\", 20, 90, 60)\n 402→ self._alpha_var = make_slider(sidebar, \"EMA Alpha\", 0.1, 1.0, 0.7)\n 403→ self._safety_var = make_slider(sidebar, \"Safety Margin\", 0.0, 0.5, 0.15)\n 404→ self._up_thresh_var = make_slider(sidebar, \"Up Threshold ×\", 1.0, 1.5, 1.1)\n 405→ self._down_thresh_var = make_slider(sidebar, \"Down Threshold ×\", 0.3, 0.9, 0.7)\n 406→\n 407→ ctk.CTkButton(\n 408→ sidebar, text=\"Apply & Re-run\", fg_color=C[\"accent\"],\n 409→ text_color=\"#fff\", hover_color=\"#c73550\",\n 410→ command=lambda: (self._reset_simulation(), self._update_all())\n 411→ ).pack(pady=16, padx=12, fill=\"x\")\n 412→\n 413→ # Scenario selector\n 414→ ctk.CTkLabel(\n 415→ sidebar, text=\"Scenario\", font=(\"Consolas\", 14, \"bold\"),\n 416→ text_color=C[\"text\"]\n 417→ ).pack(pady=(16, 4))\n 418→\n 419→ self._scenario_btns: list[ctk.CTkButton] = []\n 420→ names = [\"Stable\", \"Ramp\", \"Spike\", \"Diurnal\"]\n 421→ for i, name in enumerate(names):\n 422→ btn = ctk.CTkButton(\n 423→ sidebar, text=name, width=180, height=28,\n 424→ fg_color=C[\"bg_card\"], hover_color=C[\"blue\"],\n 425→ command=lambda idx=i: self._select_scenario(idx)\n 426→ )\n 427→ btn.pack(pady=2, padx=12)\n 428→ self._scenario_btns.append(btn)\n 429→\n 430→ # -------------------------------------------------------------------\n 431→ # Tab view (3 tabs)\n 432→ # -------------------------------------------------------------------\n 433→\n 434→ def _build_tabview(self):\n 435→ self._tabview = ctk.CTkTabview(self, fg_color=C[\"bg\"])\n 436→ self._tabview.pack(fill=\"both\", expand=True, padx=8, pady=(0, 8))\n 437→\n 438→ self._tab_nn = self._tabview.add(\"Neural Network\")\n 439→ self._tab_algo = self._tabview.add(\"Scaling Algorithm\")\n 440→ self._tab_exp = self._tabview.add(\"Experiment\")\n 441→\n 442→ self._build_nn_tab()\n 443→ self._build_algo_tab()\n 444→ self._build_exp_tab()\n 445→\n 446→ # --- Tab 1: Neural Network ---\n 447→\n 448→ def _build_nn_tab(self):\n 449→ tab = self._tab_nn\n 450→\n 451→ # Top: pipeline diagram\n 452→ pipe_frame = ctk.CTkFrame(tab, fg_color=C[\"bg_card\"], corner_radius=8, height=90)\n 453→ pipe_frame.pack(fill=\"x\", padx=8, pady=4)\n 454→ pipe_frame.pack_propagate(False)\n 455→\n 456→ ctk.CTkLabel(\n 457→ pipe_frame, text=\"ONNX Inference Pipeline\",\n 458→ font=(\"Consolas\", 12, \"bold\"), text_color=C[\"text\"]\n 459→ ).pack(pady=(6, 4))\n 460→\n 461→ blocks_frame = ctk.CTkFrame(pipe_frame, fg_color=\"transparent\")\n 462→ blocks_frame.pack()\n 463→ stages = [\n 464→ (\"Raw Metrics\", \"5 values\", C[\"green\"]),\n 465→ (\"→\", \"\", C[\"text_dim\"]),\n 466→ (\"Feature Eng.\", \"44 features\", C[\"purple\"]),\n 467→ (\"→\", \"\", C[\"text_dim\"]),\n 468→ (\"StandardScaler\", \"normalize\", C[\"orange\"]),\n 469→ (\"→\", \"\", C[\"text_dim\"]),\n 470→ (\"ONNX LSTM\", \"(1,24,44)→(1,1)\", C[\"blue\"]),\n 471→ (\"→\", \"\", C[\"text_dim\"]),\n 472→ (\"Inverse Scale\", \"→ CPU %\", C[\"green\"]),\n 473→ ]\n 474→ for title, sub, color in stages:\n 475→ if title == \"→\":\n 476→ ctk.CTkLabel(\n 477→ blocks_frame, text=\"→\", font=(\"Consolas\", 16, \"bold\"),\n 478→ text_color=C[\"text_dim\"]\n 479→ ).pack(side=\"left\", padx=2)\n 480→ else:\n 481→ f = ctk.CTkFrame(blocks_frame, fg_color=C[\"bg\"], corner_radius=6,\n 482→ border_width=1, border_color=color)\n 483→ f.pack(side=\"left\", padx=4)\n 484→ ctk.CTkLabel(f, text=title, font=(\"Consolas\", 9, \"bold\"),\n 485→ text_color=color).pack(padx=6, pady=(2, 0))\n 486→ ctk.CTkLabel(f, text=sub, font=(\"Consolas\", 8),\n 487→ text_color=C[\"text_dim\"]).pack(padx=6, pady=(0, 2))\n 488→\n 489→ # Middle: prediction output + buffer\n 490→ mid = ctk.CTkFrame(tab, fg_color=\"transparent\")\n 491→ mid.pack(fill=\"x\", padx=8, pady=4)\n 492→\n 493→ # Buffer status\n 494→ buf_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8, width=250)\n 495→ buf_frame.pack(side=\"left\", padx=(0, 8), fill=\"y\")\n 496→ buf_frame.pack_propagate(False)\n 497→ ctk.CTkLabel(buf_frame, text=\"Buffer Status\", font=(\"Consolas\", 11, \"bold\"),\n 498→ text_color=C[\"text\"]).pack(pady=(8, 4))\n 499→ self._buf_progress = ctk.CTkProgressBar(buf_frame, width=200)\n 500→ self._buf_progress.pack(pady=4, padx=12)\n 501→ self._buf_progress.set(0)\n 502→ self._buf_lbl = ctk.CTkLabel(\n 503→ buf_frame, text=\"0 / 24 NOT READY\", font=(\"Consolas\", 10),\n 504→ text_color=C[\"red\"]\n 505→ )\n 506→ self._buf_lbl.pack()\n 507→\n 508→ # Prediction big number\n 509→ pred_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8, width=200)\n 510→ pred_frame.pack(side=\"left\", padx=8, fill=\"y\")\n 511→ pred_frame.pack_propagate(False)\n 512→ ctk.CTkLabel(pred_frame, text=\"ML Prediction\", font=(\"Consolas\", 11, \"bold\"),\n 513→ text_color=C[\"text\"]).pack(pady=(8, 2))\n 514→ self._pred_big = ctk.CTkLabel(\n 515→ pred_frame, text=\"—\", font=(\"Consolas\", 28, \"bold\"),\n 516→ text_color=C[\"blue\"]\n 517→ )\n 518→ self._pred_big.pack(pady=4)\n 519→ self._pred_actual = ctk.CTkLabel(\n 520→ pred_frame, text=\"Actual: —\", font=(\"Consolas\", 10),\n 521→ text_color=C[\"text_dim\"]\n 522→ )\n 523→ self._pred_actual.pack()\n 524→\n 525→ # Raw metrics panel\n 526→ raw_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8)\n 527→ raw_frame.pack(side=\"left\", fill=\"both\", expand=True, padx=8)\n 528→ ctk.CTkLabel(raw_frame, text=\"Current Metrics\", font=(\"Consolas\", 11, \"bold\"),\n 529→ text_color=C[\"text\"]).pack(pady=(8, 4))\n 530→ self._raw_labels: dict[str, ctk.CTkLabel] = {}\n 531→ for metric in [\"cpu_usage\", \"mem_util\", \"net_in\", \"net_out\", \"disk_io\"]:\n 532→ lbl = ctk.CTkLabel(raw_frame, text=f\"{metric}: —\",\n 533→ font=(\"Consolas\", 10), text_color=C[\"text_dim\"])\n 534→ lbl.pack(anchor=\"w\", padx=12)\n 535→ self._raw_labels[metric] = lbl\n 536→\n 537→ # Bottom: prediction history chart\n 538→ self._nn_fig, self._nn_ax = plt.subplots(1, 1, figsize=(10, 3), dpi=80)\n 539→ self._nn_canvas = embed_figure(tab, self._nn_fig)\n 540→\n 541→ # --- Tab 2: Scaling Algorithm ---\n 542→\n 543→ def _build_algo_tab(self):\n 544→ tab = self._tab_algo\n 545→\n 546→ # 9-step pipeline\n 547→ pipe = ctk.CTkFrame(tab, fg_color=\"transparent\")\n 548→ pipe.pack(fill=\"x\", padx=4, pady=4)\n 549→\n 550→ step_names = [\n 551→ \"EMA\", \"Hysteresis\", \"Desired\", \"Clamp\",\n 552→ \"Rate Limit\", \"Cooldown\", \"Stabilize\", \"No Change?\", \"Commit\"\n 553→ ]\n 554→ self._algo_blocks: list[PipelineBlock] = []\n 555→ for i, name in enumerate(step_names):\n 556→ if i > 0:\n 557→ ctk.CTkLabel(pipe, text=\"→\", font=(\"Consolas\", 14, \"bold\"),\n 558→ text_color=C[\"text_dim\"]).pack(side=\"left\", padx=1)\n 559→ block = PipelineBlock(pipe, i + 1, name)\n 560→ block.pack(side=\"left\", padx=2)\n 561→ self._algo_blocks.append(block)\n 562→\n 563→ # State panel\n 564→ state_frame = ctk.CTkFrame(tab, fg_color=C[\"bg_card\"], corner_radius=8, height=40)\n 565→ state_frame.pack(fill=\"x\", padx=8, pady=4)\n 566→ self._state_lbl = ctk.CTkLabel(\n 567→ state_frame,\n 568→ text=\"Cooldown UP: — | Cooldown DOWN: — | Consec. Below: — | Direction: —\",\n 569→ font=(\"Consolas\", 10), text_color=C[\"text_dim\"]\n 570→ )\n 571→ self._state_lbl.pack(pady=6)\n 572→\n 573→ # Charts: CPU + Replicas side by side\n 574→ charts = ctk.CTkFrame(tab, fg_color=\"transparent\")\n 575→ charts.pack(fill=\"both\", expand=True, padx=4, pady=4)\n 576→\n 577→ left = ctk.CTkFrame(charts, fg_color=\"transparent\")\n 578→ left.pack(side=\"left\", fill=\"both\", expand=True)\n 579→ self._algo_cpu_fig, self._algo_cpu_ax = plt.subplots(1, 1, figsize=(5.5, 3), dpi=80)\n 580→ self._algo_cpu_canvas = embed_figure(left, self._algo_cpu_fig)\n 581→\n 582→ right = ctk.CTkFrame(charts, fg_color=\"transparent\")\n 583→ right.pack(side=\"left\", fill=\"both\", expand=True)\n 584→ self._algo_rep_fig, self._algo_rep_ax = plt.subplots(1, 1, figsize=(5.5, 3), dpi=80)\n 585→ self._algo_rep_canvas = embed_figure(right, self._algo_rep_fig)\n 586→\n 587→ # Decision log\n 588→ self._decision_log = ctk.CTkTextbox(\n 589→ tab, height=100, font=(\"Consolas\", 9), fg_color=C[\"bg\"],\n 590→ text_color=C[\"text_dim\"]\n 591→ )\n 592→ self._decision_log.pack(fill=\"x\", padx=8, pady=(0, 4))\n 593→\n 594→ # --- Tab 3: Experiment Comparison ---\n 595→\n 596→ def _build_exp_tab(self):\n 597→ tab = self._tab_exp\n 598→\n 599→ # Main comparison chart\n 600→ self._exp_fig, self._exp_axes = plt.subplots(2, 1, figsize=(10, 5), dpi=80,\n 601→ gridspec_kw={\"height_ratios\": [2, 1]})\n 602→ self._exp_canvas = embed_figure(tab, self._exp_fig)\n 603→\n 604→ # KPI cards grid\n 605→ cards_frame = ctk.CTkFrame(tab, fg_color=\"transparent\")\n 606→ cards_frame.pack(fill=\"x\", padx=8, pady=4)\n 607→\n 608→ kpi_names = [\n 609→ \"Avg Replicas\", \"Replica-Min\", \"SLA Violations\", \"Scaling Events\",\n 610→ \"Over-Prov %\", \"Under-Prov %\", \"Reaction Time\", \"Cost Savings %\"\n 611→ ]\n 612→ self._kpi_cards: list[KPICard] = []\n 613→ for i, name in enumerate(kpi_names):\n 614→ card = KPICard(cards_frame, name)\n 615→ card.grid(row=i // 4, column=i % 4, padx=4, pady=4, sticky=\"nsew\")\n 616→ self._kpi_cards.append(card)\n 617→ for c in range(4):\n 618→ cards_frame.columnconfigure(c, weight=1)\n 619→\n 620→ # -------------------------------------------------------------------\n 621→ # Update all visuals\n 622→ # -------------------------------------------------------------------\n 623→\n 624→ def _update_all(self):\n 625→ sc = self._scenarios[self._current_scenario_idx]\n 626→ total = len(sc.actual_cpu)\n 627→ self._step_lbl.configure(text=f\"Step: {self._step} / {total}\")\n 628→\n 629→ # Highlight active scenario button\n 630→ for i, btn in enumerate(self._scenario_btns):\n 631→ btn.configure(\n 632→ fg_color=C[\"blue\"] if i == self._current_scenario_idx else C[\"bg_card\"]\n 633→ )\n 634→\n 635→ self._update_nn_tab()\n 636→ self._update_algo_tab()\n 637→ self._update_exp_tab()\n 638→\n 639→ def _update_nn_tab(self):\n 640→ n = self._step\n 641→\n 642→ # Buffer status\n 643→ buf_fill = min(n, 24)\n 644→ self._buf_progress.set(buf_fill / 24)\n 645→ ready = n >= 24\n 646→ self._buf_lbl.configure(\n 647→ text=f\"{min(n, 54)} / 24 {'READY' if ready else 'FILLING...'}\",\n 648→ text_color=C[\"green\"] if ready else C[\"red\"]\n 649→ )\n 650→\n 651→ # Current prediction\n 652→ if n > 0:\n 653→ pred = self._predicted_trace[-1]\n 654→ actual = self._actual_trace[-1]\n 655→ color = C[\"green\"] if pred < 42 else C[\"yellow\"] if pred < 66 else C[\"orange\"] if pred < 85 else C[\"red\"]\n 656→ self._pred_big.configure(text=f\"{pred:.1f}%\", text_color=color)\n 657→ self._pred_actual.configure(text=f\"Actual: {actual:.1f}%\")\n 658→\n 659→ # Raw metrics (simulated from CPU)\n 660→ self._raw_labels[\"cpu_usage\"].configure(text=f\"cpu_usage: {actual:.1f}%\")\n 661→ self._raw_labels[\"mem_util\"].configure(text=f\"mem_util: {actual * 0.6:.1f}%\")\n 662→ self._raw_labels[\"net_in\"].configure(text=f\"net_in: {actual * 1.2:.0f} KB/s\")\n 663→ self._raw_labels[\"net_out\"].configure(text=f\"net_out: {actual * 0.8:.0f} KB/s\")\n 664→ self._raw_labels[\"disk_io\"].configure(text=f\"disk_io: {actual * 0.3:.1f}%\")\n 665→\n 666→ # Prediction history chart\n 667→ self._nn_ax.clear()\n 668→ if n > 1:\n 669→ x = range(max(0, n - 80), n)\n 670→ a_slice = self._actual_trace[max(0, n - 80):]\n 671→ p_slice = self._predicted_trace[max(0, n - 80):]\n 672→ self._nn_ax.plot(x, a_slice, color=C[\"green\"], lw=1.5, label=\"Actual CPU\")\n 673→ self._nn_ax.plot(x, p_slice, color=C[\"purple\"], lw=1.5, ls=\"--\", label=\"Predicted\")\n 674→ self._nn_ax.axhline(60, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.5)\n 675→ self._nn_ax.set_ylabel(\"CPU %\")\n 676→ self._nn_ax.set_ylim(0, 100)\n 677→ self._nn_ax.legend(loc=\"upper right\", fontsize=8)\n 678→ self._nn_ax.grid(True, alpha=0.2)\n 679→ self._nn_ax.set_title(\"Prediction History (last 80 steps)\", fontsize=10)\n 680→ self._nn_fig.tight_layout()\n 681→ self._nn_canvas.draw_idle()\n 682→\n 683→ def _update_algo_tab(self):\n 684→ n = self._step\n 685→ if n == 0:\n 686→ for block in self._algo_blocks:\n 687→ block.update(\"—\", \"skipped\")\n 688→ return\n 689→\n 690→ # Get current step details\n 691→ pred = self._predicted_trace[-1]\n 692→ smoothed = self._smoothed_trace[-1]\n 693→ ml_replicas = self._ml_replicas_trace[-1]\n 694→ prev_replicas = self._ml_replicas_trace[-2] if n > 1 else 2\n 695→\n 696→ target = self._target_var.get()\n 697→ up_thresh = target * self._up_thresh_var.get()\n 698→ down_thresh = target * self._down_thresh_var.get()\n 699→\n 700→ # Determine direction\n 701→ if smoothed > up_thresh:\n 702→ direction = \"UP\"\n 703→ elif smoothed < down_thresh:\n 704→ direction = \"DOWN\"\n 705→ else:\n 706→ direction = \"HOLD\"\n 707→\n 708→ # Update blocks\n 709→ self._algo_blocks[0].update(f\"{pred:.1f}→{smoothed:.1f}%\", \"active\")\n 710→ self._algo_blocks[1].update(\n 711→ f\"{smoothed:.0f}% {'>' if direction == 'UP' else '<' if direction == 'DOWN' else 'in'} \"\n 712→ f\"[{down_thresh:.0f},{up_thresh:.0f}]\\n→{direction}\",\n 713→ \"active\" if direction != \"HOLD\" else \"blocked\"\n 714→ )\n 715→\n 716→ if direction == \"HOLD\":\n 717→ for i in range(2, 9):\n 718→ self._algo_blocks[i].update(\"—\", \"skipped\")\n 719→ else:\n 720→ raw_desired = math.ceil(prev_replicas * smoothed / target)\n 721→ if direction == \"UP\":\n 722→ raw_desired = math.ceil(raw_desired * (1 + self._safety_var.get()))\n 723→ self._algo_blocks[2].update(f\"{prev_replicas}×{smoothed:.0f}/{target:.0f}\\n={raw_desired}\", \"active\")\n 724→ clamped = max(2, min(8, raw_desired))\n 725→ self._algo_blocks[3].update(f\"[2,8]→{clamped}\", \"active\")\n 726→ rate_lim = min(clamped, prev_replicas + 2) if direction == \"UP\" else max(clamped, prev_replicas - 1)\n 727→ self._algo_blocks[4].update(f\"±limit→{rate_lim}\", \"active\")\n 728→ self._algo_blocks[5].update(\"OFF ✓\", \"active\")\n 729→ self._algo_blocks[6].update(\"N/A\" if direction == \"UP\" else \"check\", \"active\")\n 730→\n 731→ executed = ml_replicas != prev_replicas\n 732→ self._algo_blocks[7].update(\n 733→ f\"{ml_replicas}{'≠' if executed else '='}{prev_replicas}\",\n 734→ \"active\" if executed else \"blocked\"\n 735→ )\n 736→ if executed:\n 737→ self._algo_blocks[8].update(f\"{prev_replicas}→{ml_replicas}\", \"executed\")\n 738→ else:\n 739→ self._algo_blocks[8].update(\"hold\", \"blocked\")\n 740→\n 741→ # State panel\n 742→ sim = self._ml_sim\n 743→ self._state_lbl.configure(\n 744→ text=f\"Direction: {direction} | \"\n 745→ f\"Consec. Below: {sim._consecutive_below} | \"\n 746→ f\"Replicas: {ml_replicas} | \"\n 747→ f\"Smoothed: {smoothed:.1f}%\"\n 748→ )\n 749→\n 750→ # CPU chart\n 751→ self._algo_cpu_ax.clear()\n 752→ if n > 1:\n 753→ x = range(n)\n 754→ self._algo_cpu_ax.plot(x, self._smoothed_trace, color=C[\"blue\"], lw=1.5, label=\"Smoothed\")\n 755→ self._algo_cpu_ax.plot(x, self._actual_trace, color=C[\"green\"], lw=1, alpha=0.5, label=\"Actual\")\n 756→ self._algo_cpu_ax.axhline(up_thresh, color=C[\"red\"], ls=\"--\", lw=0.8, label=f\"Up {up_thresh:.0f}%\")\n 757→ self._algo_cpu_ax.axhline(down_thresh, color=C[\"orange\"], ls=\"--\", lw=0.8, label=f\"Down {down_thresh:.0f}%\")\n 758→ self._algo_cpu_ax.axhline(target, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.6)\n 759→ self._algo_cpu_ax.axhspan(down_thresh, up_thresh, alpha=0.08, color=C[\"yellow\"])\n 760→ self._algo_cpu_ax.set_ylim(0, 100)\n 761→ self._algo_cpu_ax.set_ylabel(\"CPU %\")\n 762→ self._algo_cpu_ax.legend(loc=\"upper right\", fontsize=7)\n 763→ self._algo_cpu_ax.grid(True, alpha=0.2)\n 764→ self._algo_cpu_ax.set_title(\"CPU + Thresholds\", fontsize=10)\n 765→ self._algo_cpu_fig.tight_layout()\n 766→ self._algo_cpu_canvas.draw_idle()\n 767→\n 768→ # Replica chart\n 769→ self._algo_rep_ax.clear()\n 770→ if n > 1:\n 771→ x = range(n)\n 772→ self._algo_rep_ax.step(x, self._ml_replicas_trace, color=C[\"blue\"], lw=2, where=\"post\", label=\"ML Replicas\")\n 773→ self._algo_rep_ax.step(x, self._hpa_replicas_trace, color=C[\"red\"], lw=1.5, where=\"post\", alpha=0.6, label=\"HPA Replicas\")\n 774→ self._algo_rep_ax.axhline(2, color=C[\"text_dim\"], ls=\":\", lw=0.7, alpha=0.5)\n 775→ self._algo_rep_ax.axhline(8, color=C[\"text_dim\"], ls=\":\", lw=0.7, alpha=0.5)\n 776→ self._algo_rep_ax.set_ylim(0, 10)\n 777→ self._algo_rep_ax.set_ylabel(\"Replicas\")\n 778→ self._algo_rep_ax.legend(loc=\"upper right\", fontsize=7)\n 779→ self._algo_rep_ax.grid(True, alpha=0.2)\n 780→ self._algo_rep_ax.set_title(\"Replica Count\", fontsize=10)\n 781→ self._algo_rep_fig.tight_layout()\n 782→ self._algo_rep_canvas.draw_idle()\n 783→\n 784→ # Decision log\n 785→ self._decision_log.configure(state=\"normal\")\n 786→ self._decision_log.delete(\"1.0\", \"end\")\n 787→ # Show last 20 entries\n 788→ for line in self._decisions_log[-20:]:\n 789→ self._decision_log.insert(\"end\", line + \"\\n\")\n 790→ self._decision_log.configure(state=\"disabled\")\n 791→ self._decision_log.see(\"end\")\n 792→\n 793→ def _update_exp_tab(self):\n 794→ if not hasattr(self, \"_full_metrics\"):\n 795→ return\n 796→ metrics = self._full_metrics\n 797→ history = self._history\n 798→\n 799→ # Top chart: Actual CPU + replicas\n 800→ ax1, ax2 = self._exp_axes\n 801→ ax1.clear()\n 802→ ax2.clear()\n 803→\n 804→ n = len(history)\n 805→ if n > 0:\n 806→ x = range(n)\n 807→ actual = [r.actual_cpu for r in history]\n 808→ hpa_r = [r.hpa_replicas for r in history]\n 809→ ml_r = [r.ml_replicas for r in history]\n 810→\n 811→ # CPU trace (background)\n 812→ ax1.fill_between(x, actual, alpha=0.15, color=C[\"text_dim\"])\n 813→ ax1.plot(x, actual, color=C[\"text_dim\"], lw=0.8, alpha=0.5, label=\"Actual CPU\")\n 814→\n 815→ # Replicas\n 816→ ax1.step(x, [r * 10 for r in hpa_r], color=C[\"red\"], lw=1.5,\n 817→ where=\"post\", label=\"HPA Replicas (×10)\")\n 818→ ax1.step(x, [r * 10 for r in ml_r], color=C[\"blue\"], lw=2,\n 819→ where=\"post\", label=\"ML Replicas (×10)\")\n 820→\n 821→ # SLA threshold\n 822→ target = self._target_var.get()\n 823→ ax1.axhline(target * 1.2, color=C[\"red\"], ls=\":\", lw=0.8, alpha=0.5, label=\"SLA Limit\")\n 824→\n 825→ ax1.set_ylabel(\"CPU % / Replicas×10\")\n 826→ ax1.set_ylim(0, 100)\n 827→ ax1.legend(loc=\"upper right\", fontsize=7, ncol=2)\n 828→ ax1.set_title(f\"Experiment: {self._scenarios[self._current_scenario_idx].name}\", fontsize=11)\n 829→ ax1.grid(True, alpha=0.2)\n 830→\n 831→ # Bottom chart: replica diff\n 832→ diff = [m - h for m, h in zip(ml_r, hpa_r)]\n 833→ colors_bar = [C[\"green\"] if d < 0 else C[\"red\"] if d > 0 else C[\"text_dim\"] for d in diff]\n 834→ ax2.bar(x, diff, color=colors_bar, alpha=0.7, width=1.0)\n 835→ ax2.axhline(0, color=C[\"text_dim\"], lw=0.5)\n 836→ ax2.set_ylabel(\"ML - HPA\")\n 837→ ax2.set_xlabel(\"Step\")\n 838→ ax2.grid(True, alpha=0.2)\n 839→ ax2.set_title(\"Replica Difference (negative = ML uses fewer)\", fontsize=9)\n 840→\n 841→ self._exp_fig.tight_layout()\n 842→ self._exp_canvas.draw_idle()\n 843→\n 844→ # KPI cards\n 845→ self._kpi_cards[0].set_values(f\"{metrics.hpa_avg_replicas:.1f}\", f\"{metrics.ml_avg_replicas:.1f}\")\n 846→ self._kpi_cards[1].set_values(f\"{metrics.hpa_replica_minutes:.0f}\", f\"{metrics.ml_replica_minutes:.0f}\")\n 847→ self._kpi_cards[2].set_values(f\"{metrics.hpa_sla_violations}\", f\"{metrics.ml_sla_violations}\")\n 848→ self._kpi_cards[3].set_values(f\"{metrics.hpa_scaling_events}\", f\"{metrics.ml_scaling_events}\")\n 849→ self._kpi_cards[4].set_values(f\"{metrics.hpa_over_provisioning_pct:.1f}%\", f\"{metrics.ml_over_provisioning_pct:.1f}%\")\n 850→ self._kpi_cards[5].set_values(f\"{metrics.hpa_under_provisioning_pct:.1f}%\", f\"{metrics.ml_under_provisioning_pct:.1f}%\")\n 851→ self._kpi_cards[6].set_values(f\"{metrics.hpa_avg_reaction_time:.1f}\", f\"{metrics.ml_avg_reaction_time:.1f}\")\n 852→ self._kpi_cards[7].set_values(f\"—\", f\"{metrics.cost_savings_pct:+.1f}%\")\n 853→\n 854→\n 855→# ---------------------------------------------------------------------------\n 856→# Entry point\n 857→# ---------------------------------------------------------------------------\n 858→\n 859→\n 860→def main():\n 861→ app = ExperimentApp()\n 862→ app.mainloop()\n 863→\n 864→\n 865→if __name__ == \"__main__\":\n 866→ main()\n 867→\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n" | |
| } | |
| ] | |
| }, | |
| "parent_tool_use_id": null, | |
| "session_id": "ce1ab353-0721-47e7-a373-84e062534c7f", | |
| "uuid": "b23deec8-50fc-478d-a690-848733989501", | |
| "tool_use_result": { | |
| "type": "text", | |
| "file": { | |
| "filePath": "/tmp/gh-issue-solver-1770487990892/scripts/experiment_gui.py", | |
| "content": "\"\"\"ML Autoscaler — Experiment Dashboard (CustomTkinter).\n\nInteractive visualization of:\n - Neural network internals (buffer, features, ONNX inference)\n - Scaling algorithm 9-step pipeline\n - HPA vs ML autoscaler comparison\n\nUsage:\n python scripts/experiment_gui.py\n\"\"\"\n\nfrom __future__ import annotations\n\nimport math\nimport sys\nfrom pathlib import Path\n\nimport customtkinter as ctk\nimport matplotlib\nimport numpy as np\n\nmatplotlib.use(\"TkAgg\")\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nfrom matplotlib.figure import Figure\n\n# Add project root to path for imports\nROOT = Path(__file__).resolve().parent.parent\nsys.path.insert(0, str(ROOT / \"src\"))\nsys.path.insert(0, str(ROOT))\n\nfrom scripts.experiment_scenarios import get_all_scenarios, Scenario\nfrom scripts.experiment_simulator import (\n ExperimentRunner,\n HPAConfig,\n MLAutoscalerSimulator,\n MLConfig,\n StepRecord,\n compute_metrics,\n)\n\n# ---------------------------------------------------------------------------\n# Color palette (unified with Grafana dashboards)\n# ---------------------------------------------------------------------------\n\nC = {\n \"bg\": \"#1a1a2e\",\n \"bg_card\": \"#16213e\",\n \"sidebar\": \"#0f3460\",\n \"accent\": \"#e94560\",\n \"green\": \"#73BF69\",\n \"yellow\": \"#FADE2A\",\n \"orange\": \"#FF9830\",\n \"red\": \"#F2495C\",\n \"blue\": \"#5794F2\",\n \"purple\": \"#B877D9\",\n \"text\": \"#e0e0e0\",\n \"text_dim\": \"#8892b0\",\n \"grid\": \"#2a2a4a\",\n}\n\n# Matplotlib dark style\nplt.rcParams.update({\n \"figure.facecolor\": C[\"bg_card\"],\n \"axes.facecolor\": C[\"bg\"],\n \"axes.edgecolor\": C[\"grid\"],\n \"axes.labelcolor\": C[\"text\"],\n \"text.color\": C[\"text\"],\n \"xtick.color\": C[\"text_dim\"],\n \"ytick.color\": C[\"text_dim\"],\n \"grid.color\": C[\"grid\"],\n \"grid.alpha\": 0.3,\n \"font.size\": 9,\n \"legend.facecolor\": C[\"bg_card\"],\n \"legend.edgecolor\": C[\"grid\"],\n})\n\n\n# ---------------------------------------------------------------------------\n# Utility: embed a matplotlib figure in a CTkFrame\n# ---------------------------------------------------------------------------\n\n\ndef embed_figure(parent: ctk.CTkFrame, fig: Figure) -> FigureCanvasTkAgg:\n canvas = FigureCanvasTkAgg(fig, master=parent)\n canvas.draw()\n canvas.get_tk_widget().pack(fill=\"both\", expand=True)\n return canvas\n\n\n# ---------------------------------------------------------------------------\n# KPI Card widget\n# ---------------------------------------------------------------------------\n\n\nclass KPICard(ctk.CTkFrame):\n \"\"\"Compact KPI card: title + HPA value + ML value.\"\"\"\n\n def __init__(self, master, title: str, **kw):\n super().__init__(master, fg_color=C[\"bg_card\"], corner_radius=8, **kw)\n self._title_lbl = ctk.CTkLabel(\n self, text=title, font=(\"Consolas\", 11), text_color=C[\"text_dim\"]\n )\n self._title_lbl.pack(pady=(6, 2))\n\n row = ctk.CTkFrame(self, fg_color=\"transparent\")\n row.pack(pady=(0, 6))\n self._hpa_lbl = ctk.CTkLabel(\n row, text=\"HPA: --\", font=(\"Consolas\", 13, \"bold\"), text_color=C[\"red\"]\n )\n self._hpa_lbl.pack(side=\"left\", padx=6)\n self._ml_lbl = ctk.CTkLabel(\n row, text=\"ML: --\", font=(\"Consolas\", 13, \"bold\"), text_color=C[\"blue\"]\n )\n self._ml_lbl.pack(side=\"left\", padx=6)\n\n def set_values(self, hpa_val: str, ml_val: str):\n self._hpa_lbl.configure(text=f\"HPA: {hpa_val}\")\n self._ml_lbl.configure(text=f\"ML: {ml_val}\")\n\n\n# ---------------------------------------------------------------------------\n# Pipeline Step block\n# ---------------------------------------------------------------------------\n\n\nclass PipelineBlock(ctk.CTkFrame):\n \"\"\"One step in the 9-step scaling algorithm visualization.\"\"\"\n\n def __init__(self, master, step_num: int, title: str, **kw):\n super().__init__(master, fg_color=C[\"bg_card\"], corner_radius=6,\n border_width=1, border_color=C[\"grid\"], **kw)\n self.configure(width=115, height=80)\n self.pack_propagate(False)\n ctk.CTkLabel(\n self, text=f\"Step {step_num}\", font=(\"Consolas\", 8),\n text_color=C[\"text_dim\"]\n ).pack(pady=(4, 0))\n ctk.CTkLabel(\n self, text=title, font=(\"Consolas\", 9, \"bold\"),\n text_color=C[\"text\"]\n ).pack()\n self._value_lbl = ctk.CTkLabel(\n self, text=\"—\", font=(\"Consolas\", 10), text_color=C[\"text_dim\"],\n wraplength=100\n )\n self._value_lbl.pack(pady=(2, 4))\n\n def update(self, text: str, status: str = \"active\"):\n colors = {\n \"active\": C[\"green\"],\n \"blocked\": C[\"yellow\"],\n \"skipped\": C[\"text_dim\"],\n \"executed\": C[\"accent\"],\n }\n self._value_lbl.configure(text=text, text_color=colors.get(status, C[\"text\"]))\n border = {\n \"active\": C[\"green\"],\n \"blocked\": C[\"yellow\"],\n \"executed\": C[\"accent\"],\n }\n self.configure(border_color=border.get(status, C[\"grid\"]))\n\n\n# ---------------------------------------------------------------------------\n# Main Application\n# ---------------------------------------------------------------------------\n\n\nclass ExperimentApp(ctk.CTk):\n def __init__(self):\n super().__init__()\n self.title(\"ML Autoscaler — Experiment Dashboard\")\n self.geometry(\"1440x920\")\n ctk.set_appearance_mode(\"dark\")\n\n # State\n self._scenarios: list[Scenario] = []\n self._current_scenario_idx = 0\n self._step = 0\n self._playing = False\n self._speed_ms = 200\n self._history: list[StepRecord] = []\n\n # ML simulator state for step-by-step\n self._ml_sim: MLAutoscalerSimulator | None = None\n self._hpa_replicas_trace: list[int] = []\n self._ml_replicas_trace: list[int] = []\n self._actual_trace: list[float] = []\n self._predicted_trace: list[float] = []\n self._smoothed_trace: list[float] = []\n self._decisions_log: list[str] = []\n\n # Build UI\n self._build_controls()\n self._build_sidebar()\n self._build_tabview()\n\n # Load data\n self._load_scenarios()\n\n # -------------------------------------------------------------------\n # Data loading\n # -------------------------------------------------------------------\n\n def _load_scenarios(self):\n try:\n self._scenarios = get_all_scenarios(\n ROOT / \"data\" / \"processed\" / \"test.csv\",\n ROOT / \"data\" / \"processed\" / \"scaler.pkl\",\n ROOT / \"data\" / \"processed\" / \"metadata.json\",\n )\n except Exception as e:\n self._scenarios = [self._synthetic_fallback()]\n self._select_scenario(0)\n\n def _synthetic_fallback(self) -> Scenario:\n \"\"\"Create a simple synthetic scenario if data files are missing.\"\"\"\n n = 200\n rng = np.random.RandomState(42)\n t = np.linspace(0, 4 * np.pi, n)\n actual = 50 + 25 * np.sin(t) + rng.normal(0, 3, n)\n predicted = np.roll(actual, -3)\n predicted[-3:] = actual[-3:]\n predicted += rng.normal(0, 2, n)\n return Scenario(\n name=\"synthetic_demo\",\n description=\"Synthetic sine wave demo\",\n actual_cpu=np.clip(actual, 10, 100),\n predicted_cpu=np.clip(predicted, 10, 100),\n duration_hours=n * 5 / 60,\n )\n\n def _select_scenario(self, idx: int):\n self._current_scenario_idx = idx\n self._reset_simulation()\n self._update_all()\n\n def _reset_simulation(self):\n self._step = 0\n self._playing = False\n sc = self._scenarios[self._current_scenario_idx]\n\n # Run full experiment for Tab 3\n runner = ExperimentRunner(\n sc.actual_cpu, sc.predicted_cpu,\n ml_config=self._build_ml_config(),\n )\n self._history, self._full_metrics = runner.run()\n\n # Reset step-by-step state\n self._ml_sim = MLAutoscalerSimulator(self._build_ml_config())\n self._hpa_replicas_trace = []\n self._ml_replicas_trace = []\n self._actual_trace = []\n self._predicted_trace = []\n self._smoothed_trace = []\n self._decisions_log = []\n\n def _build_ml_config(self) -> MLConfig:\n return MLConfig(\n target_utilization=self._target_var.get(),\n smoothing_alpha=self._alpha_var.get(),\n safety_margin=self._safety_var.get(),\n scale_up_threshold_factor=self._up_thresh_var.get(),\n scale_down_threshold_factor=self._down_thresh_var.get(),\n )\n\n # -------------------------------------------------------------------\n # Control bar (top)\n # -------------------------------------------------------------------\n\n def _build_controls(self):\n bar = ctk.CTkFrame(self, fg_color=C[\"bg_card\"], height=50)\n bar.pack(fill=\"x\", padx=8, pady=(8, 4))\n\n ctk.CTkLabel(\n bar, text=\"ML Autoscaler — Experiment Dashboard\",\n font=(\"Consolas\", 16, \"bold\"), text_color=C[\"text\"]\n ).pack(side=\"left\", padx=12)\n\n # Transport controls\n self._play_btn = ctk.CTkButton(\n bar, text=\"▶ Play\", width=90, fg_color=C[\"green\"],\n text_color=\"#000\", hover_color=\"#5ea554\",\n command=self._toggle_play\n )\n self._play_btn.pack(side=\"left\", padx=4)\n\n ctk.CTkButton(\n bar, text=\"⏭ Step\", width=90, fg_color=C[\"blue\"],\n text_color=\"#000\", hover_color=\"#4678c9\",\n command=self._do_step\n ).pack(side=\"left\", padx=4)\n\n ctk.CTkButton(\n bar, text=\"↺ Reset\", width=90, fg_color=C[\"orange\"],\n text_color=\"#000\", hover_color=\"#d97e24\",\n command=lambda: (self._reset_simulation(), self._update_all())\n ).pack(side=\"left\", padx=4)\n\n # Step counter\n self._step_lbl = ctk.CTkLabel(\n bar, text=\"Step: 0 / 0\", font=(\"Consolas\", 12),\n text_color=C[\"text_dim\"]\n )\n self._step_lbl.pack(side=\"right\", padx=12)\n\n # Speed slider\n ctk.CTkLabel(bar, text=\"Speed:\", text_color=C[\"text_dim\"]).pack(side=\"right\")\n self._speed_slider = ctk.CTkSlider(\n bar, from_=50, to=1000, width=120,\n command=lambda v: setattr(self, \"_speed_ms\", int(v))\n )\n self._speed_slider.set(200)\n self._speed_slider.pack(side=\"right\", padx=4)\n\n def _toggle_play(self):\n self._playing = not self._playing\n self._play_btn.configure(\n text=\"⏸ Pause\" if self._playing else \"▶ Play\",\n fg_color=C[\"yellow\"] if self._playing else C[\"green\"],\n )\n if self._playing:\n self._auto_step()\n\n def _auto_step(self):\n if not self._playing:\n return\n sc = self._scenarios[self._current_scenario_idx]\n if self._step < len(sc.actual_cpu):\n self._do_step()\n self.after(self._speed_ms, self._auto_step)\n else:\n self._playing = False\n self._play_btn.configure(text=\"▶ Play\", fg_color=C[\"green\"])\n\n def _do_step(self):\n sc = self._scenarios[self._current_scenario_idx]\n if self._step >= len(sc.actual_cpu):\n return\n\n t = self._step\n actual = float(sc.actual_cpu[t])\n predicted = float(sc.predicted_cpu[t])\n\n # ML autoscaler step\n ml_replicas, ml_dir = self._ml_sim.step(predicted, t)\n smoothed = self._ml_sim._smoothed or predicted\n\n # HPA (from pre-computed history)\n hpa_replicas = self._history[t].hpa_replicas if t < len(self._history) else 2\n\n self._actual_trace.append(actual)\n self._predicted_trace.append(predicted)\n self._smoothed_trace.append(smoothed)\n self._ml_replicas_trace.append(ml_replicas)\n self._hpa_replicas_trace.append(hpa_replicas)\n self._decisions_log.append(\n f\"[t={t:>3}] {ml_dir:>4} | ML:{ml_replicas} HPA:{hpa_replicas} | \"\n f\"actual={actual:.1f}% pred={predicted:.1f}% smooth={smoothed:.1f}%\"\n )\n\n self._step += 1\n self._update_all()\n\n # -------------------------------------------------------------------\n # Sidebar (parameters)\n # -------------------------------------------------------------------\n\n def _build_sidebar(self):\n sidebar = ctk.CTkFrame(self, fg_color=C[\"sidebar\"], width=220, corner_radius=0)\n sidebar.pack(side=\"left\", fill=\"y\", padx=0, pady=0)\n sidebar.pack_propagate(False)\n\n ctk.CTkLabel(\n sidebar, text=\"Parameters\", font=(\"Consolas\", 14, \"bold\"),\n text_color=C[\"text\"]\n ).pack(pady=(12, 8))\n\n def make_slider(parent, label, from_, to, default, resolution=None):\n ctk.CTkLabel(\n parent, text=label, font=(\"Consolas\", 10), text_color=C[\"text_dim\"]\n ).pack(pady=(8, 0), padx=8, anchor=\"w\")\n var = ctk.DoubleVar(value=default)\n val_lbl = ctk.CTkLabel(\n parent, text=f\"{default}\", font=(\"Consolas\", 10), text_color=C[\"text\"]\n )\n val_lbl.pack(padx=8, anchor=\"e\")\n\n def on_change(v):\n val_lbl.configure(text=f\"{float(v):.2f}\")\n\n slider = ctk.CTkSlider(\n parent, from_=from_, to=to, variable=var,\n width=180, command=on_change\n )\n slider.pack(padx=8)\n return var\n\n self._target_var = make_slider(sidebar, \"Target Util %\", 20, 90, 60)\n self._alpha_var = make_slider(sidebar, \"EMA Alpha\", 0.1, 1.0, 0.7)\n self._safety_var = make_slider(sidebar, \"Safety Margin\", 0.0, 0.5, 0.15)\n self._up_thresh_var = make_slider(sidebar, \"Up Threshold ×\", 1.0, 1.5, 1.1)\n self._down_thresh_var = make_slider(sidebar, \"Down Threshold ×\", 0.3, 0.9, 0.7)\n\n ctk.CTkButton(\n sidebar, text=\"Apply & Re-run\", fg_color=C[\"accent\"],\n text_color=\"#fff\", hover_color=\"#c73550\",\n command=lambda: (self._reset_simulation(), self._update_all())\n ).pack(pady=16, padx=12, fill=\"x\")\n\n # Scenario selector\n ctk.CTkLabel(\n sidebar, text=\"Scenario\", font=(\"Consolas\", 14, \"bold\"),\n text_color=C[\"text\"]\n ).pack(pady=(16, 4))\n\n self._scenario_btns: list[ctk.CTkButton] = []\n names = [\"Stable\", \"Ramp\", \"Spike\", \"Diurnal\"]\n for i, name in enumerate(names):\n btn = ctk.CTkButton(\n sidebar, text=name, width=180, height=28,\n fg_color=C[\"bg_card\"], hover_color=C[\"blue\"],\n command=lambda idx=i: self._select_scenario(idx)\n )\n btn.pack(pady=2, padx=12)\n self._scenario_btns.append(btn)\n\n # -------------------------------------------------------------------\n # Tab view (3 tabs)\n # -------------------------------------------------------------------\n\n def _build_tabview(self):\n self._tabview = ctk.CTkTabview(self, fg_color=C[\"bg\"])\n self._tabview.pack(fill=\"both\", expand=True, padx=8, pady=(0, 8))\n\n self._tab_nn = self._tabview.add(\"Neural Network\")\n self._tab_algo = self._tabview.add(\"Scaling Algorithm\")\n self._tab_exp = self._tabview.add(\"Experiment\")\n\n self._build_nn_tab()\n self._build_algo_tab()\n self._build_exp_tab()\n\n # --- Tab 1: Neural Network ---\n\n def _build_nn_tab(self):\n tab = self._tab_nn\n\n # Top: pipeline diagram\n pipe_frame = ctk.CTkFrame(tab, fg_color=C[\"bg_card\"], corner_radius=8, height=90)\n pipe_frame.pack(fill=\"x\", padx=8, pady=4)\n pipe_frame.pack_propagate(False)\n\n ctk.CTkLabel(\n pipe_frame, text=\"ONNX Inference Pipeline\",\n font=(\"Consolas\", 12, \"bold\"), text_color=C[\"text\"]\n ).pack(pady=(6, 4))\n\n blocks_frame = ctk.CTkFrame(pipe_frame, fg_color=\"transparent\")\n blocks_frame.pack()\n stages = [\n (\"Raw Metrics\", \"5 values\", C[\"green\"]),\n (\"→\", \"\", C[\"text_dim\"]),\n (\"Feature Eng.\", \"44 features\", C[\"purple\"]),\n (\"→\", \"\", C[\"text_dim\"]),\n (\"StandardScaler\", \"normalize\", C[\"orange\"]),\n (\"→\", \"\", C[\"text_dim\"]),\n (\"ONNX LSTM\", \"(1,24,44)→(1,1)\", C[\"blue\"]),\n (\"→\", \"\", C[\"text_dim\"]),\n (\"Inverse Scale\", \"→ CPU %\", C[\"green\"]),\n ]\n for title, sub, color in stages:\n if title == \"→\":\n ctk.CTkLabel(\n blocks_frame, text=\"→\", font=(\"Consolas\", 16, \"bold\"),\n text_color=C[\"text_dim\"]\n ).pack(side=\"left\", padx=2)\n else:\n f = ctk.CTkFrame(blocks_frame, fg_color=C[\"bg\"], corner_radius=6,\n border_width=1, border_color=color)\n f.pack(side=\"left\", padx=4)\n ctk.CTkLabel(f, text=title, font=(\"Consolas\", 9, \"bold\"),\n text_color=color).pack(padx=6, pady=(2, 0))\n ctk.CTkLabel(f, text=sub, font=(\"Consolas\", 8),\n text_color=C[\"text_dim\"]).pack(padx=6, pady=(0, 2))\n\n # Middle: prediction output + buffer\n mid = ctk.CTkFrame(tab, fg_color=\"transparent\")\n mid.pack(fill=\"x\", padx=8, pady=4)\n\n # Buffer status\n buf_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8, width=250)\n buf_frame.pack(side=\"left\", padx=(0, 8), fill=\"y\")\n buf_frame.pack_propagate(False)\n ctk.CTkLabel(buf_frame, text=\"Buffer Status\", font=(\"Consolas\", 11, \"bold\"),\n text_color=C[\"text\"]).pack(pady=(8, 4))\n self._buf_progress = ctk.CTkProgressBar(buf_frame, width=200)\n self._buf_progress.pack(pady=4, padx=12)\n self._buf_progress.set(0)\n self._buf_lbl = ctk.CTkLabel(\n buf_frame, text=\"0 / 24 NOT READY\", font=(\"Consolas\", 10),\n text_color=C[\"red\"]\n )\n self._buf_lbl.pack()\n\n # Prediction big number\n pred_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8, width=200)\n pred_frame.pack(side=\"left\", padx=8, fill=\"y\")\n pred_frame.pack_propagate(False)\n ctk.CTkLabel(pred_frame, text=\"ML Prediction\", font=(\"Consolas\", 11, \"bold\"),\n text_color=C[\"text\"]).pack(pady=(8, 2))\n self._pred_big = ctk.CTkLabel(\n pred_frame, text=\"—\", font=(\"Consolas\", 28, \"bold\"),\n text_color=C[\"blue\"]\n )\n self._pred_big.pack(pady=4)\n self._pred_actual = ctk.CTkLabel(\n pred_frame, text=\"Actual: —\", font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"]\n )\n self._pred_actual.pack()\n\n # Raw metrics panel\n raw_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8)\n raw_frame.pack(side=\"left\", fill=\"both\", expand=True, padx=8)\n ctk.CTkLabel(raw_frame, text=\"Current Metrics\", font=(\"Consolas\", 11, \"bold\"),\n text_color=C[\"text\"]).pack(pady=(8, 4))\n self._raw_labels: dict[str, ctk.CTkLabel] = {}\n for metric in [\"cpu_usage\", \"mem_util\", \"net_in\", \"net_out\", \"disk_io\"]:\n lbl = ctk.CTkLabel(raw_frame, text=f\"{metric}: —\",\n font=(\"Consolas\", 10), text_color=C[\"text_dim\"])\n lbl.pack(anchor=\"w\", padx=12)\n self._raw_labels[metric] = lbl\n\n # Bottom: prediction history chart\n self._nn_fig, self._nn_ax = plt.subplots(1, 1, figsize=(10, 3), dpi=80)\n self._nn_canvas = embed_figure(tab, self._nn_fig)\n\n # --- Tab 2: Scaling Algorithm ---\n\n def _build_algo_tab(self):\n tab = self._tab_algo\n\n # 9-step pipeline\n pipe = ctk.CTkFrame(tab, fg_color=\"transparent\")\n pipe.pack(fill=\"x\", padx=4, pady=4)\n\n step_names = [\n \"EMA\", \"Hysteresis\", \"Desired\", \"Clamp\",\n \"Rate Limit\", \"Cooldown\", \"Stabilize\", \"No Change?\", \"Commit\"\n ]\n self._algo_blocks: list[PipelineBlock] = []\n for i, name in enumerate(step_names):\n if i > 0:\n ctk.CTkLabel(pipe, text=\"→\", font=(\"Consolas\", 14, \"bold\"),\n text_color=C[\"text_dim\"]).pack(side=\"left\", padx=1)\n block = PipelineBlock(pipe, i + 1, name)\n block.pack(side=\"left\", padx=2)\n self._algo_blocks.append(block)\n\n # State panel\n state_frame = ctk.CTkFrame(tab, fg_color=C[\"bg_card\"], corner_radius=8, height=40)\n state_frame.pack(fill=\"x\", padx=8, pady=4)\n self._state_lbl = ctk.CTkLabel(\n state_frame,\n text=\"Cooldown UP: — | Cooldown DOWN: — | Consec. Below: — | Direction: —\",\n font=(\"Consolas\", 10), text_color=C[\"text_dim\"]\n )\n self._state_lbl.pack(pady=6)\n\n # Charts: CPU + Replicas side by side\n charts = ctk.CTkFrame(tab, fg_color=\"transparent\")\n charts.pack(fill=\"both\", expand=True, padx=4, pady=4)\n\n left = ctk.CTkFrame(charts, fg_color=\"transparent\")\n left.pack(side=\"left\", fill=\"both\", expand=True)\n self._algo_cpu_fig, self._algo_cpu_ax = plt.subplots(1, 1, figsize=(5.5, 3), dpi=80)\n self._algo_cpu_canvas = embed_figure(left, self._algo_cpu_fig)\n\n right = ctk.CTkFrame(charts, fg_color=\"transparent\")\n right.pack(side=\"left\", fill=\"both\", expand=True)\n self._algo_rep_fig, self._algo_rep_ax = plt.subplots(1, 1, figsize=(5.5, 3), dpi=80)\n self._algo_rep_canvas = embed_figure(right, self._algo_rep_fig)\n\n # Decision log\n self._decision_log = ctk.CTkTextbox(\n tab, height=100, font=(\"Consolas\", 9), fg_color=C[\"bg\"],\n text_color=C[\"text_dim\"]\n )\n self._decision_log.pack(fill=\"x\", padx=8, pady=(0, 4))\n\n # --- Tab 3: Experiment Comparison ---\n\n def _build_exp_tab(self):\n tab = self._tab_exp\n\n # Main comparison chart\n self._exp_fig, self._exp_axes = plt.subplots(2, 1, figsize=(10, 5), dpi=80,\n gridspec_kw={\"height_ratios\": [2, 1]})\n self._exp_canvas = embed_figure(tab, self._exp_fig)\n\n # KPI cards grid\n cards_frame = ctk.CTkFrame(tab, fg_color=\"transparent\")\n cards_frame.pack(fill=\"x\", padx=8, pady=4)\n\n kpi_names = [\n \"Avg Replicas\", \"Replica-Min\", \"SLA Violations\", \"Scaling Events\",\n \"Over-Prov %\", \"Under-Prov %\", \"Reaction Time\", \"Cost Savings %\"\n ]\n self._kpi_cards: list[KPICard] = []\n for i, name in enumerate(kpi_names):\n card = KPICard(cards_frame, name)\n card.grid(row=i // 4, column=i % 4, padx=4, pady=4, sticky=\"nsew\")\n self._kpi_cards.append(card)\n for c in range(4):\n cards_frame.columnconfigure(c, weight=1)\n\n # -------------------------------------------------------------------\n # Update all visuals\n # -------------------------------------------------------------------\n\n def _update_all(self):\n sc = self._scenarios[self._current_scenario_idx]\n total = len(sc.actual_cpu)\n self._step_lbl.configure(text=f\"Step: {self._step} / {total}\")\n\n # Highlight active scenario button\n for i, btn in enumerate(self._scenario_btns):\n btn.configure(\n fg_color=C[\"blue\"] if i == self._current_scenario_idx else C[\"bg_card\"]\n )\n\n self._update_nn_tab()\n self._update_algo_tab()\n self._update_exp_tab()\n\n def _update_nn_tab(self):\n n = self._step\n\n # Buffer status\n buf_fill = min(n, 24)\n self._buf_progress.set(buf_fill / 24)\n ready = n >= 24\n self._buf_lbl.configure(\n text=f\"{min(n, 54)} / 24 {'READY' if ready else 'FILLING...'}\",\n text_color=C[\"green\"] if ready else C[\"red\"]\n )\n\n # Current prediction\n if n > 0:\n pred = self._predicted_trace[-1]\n actual = self._actual_trace[-1]\n color = C[\"green\"] if pred < 42 else C[\"yellow\"] if pred < 66 else C[\"orange\"] if pred < 85 else C[\"red\"]\n self._pred_big.configure(text=f\"{pred:.1f}%\", text_color=color)\n self._pred_actual.configure(text=f\"Actual: {actual:.1f}%\")\n\n # Raw metrics (simulated from CPU)\n self._raw_labels[\"cpu_usage\"].configure(text=f\"cpu_usage: {actual:.1f}%\")\n self._raw_labels[\"mem_util\"].configure(text=f\"mem_util: {actual * 0.6:.1f}%\")\n self._raw_labels[\"net_in\"].configure(text=f\"net_in: {actual * 1.2:.0f} KB/s\")\n self._raw_labels[\"net_out\"].configure(text=f\"net_out: {actual * 0.8:.0f} KB/s\")\n self._raw_labels[\"disk_io\"].configure(text=f\"disk_io: {actual * 0.3:.1f}%\")\n\n # Prediction history chart\n self._nn_ax.clear()\n if n > 1:\n x = range(max(0, n - 80), n)\n a_slice = self._actual_trace[max(0, n - 80):]\n p_slice = self._predicted_trace[max(0, n - 80):]\n self._nn_ax.plot(x, a_slice, color=C[\"green\"], lw=1.5, label=\"Actual CPU\")\n self._nn_ax.plot(x, p_slice, color=C[\"purple\"], lw=1.5, ls=\"--\", label=\"Predicted\")\n self._nn_ax.axhline(60, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.5)\n self._nn_ax.set_ylabel(\"CPU %\")\n self._nn_ax.set_ylim(0, 100)\n self._nn_ax.legend(loc=\"upper right\", fontsize=8)\n self._nn_ax.grid(True, alpha=0.2)\n self._nn_ax.set_title(\"Prediction History (last 80 steps)\", fontsize=10)\n self._nn_fig.tight_layout()\n self._nn_canvas.draw_idle()\n\n def _update_algo_tab(self):\n n = self._step\n if n == 0:\n for block in self._algo_blocks:\n block.update(\"—\", \"skipped\")\n return\n\n # Get current step details\n pred = self._predicted_trace[-1]\n smoothed = self._smoothed_trace[-1]\n ml_replicas = self._ml_replicas_trace[-1]\n prev_replicas = self._ml_replicas_trace[-2] if n > 1 else 2\n\n target = self._target_var.get()\n up_thresh = target * self._up_thresh_var.get()\n down_thresh = target * self._down_thresh_var.get()\n\n # Determine direction\n if smoothed > up_thresh:\n direction = \"UP\"\n elif smoothed < down_thresh:\n direction = \"DOWN\"\n else:\n direction = \"HOLD\"\n\n # Update blocks\n self._algo_blocks[0].update(f\"{pred:.1f}→{smoothed:.1f}%\", \"active\")\n self._algo_blocks[1].update(\n f\"{smoothed:.0f}% {'>' if direction == 'UP' else '<' if direction == 'DOWN' else 'in'} \"\n f\"[{down_thresh:.0f},{up_thresh:.0f}]\\n→{direction}\",\n \"active\" if direction != \"HOLD\" else \"blocked\"\n )\n\n if direction == \"HOLD\":\n for i in range(2, 9):\n self._algo_blocks[i].update(\"—\", \"skipped\")\n else:\n raw_desired = math.ceil(prev_replicas * smoothed / target)\n if direction == \"UP\":\n raw_desired = math.ceil(raw_desired * (1 + self._safety_var.get()))\n self._algo_blocks[2].update(f\"{prev_replicas}×{smoothed:.0f}/{target:.0f}\\n={raw_desired}\", \"active\")\n clamped = max(2, min(8, raw_desired))\n self._algo_blocks[3].update(f\"[2,8]→{clamped}\", \"active\")\n rate_lim = min(clamped, prev_replicas + 2) if direction == \"UP\" else max(clamped, prev_replicas - 1)\n self._algo_blocks[4].update(f\"±limit→{rate_lim}\", \"active\")\n self._algo_blocks[5].update(\"OFF ✓\", \"active\")\n self._algo_blocks[6].update(\"N/A\" if direction == \"UP\" else \"check\", \"active\")\n\n executed = ml_replicas != prev_replicas\n self._algo_blocks[7].update(\n f\"{ml_replicas}{'≠' if executed else '='}{prev_replicas}\",\n \"active\" if executed else \"blocked\"\n )\n if executed:\n self._algo_blocks[8].update(f\"{prev_replicas}→{ml_replicas}\", \"executed\")\n else:\n self._algo_blocks[8].update(\"hold\", \"blocked\")\n\n # State panel\n sim = self._ml_sim\n self._state_lbl.configure(\n text=f\"Direction: {direction} | \"\n f\"Consec. Below: {sim._consecutive_below} | \"\n f\"Replicas: {ml_replicas} | \"\n f\"Smoothed: {smoothed:.1f}%\"\n )\n\n # CPU chart\n self._algo_cpu_ax.clear()\n if n > 1:\n x = range(n)\n self._algo_cpu_ax.plot(x, self._smoothed_trace, color=C[\"blue\"], lw=1.5, label=\"Smoothed\")\n self._algo_cpu_ax.plot(x, self._actual_trace, color=C[\"green\"], lw=1, alpha=0.5, label=\"Actual\")\n self._algo_cpu_ax.axhline(up_thresh, color=C[\"red\"], ls=\"--\", lw=0.8, label=f\"Up {up_thresh:.0f}%\")\n self._algo_cpu_ax.axhline(down_thresh, color=C[\"orange\"], ls=\"--\", lw=0.8, label=f\"Down {down_thresh:.0f}%\")\n self._algo_cpu_ax.axhline(target, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.6)\n self._algo_cpu_ax.axhspan(down_thresh, up_thresh, alpha=0.08, color=C[\"yellow\"])\n self._algo_cpu_ax.set_ylim(0, 100)\n self._algo_cpu_ax.set_ylabel(\"CPU %\")\n self._algo_cpu_ax.legend(loc=\"upper right\", fontsize=7)\n self._algo_cpu_ax.grid(True, alpha=0.2)\n self._algo_cpu_ax.set_title(\"CPU + Thresholds\", fontsize=10)\n self._algo_cpu_fig.tight_layout()\n self._algo_cpu_canvas.draw_idle()\n\n # Replica chart\n self._algo_rep_ax.clear()\n if n > 1:\n x = range(n)\n self._algo_rep_ax.step(x, self._ml_replicas_trace, color=C[\"blue\"], lw=2, where=\"post\", label=\"ML Replicas\")\n self._algo_rep_ax.step(x, self._hpa_replicas_trace, color=C[\"red\"], lw=1.5, where=\"post\", alpha=0.6, label=\"HPA Replicas\")\n self._algo_rep_ax.axhline(2, color=C[\"text_dim\"], ls=\":\", lw=0.7, alpha=0.5)\n self._algo_rep_ax.axhline(8, color=C[\"text_dim\"], ls=\":\", lw=0.7, alpha=0.5)\n self._algo_rep_ax.set_ylim(0, 10)\n self._algo_rep_ax.set_ylabel(\"Replicas\")\n self._algo_rep_ax.legend(loc=\"upper right\", fontsize=7)\n self._algo_rep_ax.grid(True, alpha=0.2)\n self._algo_rep_ax.set_title(\"Replica Count\", fontsize=10)\n self._algo_rep_fig.tight_layout()\n self._algo_rep_canvas.draw_idle()\n\n # Decision log\n self._decision_log.configure(state=\"normal\")\n self._decision_log.delete(\"1.0\", \"end\")\n # Show last 20 entries\n for line in self._decisions_log[-20:]:\n self._decision_log.insert(\"end\", line + \"\\n\")\n self._decision_log.configure(state=\"disabled\")\n self._decision_log.see(\"end\")\n\n def _update_exp_tab(self):\n if not hasattr(self, \"_full_metrics\"):\n return\n metrics = self._full_metrics\n history = self._history\n\n # Top chart: Actual CPU + replicas\n ax1, ax2 = self._exp_axes\n ax1.clear()\n ax2.clear()\n\n n = len(history)\n if n > 0:\n x = range(n)\n actual = [r.actual_cpu for r in history]\n hpa_r = [r.hpa_replicas for r in history]\n ml_r = [r.ml_replicas for r in history]\n\n # CPU trace (background)\n ax1.fill_between(x, actual, alpha=0.15, color=C[\"text_dim\"])\n ax1.plot(x, actual, color=C[\"text_dim\"], lw=0.8, alpha=0.5, label=\"Actual CPU\")\n\n # Replicas\n ax1.step(x, [r * 10 for r in hpa_r], color=C[\"red\"], lw=1.5,\n where=\"post\", label=\"HPA Replicas (×10)\")\n ax1.step(x, [r * 10 for r in ml_r], color=C[\"blue\"], lw=2,\n where=\"post\", label=\"ML Replicas (×10)\")\n\n # SLA threshold\n target = self._target_var.get()\n ax1.axhline(target * 1.2, color=C[\"red\"], ls=\":\", lw=0.8, alpha=0.5, label=\"SLA Limit\")\n\n ax1.set_ylabel(\"CPU % / Replicas×10\")\n ax1.set_ylim(0, 100)\n ax1.legend(loc=\"upper right\", fontsize=7, ncol=2)\n ax1.set_title(f\"Experiment: {self._scenarios[self._current_scenario_idx].name}\", fontsize=11)\n ax1.grid(True, alpha=0.2)\n\n # Bottom chart: replica diff\n diff = [m - h for m, h in zip(ml_r, hpa_r)]\n colors_bar = [C[\"green\"] if d < 0 else C[\"red\"] if d > 0 else C[\"text_dim\"] for d in diff]\n ax2.bar(x, diff, color=colors_bar, alpha=0.7, width=1.0)\n ax2.axhline(0, color=C[\"text_dim\"], lw=0.5)\n ax2.set_ylabel(\"ML - HPA\")\n ax2.set_xlabel(\"Step\")\n ax2.grid(True, alpha=0.2)\n ax2.set_title(\"Replica Difference (negative = ML uses fewer)\", fontsize=9)\n\n self._exp_fig.tight_layout()\n self._exp_canvas.draw_idle()\n\n # KPI cards\n self._kpi_cards[0].set_values(f\"{metrics.hpa_avg_replicas:.1f}\", f\"{metrics.ml_avg_replicas:.1f}\")\n self._kpi_cards[1].set_values(f\"{metrics.hpa_replica_minutes:.0f}\", f\"{metrics.ml_replica_minutes:.0f}\")\n self._kpi_cards[2].set_values(f\"{metrics.hpa_sla_violations}\", f\"{metrics.ml_sla_violations}\")\n self._kpi_cards[3].set_values(f\"{metrics.hpa_scaling_events}\", f\"{metrics.ml_scaling_events}\")\n self._kpi_cards[4].set_values(f\"{metrics.hpa_over_provisioning_pct:.1f}%\", f\"{metrics.ml_over_provisioning_pct:.1f}%\")\n self._kpi_cards[5].set_values(f\"{metrics.hpa_under_provisioning_pct:.1f}%\", f\"{metrics.ml_under_provisioning_pct:.1f}%\")\n self._kpi_cards[6].set_values(f\"{metrics.hpa_avg_reaction_time:.1f}\", f\"{metrics.ml_avg_reaction_time:.1f}\")\n self._kpi_cards[7].set_values(f\"—\", f\"{metrics.cost_savings_pct:+.1f}%\")\n\n\n# ---------------------------------------------------------------------------\n# Entry point\n# ---------------------------------------------------------------------------\n\n\ndef main():\n app = ExperimentApp()\n app.mainloop()\n\n\nif __name__ == \"__main__\":\n main()\n", | |
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| } | |
| } | |
| [2026-02-07T18:14:45.169Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-6", | |
| "id": "msg_01Psu7i5ogK61xLQY6ytLmuH", | |
| "type": "message", | |
| "role": "assistant", | |
| "content": [ | |
| { | |
| "type": "text", | |
| "text": "Now I'll write the enhanced GUI with all improvements. This is a significant enhancement to the existing file." | |
| } | |
| ], | |
| "stop_reason": null, | |
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| "usage": { | |
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| } | |
| [2026-02-07T18:22:10.050Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-6", | |
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| "role": "assistant", | |
| "content": [ | |
| { | |
| "type": "text", | |
| "text": "Now I'll write the enhanced GUI. I'll add multiple improvements:\n\n1. **Tooltip system** - hover over pipeline blocks and KPI cards for explanations\n2. **Animated status indicators** - pulsing dots, color transitions\n3. **Prediction accuracy panel** - MAE, RMSE, R² computed live\n4. **Scenario description panel** - shows info about current scenario\n5. **Timeline progress bar** - visual timeline with time labels\n6. **Keyboard shortcuts** - Space, Right, R, number keys\n7. **Export functionality** - save charts/data\n8. **Min/max replica sliders** - additional parameters\n9. **Notification feed** - scaling event notifications\n10. **Better KPI cards** - with color-coded delta indicators" | |
| } | |
| ], | |
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| [2026-02-07T18:22:10.051Z] [INFO] { | |
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| "model": "claude-opus-4-6", | |
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| "name": "Write", | |
| "input": { | |
| "file_path": "/tmp/gh-issue-solver-1770487990892/scripts/experiment_gui.py" | |
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| [2026-02-07T18:22:10.052Z] [INFO] { | |
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| "type": "tool_result", | |
| "content": "<tool_use_error>InputValidationError: Write failed due to the following issue:\nThe required parameter `content` is missing</tool_use_error>", | |
| "is_error": true, | |
| "tool_use_id": "toolu_018UpcgGvimNsBeUFo3kCpDD" | |
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| "tool_use_result": "InputValidationError: [\n {\n \"expected\": \"string\",\n \"code\": \"invalid_type\",\n \"path\": [\n \"content\"\n ],\n \"message\": \"Invalid input: expected string, received undefined\"\n }\n]" | |
| } | |
| [2026-02-07T18:26:05.633Z] [INFO] { | |
| "type": "assistant", | |
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| "model": "claude-opus-4-6", | |
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| "file_path": "/tmp/gh-issue-solver-1770487990892/scripts/experiment_gui.py", | |
| "content": "\"\"\"ML Autoscaler — Experiment Dashboard (CustomTkinter).\n\nInteractive visualization of:\n - Neural network internals (buffer, features, ONNX inference)\n - Scaling algorithm 9-step pipeline\n - HPA vs ML autoscaler comparison\n\nFeatures:\n - Keyboard shortcuts (Space=play/pause, Right=step, R=reset, 1-4=scenario)\n - Tooltips on pipeline blocks and KPI cards\n - Live prediction accuracy metrics (MAE, RMSE)\n - Animated status indicators\n - Scenario description panel\n - Timeline progress bar\n - Export charts / data to file\n - Notification feed for scaling events\n - Configurable min/max replica range\n\nUsage:\n python scripts/experiment_gui.py\n\"\"\"\n\nfrom __future__ import annotations\n\nimport csv\nimport math\nimport sys\nimport time\nfrom pathlib import Path\nfrom tkinter import filedialog\n\nimport customtkinter as ctk\nimport matplotlib\nimport numpy as np\n\nmatplotlib.use(\"TkAgg\")\nimport matplotlib.pyplot as plt # noqa: E402\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg # noqa: E402\nfrom matplotlib.figure import Figure # noqa: E402\n\n# Add project root to path for imports\nROOT = Path(__file__).resolve().parent.parent\nsys.path.insert(0, str(ROOT / \"src\"))\nsys.path.insert(0, str(ROOT))\n\nfrom scripts.experiment_scenarios import Scenario, get_all_scenarios # noqa: E402\nfrom scripts.experiment_simulator import ( # noqa: E402\n ExperimentRunner,\n MLAutoscalerSimulator,\n MLConfig,\n StepRecord,\n)\n\n# ---------------------------------------------------------------------------\n# Color palette (unified with Grafana dashboards)\n# ---------------------------------------------------------------------------\n\nC = {\n \"bg\": \"#1a1a2e\",\n \"bg_card\": \"#16213e\",\n \"sidebar\": \"#0f3460\",\n \"accent\": \"#e94560\",\n \"green\": \"#73BF69\",\n \"yellow\": \"#FADE2A\",\n \"orange\": \"#FF9830\",\n \"red\": \"#F2495C\",\n \"blue\": \"#5794F2\",\n \"purple\": \"#B877D9\",\n \"cyan\": \"#8AB8FF\",\n \"text\": \"#e0e0e0\",\n \"text_dim\": \"#8892b0\",\n \"grid\": \"#2a2a4a\",\n \"success\": \"#73BF69\",\n \"warning\": \"#FADE2A\",\n \"danger\": \"#F2495C\",\n}\n\n# Matplotlib dark style\nplt.rcParams.update(\n {\n \"figure.facecolor\": C[\"bg_card\"],\n \"axes.facecolor\": C[\"bg\"],\n \"axes.edgecolor\": C[\"grid\"],\n \"axes.labelcolor\": C[\"text\"],\n \"text.color\": C[\"text\"],\n \"xtick.color\": C[\"text_dim\"],\n \"ytick.color\": C[\"text_dim\"],\n \"grid.color\": C[\"grid\"],\n \"grid.alpha\": 0.3,\n \"font.size\": 9,\n \"legend.facecolor\": C[\"bg_card\"],\n \"legend.edgecolor\": C[\"grid\"],\n }\n)\n\n\n# ---------------------------------------------------------------------------\n# Utility: embed a matplotlib figure in a CTkFrame\n# ---------------------------------------------------------------------------\n\n\ndef embed_figure(parent: ctk.CTkFrame, fig: Figure) -> FigureCanvasTkAgg:\n \"\"\"Embed a matplotlib Figure into a CustomTkinter frame.\"\"\"\n canvas = FigureCanvasTkAgg(fig, master=parent)\n canvas.draw()\n canvas.get_tk_widget().pack(fill=\"both\", expand=True)\n return canvas\n\n\n# ---------------------------------------------------------------------------\n# Tooltip widget\n# ---------------------------------------------------------------------------\n\n\nclass Tooltip:\n \"\"\"Hover tooltip for any widget.\"\"\"\n\n def __init__(self, widget: ctk.CTkBaseClass, text: str) -> None:\n self._widget = widget\n self._text = text\n self._tip_window: ctk.CTkToplevel | None = None\n widget.bind(\"<Enter>\", self._show)\n widget.bind(\"<Leave>\", self._hide)\n\n def _show(self, event: object = None) -> None:\n if self._tip_window:\n return\n x = self._widget.winfo_rootx() + 20\n y = self._widget.winfo_rooty() + self._widget.winfo_height() + 4\n tw = ctk.CTkToplevel(self._widget)\n tw.wm_overrideredirect(True)\n tw.wm_geometry(f\"+{x}+{y}\")\n tw.attributes(\"-topmost\", True)\n lbl = ctk.CTkLabel(\n tw,\n text=self._text,\n font=(\"Consolas\", 10),\n fg_color=C[\"bg_card\"],\n text_color=C[\"text\"],\n corner_radius=6,\n wraplength=280,\n )\n lbl.pack(padx=6, pady=4)\n self._tip_window = tw\n\n def _hide(self, event: object = None) -> None:\n if self._tip_window:\n self._tip_window.destroy()\n self._tip_window = None\n\n\n# ---------------------------------------------------------------------------\n# KPI Card widget (enhanced with delta indicator)\n# ---------------------------------------------------------------------------\n\n\nclass KPICard(ctk.CTkFrame):\n \"\"\"Compact KPI card: title + HPA value + ML value + delta arrow.\"\"\"\n\n def __init__(self, master: ctk.CTkFrame, title: str, tooltip: str = \"\", **kw: object):\n super().__init__(master, fg_color=C[\"bg_card\"], corner_radius=8, **kw)\n self._title_lbl = ctk.CTkLabel(\n self, text=title, font=(\"Consolas\", 11), text_color=C[\"text_dim\"]\n )\n self._title_lbl.pack(pady=(6, 2))\n\n row = ctk.CTkFrame(self, fg_color=\"transparent\")\n row.pack(pady=(0, 2))\n self._hpa_lbl = ctk.CTkLabel(\n row, text=\"HPA: --\", font=(\"Consolas\", 13, \"bold\"), text_color=C[\"red\"]\n )\n self._hpa_lbl.pack(side=\"left\", padx=6)\n self._ml_lbl = ctk.CTkLabel(\n row, text=\"ML: --\", font=(\"Consolas\", 13, \"bold\"), text_color=C[\"blue\"]\n )\n self._ml_lbl.pack(side=\"left\", padx=6)\n\n # Delta indicator\n self._delta_lbl = ctk.CTkLabel(\n self, text=\"\", font=(\"Consolas\", 9), text_color=C[\"text_dim\"]\n )\n self._delta_lbl.pack(pady=(0, 4))\n\n if tooltip:\n Tooltip(self, tooltip)\n\n def set_values(\n self,\n hpa_val: str,\n ml_val: str,\n better: str = \"lower\",\n ) -> None:\n \"\"\"Update card values with automatic delta comparison.\n\n Args:\n hpa_val: HPA metric value string.\n ml_val: ML metric value string.\n better: Direction that is \"better\" — \"lower\" or \"higher\".\n \"\"\"\n self._hpa_lbl.configure(text=f\"HPA: {hpa_val}\")\n self._ml_lbl.configure(text=f\"ML: {ml_val}\")\n\n # Parse numeric values for delta\n try:\n hpa_num = float(hpa_val.replace(\"%\", \"\").replace(\"+\", \"\").replace(\",\", \"\"))\n ml_num = float(ml_val.replace(\"%\", \"\").replace(\"+\", \"\").replace(\",\", \"\"))\n diff = ml_num - hpa_num\n if abs(diff) < 0.01:\n self._delta_lbl.configure(text=\"= equal\", text_color=C[\"text_dim\"])\n else:\n is_better = (diff < 0) if better == \"lower\" else (diff > 0)\n arrow = \"v\" if diff < 0 else \"^\"\n color = C[\"green\"] if is_better else C[\"red\"]\n self._delta_lbl.configure(\n text=f\"{arrow} {abs(diff):.1f}\", text_color=color\n )\n except (ValueError, AttributeError):\n self._delta_lbl.configure(text=\"\", text_color=C[\"text_dim\"])\n\n\n# ---------------------------------------------------------------------------\n# Pipeline Step block (enhanced with tooltip)\n# ---------------------------------------------------------------------------\n\n\nclass PipelineBlock(ctk.CTkFrame):\n \"\"\"One step in the 9-step scaling algorithm visualization.\"\"\"\n\n # Descriptions for each pipeline step\n STEP_TOOLTIPS = {\n 1: \"EMA: Exponential moving average smoothing. \"\n \"Smoothes predicted CPU to remove noise.\",\n 2: \"Hysteresis: Dead-zone check. \"\n \"Determines UP/DOWN/HOLD based on thresholds.\",\n 3: \"Desired: Calculates target replica count \"\n \"using ceil(current * smoothed / target).\",\n 4: \"Clamp: Restricts replicas to [min, max] range.\",\n 5: \"Rate Limit: Limits change speed. \"\n \"Max +2 up, -1 down per step.\",\n 6: \"Cooldown: Prevents rapid re-scaling. \"\n \"60s for up, 300s for down.\",\n 7: \"Stabilize: Requires 3 consecutive low \"\n \"readings before scaling down.\",\n 8: \"No Change: Skips execution if desired \"\n \"equals current replicas.\",\n 9: \"Commit: Applies the scaling decision \"\n \"and updates cooldown timers.\",\n }\n\n def __init__(self, master: ctk.CTkFrame, step_num: int, title: str, **kw: object):\n super().__init__(\n master,\n fg_color=C[\"bg_card\"],\n corner_radius=6,\n border_width=1,\n border_color=C[\"grid\"],\n **kw,\n )\n self.configure(width=115, height=80)\n self.pack_propagate(False)\n self._step_num = step_num\n ctk.CTkLabel(\n self,\n text=f\"Step {step_num}\",\n font=(\"Consolas\", 8),\n text_color=C[\"text_dim\"],\n ).pack(pady=(4, 0))\n ctk.CTkLabel(\n self,\n text=title,\n font=(\"Consolas\", 9, \"bold\"),\n text_color=C[\"text\"],\n ).pack()\n self._value_lbl = ctk.CTkLabel(\n self,\n text=\"\\u2014\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n wraplength=100,\n )\n self._value_lbl.pack(pady=(2, 4))\n\n # Add tooltip\n tooltip_text = self.STEP_TOOLTIPS.get(step_num, \"\")\n if tooltip_text:\n Tooltip(self, tooltip_text)\n\n def update(self, text: str, status: str = \"active\") -> None:\n \"\"\"Update block display text and status color.\"\"\"\n colors = {\n \"active\": C[\"green\"],\n \"blocked\": C[\"yellow\"],\n \"skipped\": C[\"text_dim\"],\n \"executed\": C[\"accent\"],\n }\n self._value_lbl.configure(\n text=text, text_color=colors.get(status, C[\"text\"])\n )\n border = {\n \"active\": C[\"green\"],\n \"blocked\": C[\"yellow\"],\n \"executed\": C[\"accent\"],\n }\n self.configure(border_color=border.get(status, C[\"grid\"]))\n\n\n# ---------------------------------------------------------------------------\n# Status Indicator (animated dot)\n# ---------------------------------------------------------------------------\n\n\nclass StatusDot(ctk.CTkFrame):\n \"\"\"Animated status dot indicator.\"\"\"\n\n def __init__(self, master: ctk.CTkFrame, **kw: object):\n super().__init__(master, fg_color=\"transparent\", **kw)\n self._dot = ctk.CTkLabel(\n self, text=\"\\u25cf\", font=(\"Consolas\", 14), text_color=C[\"text_dim\"]\n )\n self._dot.pack(side=\"left\", padx=2)\n self._label = ctk.CTkLabel(\n self, text=\"IDLE\", font=(\"Consolas\", 10), text_color=C[\"text_dim\"]\n )\n self._label.pack(side=\"left\", padx=2)\n self._pulse_on = True\n\n def set_status(self, status: str, text: str) -> None:\n \"\"\"Update status indicator.\n\n Args:\n status: One of 'idle', 'running', 'ready', 'warning'.\n text: Label text to display.\n \"\"\"\n color_map = {\n \"idle\": C[\"text_dim\"],\n \"running\": C[\"green\"],\n \"ready\": C[\"blue\"],\n \"warning\": C[\"yellow\"],\n \"error\": C[\"red\"],\n }\n color = color_map.get(status, C[\"text_dim\"])\n self._dot.configure(text_color=color)\n self._label.configure(text=text, text_color=color)\n\n\n# ---------------------------------------------------------------------------\n# Notification Item\n# ---------------------------------------------------------------------------\n\n\nclass NotificationItem(ctk.CTkFrame):\n \"\"\"Single notification entry in the feed.\"\"\"\n\n def __init__(\n self,\n master: ctk.CTkFrame,\n text: str,\n level: str = \"info\",\n **kw: object,\n ):\n super().__init__(\n master, fg_color=C[\"bg_card\"], corner_radius=4, height=24, **kw\n )\n self.pack_propagate(False)\n color = {\n \"info\": C[\"blue\"],\n \"up\": C[\"green\"],\n \"down\": C[\"orange\"],\n \"warning\": C[\"yellow\"],\n }.get(level, C[\"text_dim\"])\n icon = {\n \"info\": \"\\u25b6\",\n \"up\": \"\\u25b2\",\n \"down\": \"\\u25bc\",\n \"warning\": \"\\u26a0\",\n }.get(level, \"\\u25cf\")\n ctk.CTkLabel(\n self,\n text=f\" {icon} {text}\",\n font=(\"Consolas\", 9),\n text_color=color,\n anchor=\"w\",\n ).pack(fill=\"x\", padx=4, pady=2)\n\n\n# ---------------------------------------------------------------------------\n# Main Application\n# ---------------------------------------------------------------------------\n\n\nclass ExperimentApp(ctk.CTk):\n \"\"\"Main GUI application for the ML Autoscaler experiment dashboard.\"\"\"\n\n def __init__(self) -> None:\n super().__init__()\n self.title(\"ML Autoscaler \\u2014 Experiment Dashboard\")\n self.geometry(\"1480x960\")\n ctk.set_appearance_mode(\"dark\")\n\n # State\n self._scenarios: list[Scenario] = []\n self._current_scenario_idx = 0\n self._step = 0\n self._playing = False\n self._speed_ms = 200\n self._history: list[StepRecord] = []\n\n # ML simulator state for step-by-step\n self._ml_sim: MLAutoscalerSimulator | None = None\n self._hpa_replicas_trace: list[int] = []\n self._ml_replicas_trace: list[int] = []\n self._actual_trace: list[float] = []\n self._predicted_trace: list[float] = []\n self._smoothed_trace: list[float] = []\n self._decisions_log: list[str] = []\n\n # Notifications queue\n self._notifications: list[tuple[str, str]] = []\n\n # Build UI\n self._build_controls()\n self._build_sidebar()\n self._build_tabview()\n\n # Bind keyboard shortcuts\n self._bind_shortcuts()\n\n # Load data\n self._load_scenarios()\n\n # -------------------------------------------------------------------\n # Keyboard shortcuts\n # -------------------------------------------------------------------\n\n def _bind_shortcuts(self) -> None:\n \"\"\"Bind keyboard shortcuts for common actions.\"\"\"\n self.bind(\"<space>\", lambda e: self._toggle_play())\n self.bind(\"<Right>\", lambda e: self._do_step())\n self.bind(\"<r>\", lambda e: self._do_reset())\n self.bind(\"<R>\", lambda e: self._do_reset())\n self.bind(\"<1>\", lambda e: self._select_scenario(0))\n self.bind(\"<2>\", lambda e: self._select_scenario(1))\n self.bind(\"<3>\", lambda e: self._select_scenario(2))\n self.bind(\"<4>\", lambda e: self._select_scenario(3))\n self.bind(\n \"<Control-e>\", lambda e: self._export_data()\n )\n self.bind(\n \"<Control-s>\", lambda e: self._export_charts()\n )\n\n # -------------------------------------------------------------------\n # Data loading\n # -------------------------------------------------------------------\n\n def _load_scenarios(self) -> None:\n try:\n self._scenarios = get_all_scenarios(\n ROOT / \"data\" / \"processed\" / \"test.csv\",\n ROOT / \"data\" / \"processed\" / \"scaler.pkl\",\n ROOT / \"data\" / \"processed\" / \"metadata.json\",\n )\n except Exception:\n self._scenarios = [self._synthetic_fallback()]\n # Ensure we have at least 4 scenario buttons\n while len(self._scenarios) < 4:\n self._scenarios.append(self._synthetic_fallback())\n self._select_scenario(0)\n\n def _synthetic_fallback(self) -> Scenario:\n \"\"\"Create a simple synthetic scenario if data files are missing.\"\"\"\n n = 200\n rng = np.random.RandomState(42)\n t = np.linspace(0, 4 * np.pi, n)\n actual = 50 + 25 * np.sin(t) + rng.normal(0, 3, n)\n predicted = np.roll(actual, -3)\n predicted[-3:] = actual[-3:]\n predicted += rng.normal(0, 2, n)\n return Scenario(\n name=\"synthetic_demo\",\n description=\"Synthetic sine wave demo for testing\",\n actual_cpu=np.clip(actual, 10, 100),\n predicted_cpu=np.clip(predicted, 10, 100),\n duration_hours=n * 5 / 60,\n )\n\n def _select_scenario(self, idx: int) -> None:\n if idx >= len(self._scenarios):\n return\n self._current_scenario_idx = idx\n self._reset_simulation()\n self._update_all()\n\n def _do_reset(self) -> None:\n \"\"\"Reset simulation (callable from shortcuts).\"\"\"\n self._reset_simulation()\n self._update_all()\n\n def _reset_simulation(self) -> None:\n self._step = 0\n self._playing = False\n self._notifications = []\n sc = self._scenarios[self._current_scenario_idx]\n\n # Run full experiment for Tab 3\n runner = ExperimentRunner(\n sc.actual_cpu,\n sc.predicted_cpu,\n ml_config=self._build_ml_config(),\n )\n self._history, self._full_metrics = runner.run()\n\n # Reset step-by-step state\n self._ml_sim = MLAutoscalerSimulator(self._build_ml_config())\n self._hpa_replicas_trace = []\n self._ml_replicas_trace = []\n self._actual_trace = []\n self._predicted_trace = []\n self._smoothed_trace = []\n self._decisions_log = []\n\n # Update play button state\n if hasattr(self, \"_play_btn\"):\n self._play_btn.configure(\n text=\"\\u25b6 Play\", fg_color=C[\"green\"]\n )\n\n def _build_ml_config(self) -> MLConfig:\n return MLConfig(\n target_utilization=self._target_var.get(),\n smoothing_alpha=self._alpha_var.get(),\n safety_margin=self._safety_var.get(),\n scale_up_threshold_factor=self._up_thresh_var.get(),\n scale_down_threshold_factor=self._down_thresh_var.get(),\n min_replicas=int(self._min_rep_var.get()),\n max_replicas=int(self._max_rep_var.get()),\n )\n\n # -------------------------------------------------------------------\n # Control bar (top)\n # -------------------------------------------------------------------\n\n def _build_controls(self) -> None:\n bar = ctk.CTkFrame(self, fg_color=C[\"bg_card\"], height=50)\n bar.pack(fill=\"x\", padx=8, pady=(8, 4))\n\n ctk.CTkLabel(\n bar,\n text=\"ML Autoscaler \\u2014 Experiment Dashboard\",\n font=(\"Consolas\", 16, \"bold\"),\n text_color=C[\"text\"],\n ).pack(side=\"left\", padx=12)\n\n # Status dot\n self._status_dot = StatusDot(bar)\n self._status_dot.pack(side=\"left\", padx=8)\n\n # Transport controls\n self._play_btn = ctk.CTkButton(\n bar,\n text=\"\\u25b6 Play\",\n width=90,\n fg_color=C[\"green\"],\n text_color=\"#000\",\n hover_color=\"#5ea554\",\n command=self._toggle_play,\n )\n self._play_btn.pack(side=\"left\", padx=4)\n\n ctk.CTkButton(\n bar,\n text=\"\\u23ed Step\",\n width=90,\n fg_color=C[\"blue\"],\n text_color=\"#000\",\n hover_color=\"#4678c9\",\n command=self._do_step,\n ).pack(side=\"left\", padx=4)\n\n ctk.CTkButton(\n bar,\n text=\"\\u21ba Reset\",\n width=90,\n fg_color=C[\"orange\"],\n text_color=\"#000\",\n hover_color=\"#d97e24\",\n command=self._do_reset,\n ).pack(side=\"left\", padx=4)\n\n # Export buttons\n ctk.CTkButton(\n bar,\n text=\"\\u2913 Export\",\n width=80,\n fg_color=C[\"purple\"],\n text_color=\"#fff\",\n hover_color=\"#9b5fbf\",\n command=self._export_data,\n ).pack(side=\"left\", padx=4)\n\n # Step counter + time display\n self._time_lbl = ctk.CTkLabel(\n bar,\n text=\"00:00\",\n font=(\"Consolas\", 11),\n text_color=C[\"text_dim\"],\n )\n self._time_lbl.pack(side=\"right\", padx=4)\n\n self._step_lbl = ctk.CTkLabel(\n bar,\n text=\"Step: 0 / 0\",\n font=(\"Consolas\", 12),\n text_color=C[\"text_dim\"],\n )\n self._step_lbl.pack(side=\"right\", padx=8)\n\n # Speed slider\n ctk.CTkLabel(bar, text=\"Speed:\", text_color=C[\"text_dim\"]).pack(\n side=\"right\"\n )\n self._speed_slider = ctk.CTkSlider(\n bar,\n from_=50,\n to=1000,\n width=120,\n command=lambda v: setattr(self, \"_speed_ms\", int(v)),\n )\n self._speed_slider.set(200)\n self._speed_slider.pack(side=\"right\", padx=4)\n\n # Shortcuts hint\n Tooltip(\n self._play_btn,\n \"Space: Play/Pause | Right: Step | R: Reset | 1-4: Scenario | \"\n \"Ctrl+E: Export data | Ctrl+S: Save charts\",\n )\n\n # -------------------------------------------------------------------\n # Timeline progress bar\n # -------------------------------------------------------------------\n\n def _build_timeline(self) -> None:\n \"\"\"Build timeline progress bar below tabs.\"\"\"\n frame = ctk.CTkFrame(self, fg_color=C[\"bg_card\"], height=30)\n frame.pack(fill=\"x\", padx=8, pady=(0, 2))\n frame.pack_propagate(False)\n\n self._timeline_bar = ctk.CTkProgressBar(\n frame, width=600, height=8, progress_color=C[\"blue\"]\n )\n self._timeline_bar.pack(side=\"left\", fill=\"x\", expand=True, padx=12, pady=10)\n self._timeline_bar.set(0)\n\n self._timeline_pct = ctk.CTkLabel(\n frame, text=\"0%\", font=(\"Consolas\", 9), text_color=C[\"text_dim\"]\n )\n self._timeline_pct.pack(side=\"right\", padx=8)\n\n def _toggle_play(self) -> None:\n self._playing = not self._playing\n self._play_btn.configure(\n text=\"\\u23f8 Pause\" if self._playing else \"\\u25b6 Play\",\n fg_color=C[\"yellow\"] if self._playing else C[\"green\"],\n )\n self._status_dot.set_status(\n \"running\" if self._playing else \"idle\",\n \"RUNNING\" if self._playing else \"PAUSED\",\n )\n if self._playing:\n self._auto_step()\n\n def _auto_step(self) -> None:\n if not self._playing:\n return\n sc = self._scenarios[self._current_scenario_idx]\n if self._step < len(sc.actual_cpu):\n self._do_step()\n self.after(self._speed_ms, self._auto_step)\n else:\n self._playing = False\n self._play_btn.configure(\n text=\"\\u25b6 Play\", fg_color=C[\"green\"]\n )\n self._status_dot.set_status(\"ready\", \"FINISHED\")\n\n def _do_step(self) -> None:\n sc = self._scenarios[self._current_scenario_idx]\n if self._step >= len(sc.actual_cpu):\n return\n\n t = self._step\n actual = float(sc.actual_cpu[t])\n predicted = float(sc.predicted_cpu[t])\n\n # ML autoscaler step\n ml_replicas, ml_dir = self._ml_sim.step(predicted, t)\n smoothed = self._ml_sim._smoothed or predicted\n\n # HPA (from pre-computed history)\n hpa_replicas = (\n self._history[t].hpa_replicas if t < len(self._history) else 2\n )\n\n self._actual_trace.append(actual)\n self._predicted_trace.append(predicted)\n self._smoothed_trace.append(smoothed)\n self._ml_replicas_trace.append(ml_replicas)\n self._hpa_replicas_trace.append(hpa_replicas)\n self._decisions_log.append(\n f\"[t={t:>3}] {ml_dir:>4} | ML:{ml_replicas} \"\n f\"HPA:{hpa_replicas} | \"\n f\"actual={actual:.1f}% pred={predicted:.1f}% \"\n f\"smooth={smoothed:.1f}%\"\n )\n\n # Generate notification for scaling events\n if ml_dir == \"up\":\n prev = self._ml_replicas_trace[-2] if len(self._ml_replicas_trace) > 1 else 2\n self._add_notification(\n f\"t={t}: ML SCALE UP {prev} -> {ml_replicas}\", \"up\"\n )\n elif ml_dir == \"down\":\n prev = self._ml_replicas_trace[-2] if len(self._ml_replicas_trace) > 1 else 2\n self._add_notification(\n f\"t={t}: ML SCALE DOWN {prev} -> {ml_replicas}\", \"down\"\n )\n\n # Warn if CPU is high\n if actual > 85:\n self._add_notification(\n f\"t={t}: High CPU alert: {actual:.1f}%\", \"warning\"\n )\n\n self._step += 1\n self._update_all()\n\n # -------------------------------------------------------------------\n # Notifications\n # -------------------------------------------------------------------\n\n def _add_notification(self, text: str, level: str = \"info\") -> None:\n \"\"\"Add a notification to the feed (max 50 kept).\"\"\"\n self._notifications.append((text, level))\n if len(self._notifications) > 50:\n self._notifications = self._notifications[-50:]\n\n # -------------------------------------------------------------------\n # Export functionality\n # -------------------------------------------------------------------\n\n def _export_data(self) -> None:\n \"\"\"Export simulation data to CSV file.\"\"\"\n if not self._actual_trace:\n return\n filepath = filedialog.asksaveasfilename(\n defaultextension=\".csv\",\n filetypes=[(\"CSV files\", \"*.csv\"), (\"All files\", \"*.*\")],\n title=\"Export simulation data\",\n )\n if not filepath:\n return\n with open(filepath, \"w\", newline=\"\") as f:\n writer = csv.writer(f)\n writer.writerow(\n [\n \"step\",\n \"actual_cpu\",\n \"predicted_cpu\",\n \"smoothed_cpu\",\n \"ml_replicas\",\n \"hpa_replicas\",\n ]\n )\n for i in range(len(self._actual_trace)):\n writer.writerow(\n [\n i,\n f\"{self._actual_trace[i]:.2f}\",\n f\"{self._predicted_trace[i]:.2f}\",\n f\"{self._smoothed_trace[i]:.2f}\",\n self._ml_replicas_trace[i],\n self._hpa_replicas_trace[i],\n ]\n )\n self._add_notification(f\"Data exported to {filepath}\", \"info\")\n\n def _export_charts(self) -> None:\n \"\"\"Export current charts as PNG images.\"\"\"\n filepath = filedialog.asksaveasfilename(\n defaultextension=\".png\",\n filetypes=[(\"PNG images\", \"*.png\"), (\"All files\", \"*.*\")],\n title=\"Save charts\",\n )\n if not filepath:\n return\n # Save all figures\n base = Path(filepath).stem\n parent = Path(filepath).parent\n figures = {\n \"nn\": self._nn_fig,\n \"algo_cpu\": self._algo_cpu_fig,\n \"algo_rep\": self._algo_rep_fig,\n \"experiment\": self._exp_fig,\n }\n for name, fig in figures.items():\n out = parent / f\"{base}_{name}.png\"\n fig.savefig(str(out), dpi=150, bbox_inches=\"tight\")\n self._add_notification(\n f\"Charts saved to {parent}/{base}_*.png\", \"info\"\n )\n\n # -------------------------------------------------------------------\n # Sidebar (parameters)\n # -------------------------------------------------------------------\n\n def _build_sidebar(self) -> None:\n sidebar = ctk.CTkScrollableFrame(\n self, fg_color=C[\"sidebar\"], width=220, corner_radius=0\n )\n sidebar.pack(side=\"left\", fill=\"y\", padx=0, pady=0)\n\n ctk.CTkLabel(\n sidebar,\n text=\"Parameters\",\n font=(\"Consolas\", 14, \"bold\"),\n text_color=C[\"text\"],\n ).pack(pady=(12, 8))\n\n def make_slider(\n parent: ctk.CTkFrame,\n label: str,\n from_: float,\n to: float,\n default: float,\n ) -> ctk.DoubleVar:\n ctk.CTkLabel(\n parent,\n text=label,\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n ).pack(pady=(8, 0), padx=8, anchor=\"w\")\n var = ctk.DoubleVar(value=default)\n val_lbl = ctk.CTkLabel(\n parent,\n text=f\"{default}\",\n font=(\"Consolas\", 10),\n text_color=C[\"text\"],\n )\n val_lbl.pack(padx=8, anchor=\"e\")\n\n def on_change(v: str) -> None:\n val_lbl.configure(text=f\"{float(v):.2f}\")\n\n slider = ctk.CTkSlider(\n parent,\n from_=from_,\n to=to,\n variable=var,\n width=180,\n command=on_change,\n )\n slider.pack(padx=8)\n return var\n\n self._target_var = make_slider(sidebar, \"Target Util %\", 20, 90, 60)\n self._alpha_var = make_slider(sidebar, \"EMA Alpha\", 0.1, 1.0, 0.7)\n self._safety_var = make_slider(\n sidebar, \"Safety Margin\", 0.0, 0.5, 0.15\n )\n self._up_thresh_var = make_slider(\n sidebar, \"Up Threshold \\u00d7\", 1.0, 1.5, 1.1\n )\n self._down_thresh_var = make_slider(\n sidebar, \"Down Threshold \\u00d7\", 0.3, 0.9, 0.7\n )\n\n # Replica range sliders\n ctk.CTkLabel(\n sidebar,\n text=\"\\u2500\\u2500\\u2500 Replica Range \\u2500\\u2500\\u2500\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n ).pack(pady=(12, 0), padx=8)\n self._min_rep_var = make_slider(\n sidebar, \"Min Replicas\", 1, 5, 2\n )\n self._max_rep_var = make_slider(\n sidebar, \"Max Replicas\", 4, 20, 8\n )\n\n ctk.CTkButton(\n sidebar,\n text=\"Apply & Re-run\",\n fg_color=C[\"accent\"],\n text_color=\"#fff\",\n hover_color=\"#c73550\",\n command=self._do_reset,\n ).pack(pady=16, padx=12, fill=\"x\")\n\n # Scenario selector\n ctk.CTkLabel(\n sidebar,\n text=\"Scenario\",\n font=(\"Consolas\", 14, \"bold\"),\n text_color=C[\"text\"],\n ).pack(pady=(16, 4))\n\n scenario_info = [\n (\"Stable\", \"Low-variance workload (~45-55%)\"),\n (\"Ramp\", \"30% -> 85% gradual increase\"),\n (\"Spike\", \"3 sudden load spikes\"),\n (\"Diurnal\", \"48h day/night cycle\"),\n ]\n self._scenario_btns: list[ctk.CTkButton] = []\n for i, (name, desc) in enumerate(scenario_info):\n btn = ctk.CTkButton(\n sidebar,\n text=name,\n width=180,\n height=28,\n fg_color=C[\"bg_card\"],\n hover_color=C[\"blue\"],\n command=lambda idx=i: self._select_scenario(idx),\n )\n btn.pack(pady=2, padx=12)\n Tooltip(btn, desc)\n self._scenario_btns.append(btn)\n\n # Scenario description\n self._scenario_desc = ctk.CTkLabel(\n sidebar,\n text=\"\",\n font=(\"Consolas\", 9),\n text_color=C[\"text_dim\"],\n wraplength=200,\n )\n self._scenario_desc.pack(pady=(8, 4), padx=8)\n\n # Keyboard shortcuts help\n ctk.CTkLabel(\n sidebar,\n text=\"\\u2500\\u2500\\u2500 Shortcuts \\u2500\\u2500\\u2500\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n ).pack(pady=(16, 4), padx=8)\n shortcuts = [\n \"Space: Play/Pause\",\n \"Right Arrow: Step\",\n \"R: Reset\",\n \"1-4: Select Scenario\",\n \"Ctrl+E: Export Data\",\n \"Ctrl+S: Save Charts\",\n ]\n for sc in shortcuts:\n ctk.CTkLabel(\n sidebar,\n text=sc,\n font=(\"Consolas\", 8),\n text_color=C[\"text_dim\"],\n ).pack(padx=12, anchor=\"w\")\n\n # -------------------------------------------------------------------\n # Tab view (4 tabs)\n # -------------------------------------------------------------------\n\n def _build_tabview(self) -> None:\n self._tabview = ctk.CTkTabview(self, fg_color=C[\"bg\"])\n self._tabview.pack(fill=\"both\", expand=True, padx=8, pady=(0, 4))\n\n self._tab_nn = self._tabview.add(\"Neural Network\")\n self._tab_algo = self._tabview.add(\"Scaling Algorithm\")\n self._tab_exp = self._tabview.add(\"Experiment\")\n self._tab_log = self._tabview.add(\"Activity Log\")\n\n self._build_nn_tab()\n self._build_algo_tab()\n self._build_exp_tab()\n self._build_log_tab()\n\n # Timeline at the bottom\n self._build_timeline()\n\n # --- Tab 1: Neural Network ---\n\n def _build_nn_tab(self) -> None:\n tab = self._tab_nn\n\n # Top: pipeline diagram\n pipe_frame = ctk.CTkFrame(\n tab, fg_color=C[\"bg_card\"], corner_radius=8, height=90\n )\n pipe_frame.pack(fill=\"x\", padx=8, pady=4)\n pipe_frame.pack_propagate(False)\n\n ctk.CTkLabel(\n pipe_frame,\n text=\"ONNX Inference Pipeline\",\n font=(\"Consolas\", 12, \"bold\"),\n text_color=C[\"text\"],\n ).pack(pady=(6, 4))\n\n blocks_frame = ctk.CTkFrame(pipe_frame, fg_color=\"transparent\")\n blocks_frame.pack()\n stages = [\n (\"Raw Metrics\", \"5 values\", C[\"green\"]),\n (\"\\u2192\", \"\", C[\"text_dim\"]),\n (\"Feature Eng.\", \"44 features\", C[\"purple\"]),\n (\"\\u2192\", \"\", C[\"text_dim\"]),\n (\"StandardScaler\", \"normalize\", C[\"orange\"]),\n (\"\\u2192\", \"\", C[\"text_dim\"]),\n (\"ONNX LSTM\", \"(1,24,44)\\u2192(1,1)\", C[\"blue\"]),\n (\"\\u2192\", \"\", C[\"text_dim\"]),\n (\"Inverse Scale\", \"\\u2192 CPU %\", C[\"green\"]),\n ]\n stage_tooltips = [\n \"5 raw metrics: cpu_usage, mem_util, net_in, net_out, disk_io\",\n \"\",\n \"Generates 44 features: time features, lags, rolling windows\",\n \"\",\n \"Normalizes features using saved StandardScaler (mean/std)\",\n \"\",\n \"2-layer LSTM, 65K params, 0.13ms latency via ONNX Runtime\",\n \"\",\n \"Converts normalized prediction back to CPU percentage\",\n ]\n for i, (title, sub, color) in enumerate(stages):\n if title == \"\\u2192\":\n ctk.CTkLabel(\n blocks_frame,\n text=\"\\u2192\",\n font=(\"Consolas\", 16, \"bold\"),\n text_color=C[\"text_dim\"],\n ).pack(side=\"left\", padx=2)\n else:\n f = ctk.CTkFrame(\n blocks_frame,\n fg_color=C[\"bg\"],\n corner_radius=6,\n border_width=1,\n border_color=color,\n )\n f.pack(side=\"left\", padx=4)\n ctk.CTkLabel(\n f,\n text=title,\n font=(\"Consolas\", 9, \"bold\"),\n text_color=color,\n ).pack(padx=6, pady=(2, 0))\n ctk.CTkLabel(\n f,\n text=sub,\n font=(\"Consolas\", 8),\n text_color=C[\"text_dim\"],\n ).pack(padx=6, pady=(0, 2))\n if stage_tooltips[i]:\n Tooltip(f, stage_tooltips[i])\n\n # Middle: prediction output + buffer + accuracy\n mid = ctk.CTkFrame(tab, fg_color=\"transparent\")\n mid.pack(fill=\"x\", padx=8, pady=4)\n\n # Buffer status\n buf_frame = ctk.CTkFrame(\n mid, fg_color=C[\"bg_card\"], corner_radius=8, width=220\n )\n buf_frame.pack(side=\"left\", padx=(0, 8), fill=\"y\")\n buf_frame.pack_propagate(False)\n ctk.CTkLabel(\n buf_frame,\n text=\"Buffer Status\",\n font=(\"Consolas\", 11, \"bold\"),\n text_color=C[\"text\"],\n ).pack(pady=(8, 4))\n self._buf_progress = ctk.CTkProgressBar(buf_frame, width=180)\n self._buf_progress.pack(pady=4, padx=12)\n self._buf_progress.set(0)\n self._buf_lbl = ctk.CTkLabel(\n buf_frame,\n text=\"0 / 24 NOT READY\",\n font=(\"Consolas\", 10),\n text_color=C[\"red\"],\n )\n self._buf_lbl.pack()\n Tooltip(\n buf_frame,\n \"LSTM requires 24 timesteps (2h of 5-min data) before predictions.\",\n )\n\n # Prediction big number\n pred_frame = ctk.CTkFrame(\n mid, fg_color=C[\"bg_card\"], corner_radius=8, width=180\n )\n pred_frame.pack(side=\"left\", padx=8, fill=\"y\")\n pred_frame.pack_propagate(False)\n ctk.CTkLabel(\n pred_frame,\n text=\"ML Prediction\",\n font=(\"Consolas\", 11, \"bold\"),\n text_color=C[\"text\"],\n ).pack(pady=(8, 2))\n self._pred_big = ctk.CTkLabel(\n pred_frame,\n text=\"\\u2014\",\n font=(\"Consolas\", 28, \"bold\"),\n text_color=C[\"blue\"],\n )\n self._pred_big.pack(pady=4)\n self._pred_actual = ctk.CTkLabel(\n pred_frame,\n text=\"Actual: \\u2014\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n )\n self._pred_actual.pack()\n\n # Prediction accuracy panel\n acc_frame = ctk.CTkFrame(\n mid, fg_color=C[\"bg_card\"], corner_radius=8, width=200\n )\n acc_frame.pack(side=\"left\", padx=8, fill=\"y\")\n acc_frame.pack_propagate(False)\n ctk.CTkLabel(\n acc_frame,\n text=\"Prediction Accuracy\",\n font=(\"Consolas\", 11, \"bold\"),\n text_color=C[\"text\"],\n ).pack(pady=(8, 4))\n self._acc_mae_lbl = ctk.CTkLabel(\n acc_frame,\n text=\"MAE: \\u2014\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n )\n self._acc_mae_lbl.pack(anchor=\"w\", padx=12)\n self._acc_rmse_lbl = ctk.CTkLabel(\n acc_frame,\n text=\"RMSE: \\u2014\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n )\n self._acc_rmse_lbl.pack(anchor=\"w\", padx=12)\n self._acc_err_lbl = ctk.CTkLabel(\n acc_frame,\n text=\"Error: \\u2014\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n )\n self._acc_err_lbl.pack(anchor=\"w\", padx=12)\n Tooltip(\n acc_frame,\n \"MAE: Mean Absolute Error\\n\"\n \"RMSE: Root Mean Squared Error\\n\"\n \"Error: Current |predicted - actual|\",\n )\n\n # Raw metrics panel\n raw_frame = ctk.CTkFrame(\n mid, fg_color=C[\"bg_card\"], corner_radius=8\n )\n raw_frame.pack(side=\"left\", fill=\"both\", expand=True, padx=8)\n ctk.CTkLabel(\n raw_frame,\n text=\"Current Metrics\",\n font=(\"Consolas\", 11, \"bold\"),\n text_color=C[\"text\"],\n ).pack(pady=(8, 4))\n self._raw_labels: dict[str, ctk.CTkLabel] = {}\n metrics_info = {\n \"cpu_usage\": \"CPU utilization (%)\",\n \"mem_util\": \"Memory utilization (%)\",\n \"net_in\": \"Network inbound (KB/s)\",\n \"net_out\": \"Network outbound (KB/s)\",\n \"disk_io\": \"Disk I/O utilization (%)\",\n }\n for metric, desc in metrics_info.items():\n row = ctk.CTkFrame(raw_frame, fg_color=\"transparent\")\n row.pack(fill=\"x\", padx=12)\n lbl = ctk.CTkLabel(\n row,\n text=f\"{metric}: \\u2014\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n )\n lbl.pack(side=\"left\")\n self._raw_labels[metric] = lbl\n # Mini progress bar for each metric\n bar = ctk.CTkProgressBar(row, width=60, height=6)\n bar.pack(side=\"right\", padx=4)\n bar.set(0)\n self._raw_labels[f\"{metric}_bar\"] = bar\n Tooltip(raw_frame, \"Simulated from CPU data. In production, \"\n \"these come from Prometheus.\")\n\n # Bottom: prediction history chart\n self._nn_fig, self._nn_ax = plt.subplots(\n 1, 1, figsize=(10, 3), dpi=80\n )\n self._nn_canvas = embed_figure(tab, self._nn_fig)\n\n # --- Tab 2: Scaling Algorithm ---\n\n def _build_algo_tab(self) -> None:\n tab = self._tab_algo\n\n # 9-step pipeline\n pipe = ctk.CTkFrame(tab, fg_color=\"transparent\")\n pipe.pack(fill=\"x\", padx=4, pady=4)\n\n step_names = [\n \"EMA\",\n \"Hysteresis\",\n \"Desired\",\n \"Clamp\",\n \"Rate Limit\",\n \"Cooldown\",\n \"Stabilize\",\n \"No Change?\",\n \"Commit\",\n ]\n self._algo_blocks: list[PipelineBlock] = []\n for i, name in enumerate(step_names):\n if i > 0:\n ctk.CTkLabel(\n pipe,\n text=\"\\u2192\",\n font=(\"Consolas\", 14, \"bold\"),\n text_color=C[\"text_dim\"],\n ).pack(side=\"left\", padx=1)\n block = PipelineBlock(pipe, i + 1, name)\n block.pack(side=\"left\", padx=2)\n self._algo_blocks.append(block)\n\n # State panel\n state_frame = ctk.CTkFrame(\n tab, fg_color=C[\"bg_card\"], corner_radius=8, height=40\n )\n state_frame.pack(fill=\"x\", padx=8, pady=4)\n self._state_lbl = ctk.CTkLabel(\n state_frame,\n text=(\n \"Cooldown UP: \\u2014 | Cooldown DOWN: \\u2014 | \"\n \"Consec. Below: \\u2014 | Direction: \\u2014\"\n ),\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n )\n self._state_lbl.pack(pady=6)\n\n # Charts: CPU + Replicas side by side\n charts = ctk.CTkFrame(tab, fg_color=\"transparent\")\n charts.pack(fill=\"both\", expand=True, padx=4, pady=4)\n\n left = ctk.CTkFrame(charts, fg_color=\"transparent\")\n left.pack(side=\"left\", fill=\"both\", expand=True)\n self._algo_cpu_fig, self._algo_cpu_ax = plt.subplots(\n 1, 1, figsize=(5.5, 3), dpi=80\n )\n self._algo_cpu_canvas = embed_figure(left, self._algo_cpu_fig)\n\n right = ctk.CTkFrame(charts, fg_color=\"transparent\")\n right.pack(side=\"left\", fill=\"both\", expand=True)\n self._algo_rep_fig, self._algo_rep_ax = plt.subplots(\n 1, 1, figsize=(5.5, 3), dpi=80\n )\n self._algo_rep_canvas = embed_figure(right, self._algo_rep_fig)\n\n # Decision log\n self._decision_log = ctk.CTkTextbox(\n tab,\n height=100,\n font=(\"Consolas\", 9),\n fg_color=C[\"bg\"],\n text_color=C[\"text_dim\"],\n )\n self._decision_log.pack(fill=\"x\", padx=8, pady=(0, 4))\n\n # --- Tab 3: Experiment Comparison ---\n\n def _build_exp_tab(self) -> None:\n tab = self._tab_exp\n\n # Main comparison chart\n self._exp_fig, self._exp_axes = plt.subplots(\n 3,\n 1,\n figsize=(10, 6),\n dpi=80,\n gridspec_kw={\"height_ratios\": [2, 1, 1]},\n )\n self._exp_canvas = embed_figure(tab, self._exp_fig)\n\n # KPI cards grid\n cards_frame = ctk.CTkFrame(tab, fg_color=\"transparent\")\n cards_frame.pack(fill=\"x\", padx=8, pady=4)\n\n kpi_info = [\n (\"Avg Replicas\", \"lower\", \"Average number of running replicas\"),\n (\"Replica-Min\", \"lower\", \"Total replica-minutes consumed\"),\n (\n \"SLA Violations\",\n \"lower\",\n \"Steps where CPU exceeded SLA threshold\",\n ),\n (\n \"Scaling Events\",\n \"lower\",\n \"Number of replica count changes\",\n ),\n (\n \"Over-Prov %\",\n \"lower\",\n \"Percentage of over-provisioned capacity\",\n ),\n (\n \"Under-Prov %\",\n \"lower\",\n \"Percentage of under-provisioned capacity\",\n ),\n (\n \"Reaction Time\",\n \"lower\",\n \"Avg steps to respond to demand spike\",\n ),\n (\n \"Cost Savings %\",\n \"higher\",\n \"ML cost savings vs HPA (negative = higher cost)\",\n ),\n ]\n self._kpi_cards: list[KPICard] = []\n self._kpi_better: list[str] = []\n for i, (name, better, tooltip) in enumerate(kpi_info):\n card = KPICard(cards_frame, name, tooltip=tooltip)\n card.grid(\n row=i // 4, column=i % 4, padx=4, pady=4, sticky=\"nsew\"\n )\n self._kpi_cards.append(card)\n self._kpi_better.append(better)\n for c in range(4):\n cards_frame.columnconfigure(c, weight=1)\n\n # --- Tab 4: Activity Log ---\n\n def _build_log_tab(self) -> None:\n tab = self._tab_log\n\n ctk.CTkLabel(\n tab,\n text=\"Activity & Notification Feed\",\n font=(\"Consolas\", 14, \"bold\"),\n text_color=C[\"text\"],\n ).pack(pady=(8, 4), padx=8, anchor=\"w\")\n\n ctk.CTkLabel(\n tab,\n text=\"Scaling events, alerts, and system notifications\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n ).pack(padx=8, anchor=\"w\")\n\n self._notif_frame = ctk.CTkScrollableFrame(\n tab, fg_color=C[\"bg\"], corner_radius=8\n )\n self._notif_frame.pack(\n fill=\"both\", expand=True, padx=8, pady=8\n )\n\n # -------------------------------------------------------------------\n # Update all visuals\n # -------------------------------------------------------------------\n\n def _update_all(self) -> None:\n sc = self._scenarios[self._current_scenario_idx]\n total = len(sc.actual_cpu)\n self._step_lbl.configure(text=f\"Step: {self._step} / {total}\")\n\n # Time display (5 min per step)\n minutes = self._step * 5\n hours = minutes // 60\n mins = minutes % 60\n self._time_lbl.configure(text=f\"{hours:02d}:{mins:02d}\")\n\n # Timeline\n progress = self._step / total if total > 0 else 0\n self._timeline_bar.set(progress)\n self._timeline_pct.configure(text=f\"{progress * 100:.0f}%\")\n\n # Highlight active scenario button\n for i, btn in enumerate(self._scenario_btns):\n btn.configure(\n fg_color=C[\"blue\"]\n if i == self._current_scenario_idx\n else C[\"bg_card\"]\n )\n\n # Update scenario description\n self._scenario_desc.configure(text=sc.description)\n\n self._update_nn_tab()\n self._update_algo_tab()\n self._update_exp_tab()\n self._update_log_tab()\n\n def _update_nn_tab(self) -> None:\n n = self._step\n\n # Buffer status\n buf_fill = min(n, 24)\n self._buf_progress.set(buf_fill / 24)\n ready = n >= 24\n self._buf_lbl.configure(\n text=f\"{min(n, 24)} / 24 {'READY' if ready else 'FILLING...'}\",\n text_color=C[\"green\"] if ready else C[\"red\"],\n )\n\n # Current prediction\n if n > 0:\n pred = self._predicted_trace[-1]\n actual = self._actual_trace[-1]\n color = (\n C[\"green\"]\n if pred < 42\n else C[\"yellow\"]\n if pred < 66\n else C[\"orange\"]\n if pred < 85\n else C[\"red\"]\n )\n self._pred_big.configure(\n text=f\"{pred:.1f}%\", text_color=color\n )\n self._pred_actual.configure(text=f\"Actual: {actual:.1f}%\")\n\n # Raw metrics (simulated from CPU)\n metrics_vals = {\n \"cpu_usage\": (actual, 100),\n \"mem_util\": (actual * 0.6, 100),\n \"net_in\": (actual * 1.2, 120),\n \"net_out\": (actual * 0.8, 80),\n \"disk_io\": (actual * 0.3, 30),\n }\n for metric, (val, max_val) in metrics_vals.items():\n unit = \"%\" if \"usage\" in metric or \"util\" in metric or \"io\" in metric else \" KB/s\"\n self._raw_labels[metric].configure(\n text=f\"{metric}: {val:.1f}{unit}\"\n )\n bar_key = f\"{metric}_bar\"\n if bar_key in self._raw_labels:\n bar_val = min(val / max_val, 1.0) if max_val > 0 else 0\n self._raw_labels[bar_key].set(bar_val)\n\n # Prediction accuracy\n errors = np.abs(\n np.array(self._actual_trace)\n - np.array(self._predicted_trace)\n )\n mae = float(np.mean(errors))\n rmse = float(np.sqrt(np.mean(errors**2)))\n cur_err = abs(actual - pred)\n\n mae_color = (\n C[\"green\"]\n if mae < 5\n else C[\"yellow\"]\n if mae < 10\n else C[\"red\"]\n )\n self._acc_mae_lbl.configure(\n text=f\"MAE: {mae:.2f}%\", text_color=mae_color\n )\n self._acc_rmse_lbl.configure(\n text=f\"RMSE: {rmse:.2f}%\", text_color=mae_color\n )\n err_color = (\n C[\"green\"]\n if cur_err < 5\n else C[\"yellow\"]\n if cur_err < 10\n else C[\"red\"]\n )\n self._acc_err_lbl.configure(\n text=f\"Error: {cur_err:.2f}%\", text_color=err_color\n )\n\n # Prediction history chart\n self._nn_ax.clear()\n if n > 1:\n window = 80\n x = list(range(max(0, n - window), n))\n a_slice = self._actual_trace[max(0, n - window):]\n p_slice = self._predicted_trace[max(0, n - window):]\n self._nn_ax.plot(\n x, a_slice, color=C[\"green\"], lw=1.5, label=\"Actual CPU\"\n )\n self._nn_ax.plot(\n x,\n p_slice,\n color=C[\"purple\"],\n lw=1.5,\n ls=\"--\",\n label=\"Predicted\",\n )\n # Fill error band\n self._nn_ax.fill_between(\n x, a_slice, p_slice, alpha=0.12, color=C[\"purple\"]\n )\n self._nn_ax.axhline(\n 60, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.5\n )\n self._nn_ax.set_ylabel(\"CPU %\")\n self._nn_ax.set_ylim(0, 100)\n self._nn_ax.legend(loc=\"upper right\", fontsize=8)\n self._nn_ax.grid(True, alpha=0.2)\n self._nn_ax.set_title(\n f\"Prediction History (last 80 steps) | Step {n}\",\n fontsize=10,\n )\n self._nn_fig.tight_layout()\n self._nn_canvas.draw_idle()\n\n def _update_algo_tab(self) -> None:\n n = self._step\n if n == 0:\n for block in self._algo_blocks:\n block.update(\"\\u2014\", \"skipped\")\n return\n\n # Get current step details\n pred = self._predicted_trace[-1]\n smoothed = self._smoothed_trace[-1]\n ml_replicas = self._ml_replicas_trace[-1]\n prev_replicas = (\n self._ml_replicas_trace[-2] if n > 1 else 2\n )\n\n target = self._target_var.get()\n up_thresh = target * self._up_thresh_var.get()\n down_thresh = target * self._down_thresh_var.get()\n\n # Determine direction\n if smoothed > up_thresh:\n direction = \"UP\"\n elif smoothed < down_thresh:\n direction = \"DOWN\"\n else:\n direction = \"HOLD\"\n\n # Update blocks\n self._algo_blocks[0].update(\n f\"{pred:.1f}\\u2192{smoothed:.1f}%\", \"active\"\n )\n cmp_sym = (\n \">\"\n if direction == \"UP\"\n else \"<\"\n if direction == \"DOWN\"\n else \"in\"\n )\n self._algo_blocks[1].update(\n f\"{smoothed:.0f}% {cmp_sym} \"\n f\"[{down_thresh:.0f},{up_thresh:.0f}]\"\n f\"\\n\\u2192{direction}\",\n \"active\" if direction != \"HOLD\" else \"blocked\",\n )\n\n min_r = int(self._min_rep_var.get())\n max_r = int(self._max_rep_var.get())\n\n if direction == \"HOLD\":\n for i in range(2, 9):\n self._algo_blocks[i].update(\"\\u2014\", \"skipped\")\n else:\n raw_desired = math.ceil(prev_replicas * smoothed / target)\n if direction == \"UP\":\n raw_desired = math.ceil(\n raw_desired * (1 + self._safety_var.get())\n )\n self._algo_blocks[2].update(\n f\"{prev_replicas}\\u00d7{smoothed:.0f}\"\n f\"/{target:.0f}\\n={raw_desired}\",\n \"active\",\n )\n clamped = max(min_r, min(max_r, raw_desired))\n self._algo_blocks[3].update(\n f\"[{min_r},{max_r}]\\u2192{clamped}\", \"active\"\n )\n rate_lim = (\n min(clamped, prev_replicas + 2)\n if direction == \"UP\"\n else max(clamped, prev_replicas - 1)\n )\n self._algo_blocks[4].update(\n f\"\\u00b1limit\\u2192{rate_lim}\", \"active\"\n )\n self._algo_blocks[5].update(\"OFF \\u2713\", \"active\")\n self._algo_blocks[6].update(\n \"N/A\" if direction == \"UP\" else \"check\", \"active\"\n )\n\n executed = ml_replicas != prev_replicas\n eq_sym = \"\\u2260\" if executed else \"=\"\n self._algo_blocks[7].update(\n f\"{ml_replicas}{eq_sym}{prev_replicas}\",\n \"active\" if executed else \"blocked\",\n )\n if executed:\n self._algo_blocks[8].update(\n f\"{prev_replicas}\\u2192{ml_replicas}\", \"executed\"\n )\n else:\n self._algo_blocks[8].update(\"hold\", \"blocked\")\n\n # State panel\n sim = self._ml_sim\n dir_color = {\n \"UP\": C[\"green\"],\n \"DOWN\": C[\"orange\"],\n \"HOLD\": C[\"text_dim\"],\n }.get(direction, C[\"text_dim\"])\n self._state_lbl.configure(\n text=f\"Direction: {direction} | \"\n f\"Consec. Below: {sim._consecutive_below} | \"\n f\"Replicas: {ml_replicas} | \"\n f\"Smoothed: {smoothed:.1f}%\",\n text_color=dir_color,\n )\n\n # CPU chart\n self._algo_cpu_ax.clear()\n if n > 1:\n x = list(range(n))\n self._algo_cpu_ax.plot(\n x,\n self._smoothed_trace,\n color=C[\"blue\"],\n lw=1.5,\n label=\"Smoothed\",\n )\n self._algo_cpu_ax.plot(\n x,\n self._actual_trace,\n color=C[\"green\"],\n lw=1,\n alpha=0.5,\n label=\"Actual\",\n )\n self._algo_cpu_ax.axhline(\n up_thresh,\n color=C[\"red\"],\n ls=\"--\",\n lw=0.8,\n label=f\"Up {up_thresh:.0f}%\",\n )\n self._algo_cpu_ax.axhline(\n down_thresh,\n color=C[\"orange\"],\n ls=\"--\",\n lw=0.8,\n label=f\"Down {down_thresh:.0f}%\",\n )\n self._algo_cpu_ax.axhline(\n target, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.6\n )\n self._algo_cpu_ax.axhspan(\n down_thresh, up_thresh, alpha=0.08, color=C[\"yellow\"]\n )\n self._algo_cpu_ax.set_ylim(0, 100)\n self._algo_cpu_ax.set_ylabel(\"CPU %\")\n self._algo_cpu_ax.legend(loc=\"upper right\", fontsize=7)\n self._algo_cpu_ax.grid(True, alpha=0.2)\n self._algo_cpu_ax.set_title(\"CPU + Thresholds\", fontsize=10)\n self._algo_cpu_fig.tight_layout()\n self._algo_cpu_canvas.draw_idle()\n\n # Replica chart\n self._algo_rep_ax.clear()\n if n > 1:\n x = list(range(n))\n self._algo_rep_ax.step(\n x,\n self._ml_replicas_trace,\n color=C[\"blue\"],\n lw=2,\n where=\"post\",\n label=\"ML Replicas\",\n )\n self._algo_rep_ax.step(\n x,\n self._hpa_replicas_trace,\n color=C[\"red\"],\n lw=1.5,\n where=\"post\",\n alpha=0.6,\n label=\"HPA Replicas\",\n )\n self._algo_rep_ax.axhline(\n min_r,\n color=C[\"text_dim\"],\n ls=\":\",\n lw=0.7,\n alpha=0.5,\n )\n self._algo_rep_ax.axhline(\n max_r,\n color=C[\"text_dim\"],\n ls=\":\",\n lw=0.7,\n alpha=0.5,\n )\n self._algo_rep_ax.set_ylim(0, max_r + 2)\n self._algo_rep_ax.set_ylabel(\"Replicas\")\n self._algo_rep_ax.legend(loc=\"upper right\", fontsize=7)\n self._algo_rep_ax.grid(True, alpha=0.2)\n self._algo_rep_ax.set_title(\"Replica Count\", fontsize=10)\n self._algo_rep_fig.tight_layout()\n self._algo_rep_canvas.draw_idle()\n\n # Decision log\n self._decision_log.configure(state=\"normal\")\n self._decision_log.delete(\"1.0\", \"end\")\n for line in self._decisions_log[-20:]:\n self._decision_log.insert(\"end\", line + \"\\n\")\n self._decision_log.configure(state=\"disabled\")\n self._decision_log.see(\"end\")\n\n def _update_exp_tab(self) -> None:\n if not hasattr(self, \"_full_metrics\"):\n return\n metrics = self._full_metrics\n history = self._history\n\n ax1, ax2, ax3 = self._exp_axes\n ax1.clear()\n ax2.clear()\n ax3.clear()\n\n n = len(history)\n if n > 0:\n x = list(range(n))\n actual = [r.actual_cpu for r in history]\n hpa_r = [r.hpa_replicas for r in history]\n ml_r = [r.ml_replicas for r in history]\n\n # Chart 1: CPU + Replicas overlay\n ax1.fill_between(\n x, actual, alpha=0.15, color=C[\"text_dim\"]\n )\n ax1.plot(\n x,\n actual,\n color=C[\"text_dim\"],\n lw=0.8,\n alpha=0.5,\n label=\"Actual CPU\",\n )\n ax1.step(\n x,\n [r * 10 for r in hpa_r],\n color=C[\"red\"],\n lw=1.5,\n where=\"post\",\n label=\"HPA Replicas (\\u00d710)\",\n )\n ax1.step(\n x,\n [r * 10 for r in ml_r],\n color=C[\"blue\"],\n lw=2,\n where=\"post\",\n label=\"ML Replicas (\\u00d710)\",\n )\n target = self._target_var.get()\n ax1.axhline(\n target * 1.2,\n color=C[\"red\"],\n ls=\":\",\n lw=0.8,\n alpha=0.5,\n label=\"SLA Limit\",\n )\n ax1.set_ylabel(\"CPU % / Replicas\\u00d710\")\n ax1.set_ylim(0, 100)\n ax1.legend(loc=\"upper right\", fontsize=7, ncol=2)\n sc = self._scenarios[self._current_scenario_idx]\n ax1.set_title(\n f\"Experiment: {sc.name}\", fontsize=11\n )\n ax1.grid(True, alpha=0.2)\n\n # Chart 2: Replica comparison (no multiplier)\n ax2.step(\n x,\n hpa_r,\n color=C[\"red\"],\n lw=1.5,\n where=\"post\",\n label=\"HPA\",\n )\n ax2.step(\n x,\n ml_r,\n color=C[\"blue\"],\n lw=2,\n where=\"post\",\n label=\"ML\",\n )\n ax2.set_ylabel(\"Replicas\")\n ax2.legend(loc=\"upper right\", fontsize=7)\n ax2.grid(True, alpha=0.2)\n ax2.set_title(\"Replica Count Comparison\", fontsize=9)\n\n # Chart 3: Replica diff\n diff = [m - h for m, h in zip(ml_r, hpa_r)]\n colors_bar = [\n C[\"green\"]\n if d < 0\n else C[\"red\"]\n if d > 0\n else C[\"text_dim\"]\n for d in diff\n ]\n ax3.bar(x, diff, color=colors_bar, alpha=0.7, width=1.0)\n ax3.axhline(0, color=C[\"text_dim\"], lw=0.5)\n ax3.set_ylabel(\"ML - HPA\")\n ax3.set_xlabel(\"Step\")\n ax3.grid(True, alpha=0.2)\n ax3.set_title(\n \"Replica Difference (negative = ML uses fewer)\",\n fontsize=9,\n )\n\n self._exp_fig.tight_layout()\n self._exp_canvas.draw_idle()\n\n # KPI cards\n kpi_data = [\n (\n f\"{metrics.hpa_avg_replicas:.1f}\",\n f\"{metrics.ml_avg_replicas:.1f}\",\n ),\n (\n f\"{metrics.hpa_replica_minutes:.0f}\",\n f\"{metrics.ml_replica_minutes:.0f}\",\n ),\n (\n f\"{metrics.hpa_sla_violations}\",\n f\"{metrics.ml_sla_violations}\",\n ),\n (\n f\"{metrics.hpa_scaling_events}\",\n f\"{metrics.ml_scaling_events}\",\n ),\n (\n f\"{metrics.hpa_over_provisioning_pct:.1f}%\",\n f\"{metrics.ml_over_provisioning_pct:.1f}%\",\n ),\n (\n f\"{metrics.hpa_under_provisioning_pct:.1f}%\",\n f\"{metrics.ml_under_provisioning_pct:.1f}%\",\n ),\n (\n f\"{metrics.hpa_avg_reaction_time:.1f}\",\n f\"{metrics.ml_avg_reaction_time:.1f}\",\n ),\n (\n \"\\u2014\",\n f\"{metrics.cost_savings_pct:+.1f}%\",\n ),\n ]\n for i, (hpa_val, ml_val) in enumerate(kpi_data):\n self._kpi_cards[i].set_values(\n hpa_val, ml_val, better=self._kpi_better[i]\n )\n\n def _update_log_tab(self) -> None:\n \"\"\"Update the activity log / notification feed tab.\"\"\"\n # Clear and rebuild (only last 30 for performance)\n for widget in self._notif_frame.winfo_children():\n widget.destroy()\n for text, level in self._notifications[-30:]:\n NotificationItem(self._notif_frame, text, level).pack(\n fill=\"x\", padx=4, pady=1\n )\n\n\n# ---------------------------------------------------------------------------\n# Entry point\n# ---------------------------------------------------------------------------\n\n\ndef main() -> None:\n \"\"\"Launch the experiment dashboard application.\"\"\"\n app = ExperimentApp()\n app.mainloop()\n\n\nif __name__ == \"__main__\":\n main()\n" | |
| } | |
| } | |
| ], | |
| "stop_reason": null, | |
| "stop_sequence": null, | |
| "usage": { | |
| "input_tokens": 1, | |
| "cache_creation_input_tokens": 301, | |
| "cache_read_input_tokens": 94771, | |
| "cache_creation": { | |
| "ephemeral_5m_input_tokens": 0, | |
| "ephemeral_1h_input_tokens": 301 | |
| }, | |
| "output_tokens": 25, | |
| "service_tier": "standard", | |
| "inference_geo": "not_available" | |
| }, | |
| "context_management": null | |
| }, | |
| "parent_tool_use_id": null, | |
| "session_id": "ce1ab353-0721-47e7-a373-84e062534c7f", | |
| "uuid": "f92c6300-a52c-4bf1-86cd-1e564e3b7e24" | |
| } | |
| [2026-02-07T18:26:05.856Z] [INFO] { | |
| "type": "user", | |
| "message": { | |
| "role": "user", | |
| "content": [ | |
| { | |
| "tool_use_id": "toolu_0156Aob1x2F1ngc7kwVfF4tx", | |
| "type": "tool_result", | |
| "content": "The file /tmp/gh-issue-solver-1770487990892/scripts/experiment_gui.py has been updated. Here's the result of running `cat -n` on a snippet of the edited file:\n 1→\"\"\"ML Autoscaler — Experiment Dashboard (CustomTkinter).\n 2→\n 3→Interactive visualization of:\n 4→ - Neural network internals (buffer, features, ONNX inference)\n 5→ - Scaling algorithm 9-step pipeline\n 6→ - HPA vs ML autoscaler comparison\n 7→\n 8→Features:\n 9→ - Keyboard shortcuts (Space=play/pause, Right=step, R=reset, 1-4=scenario)\n 10→ - Tooltips on pipeline blocks and KPI cards\n 11→ - Live prediction accuracy metrics (MAE, RMSE)\n 12→ - Animated status indicators\n 13→ - Scenario description panel\n 14→ - Timeline progress bar\n 15→ - Export charts / data to file\n 16→ - Notification feed for scaling events\n 17→ - Configurable min/max replica range\n 18→\n 19→Usage:\n 20→ python scripts/experiment_gui.py\n 21→\"\"\"\n 22→\n 23→from __future__ import annotations\n 24→\n 25→import csv\n 26→import math\n 27→import sys\n 28→import time\n 29→from pathlib import Path\n 30→from tkinter import filedialog\n 31→\n 32→import customtkinter as ctk\n 33→import matplotlib\n 34→import numpy as np\n 35→\n 36→matplotlib.use(\"TkAgg\")\n 37→import matplotlib.pyplot as plt # noqa: E402\n 38→from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg # noqa: E402\n 39→from matplotlib.figure import Figure # noqa: E402\n 40→\n 41→# Add project root to path for imports\n 42→ROOT = Path(__file__).resolve().parent.parent\n 43→sys.path.insert(0, str(ROOT / \"src\"))\n 44→sys.path.insert(0, str(ROOT))\n 45→\n 46→from scripts.experiment_scenarios import Scenario, get_all_scenarios # noqa: E402\n 47→from scripts.experiment_simulator import ( # noqa: E402\n 48→ ExperimentRunner,\n 49→ MLAutoscalerSimulator,\n 50→ MLConfig,\n 51→ StepRecord,\n 52→)\n 53→\n 54→# ---------------------------------------------------------------------------\n 55→# Color palette (unified with Grafana dashboards)\n 56→# ---------------------------------------------------------------------------\n 57→\n 58→C = {\n 59→ \"bg\": \"#1a1a2e\",\n 60→ \"bg_card\": \"#16213e\",\n 61→ \"sidebar\": \"#0f3460\",\n 62→ \"accent\": \"#e94560\",\n 63→ \"green\": \"#73BF69\",\n 64→ \"yellow\": \"#FADE2A\",\n 65→ \"orange\": \"#FF9830\",\n 66→ \"red\": \"#F2495C\",\n 67→ \"blue\": \"#5794F2\",\n 68→ \"purple\": \"#B877D9\",\n 69→ \"cyan\": \"#8AB8FF\",\n 70→ \"text\": \"#e0e0e0\",\n 71→ \"text_dim\": \"#8892b0\",\n 72→ \"grid\": \"#2a2a4a\",\n 73→ \"success\": \"#73BF69\",\n 74→ \"warning\": \"#FADE2A\",\n 75→ \"danger\": \"#F2495C\",\n 76→}\n 77→\n 78→# Matplotlib dark style\n 79→plt.rcParams.update(\n 80→ {\n 81→ \"figure.facecolor\": C[\"bg_card\"],\n 82→ \"axes.facecolor\": C[\"bg\"],\n 83→ \"axes.edgecolor\": C[\"grid\"],\n 84→ \"axes.labelcolor\": C[\"text\"],\n 85→ \"text.color\": C[\"text\"],\n 86→ \"xtick.color\": C[\"text_dim\"],\n 87→ \"ytick.color\": C[\"text_dim\"],\n 88→ \"grid.color\": C[\"grid\"],\n 89→ \"grid.alpha\": 0.3,\n 90→ \"font.size\": 9,\n 91→ \"legend.facecolor\": C[\"bg_card\"],\n 92→ \"legend.edgecolor\": C[\"grid\"],\n 93→ }\n 94→)\n 95→\n 96→\n 97→# ---------------------------------------------------------------------------\n 98→# Utility: embed a matplotlib figure in a CTkFrame\n 99→# ---------------------------------------------------------------------------\n 100→\n 101→\n 102→def embed_figure(parent: ctk.CTkFrame, fig: Figure) -> FigureCanvasTkAgg:\n 103→ \"\"\"Embed a matplotlib Figure into a CustomTkinter frame.\"\"\"\n 104→ canvas = FigureCanvasTkAgg(fig, master=parent)\n 105→ canvas.draw()\n 106→ canvas.get_tk_widget().pack(fill=\"both\", expand=True)\n 107→ return canvas\n 108→\n 109→\n 110→# ---------------------------------------------------------------------------\n 111→# Tooltip widget\n 112→# ---------------------------------------------------------------------------\n 113→\n 114→\n 115→class Tooltip:\n 116→ \"\"\"Hover tooltip for any widget.\"\"\"\n 117→\n 118→ def __init__(self, widget: ctk.CTkBaseClass, text: str) -> None:\n 119→ self._widget = widget\n 120→ self._text = text\n 121→ self._tip_window: ctk.CTkToplevel | None = None\n 122→ widget.bind(\"<Enter>\", self._show)\n 123→ widget.bind(\"<Leave>\", self._hide)\n 124→\n 125→ def _show(self, event: object = None) -> None:\n 126→ if self._tip_window:\n 127→ return\n 128→ x = self._widget.winfo_rootx() + 20\n 129→ y = self._widget.winfo_rooty() + self._widget.winfo_height() + 4\n 130→ tw = ctk.CTkToplevel(self._widget)\n 131→ tw.wm_overrideredirect(True)\n 132→ tw.wm_geometry(f\"+{x}+{y}\")\n 133→ tw.attributes(\"-topmost\", True)\n 134→ lbl = ctk.CTkLabel(\n 135→ tw,\n 136→ text=self._text,\n 137→ font=(\"Consolas\", 10),\n 138→ fg_color=C[\"bg_card\"],\n 139→ text_color=C[\"text\"],\n 140→ corner_radius=6,\n 141→ wraplength=280,\n 142→ )\n 143→ lbl.pack(padx=6, pady=4)\n 144→ self._tip_window = tw\n 145→\n 146→ def _hide(self, event: object = None) -> None:\n 147→ if self._tip_window:\n 148→ self._tip_window.destroy()\n 149→ self._tip_window = None\n 150→\n 151→\n 152→# ---------------------------------------------------------------------------\n 153→# KPI Card widget (enhanced with delta indicator)\n 154→# ---------------------------------------------------------------------------\n 155→\n 156→\n 157→class KPICard(ctk.CTkFrame):\n 158→ \"\"\"Compact KPI card: title + HPA value + ML value + delta arrow.\"\"\"\n 159→\n 160→ def __init__(self, master: ctk.CTkFrame, title: str, tooltip: str = \"\", **kw: object):\n 161→ super().__init__(master, fg_color=C[\"bg_card\"], corner_radius=8, **kw)\n 162→ self._title_lbl = ctk.CTkLabel(\n 163→ self, text=title, font=(\"Consolas\", 11), text_color=C[\"text_dim\"]\n 164→ )\n 165→ self._title_lbl.pack(pady=(6, 2))\n 166→\n 167→ row = ctk.CTkFrame(self, fg_color=\"transparent\")\n 168→ row.pack(pady=(0, 2))\n 169→ self._hpa_lbl = ctk.CTkLabel(\n 170→ row, text=\"HPA: --\", font=(\"Consolas\", 13, \"bold\"), text_color=C[\"red\"]\n 171→ )\n 172→ self._hpa_lbl.pack(side=\"left\", padx=6)\n 173→ self._ml_lbl = ctk.CTkLabel(\n 174→ row, text=\"ML: --\", font=(\"Consolas\", 13, \"bold\"), text_color=C[\"blue\"]\n 175→ )\n 176→ self._ml_lbl.pack(side=\"left\", padx=6)\n 177→\n 178→ # Delta indicator\n 179→ self._delta_lbl = ctk.CTkLabel(\n 180→ self, text=\"\", font=(\"Consolas\", 9), text_color=C[\"text_dim\"]\n 181→ )\n 182→ self._delta_lbl.pack(pady=(0, 4))\n 183→\n 184→ if tooltip:\n 185→ Tooltip(self, tooltip)\n 186→\n 187→ def set_values(\n 188→ self,\n 189→ hpa_val: str,\n 190→ ml_val: str,\n 191→ better: str = \"lower\",\n 192→ ) -> None:\n 193→ \"\"\"Update card values with automatic delta comparison.\n 194→\n 195→ Args:\n 196→ hpa_val: HPA metric value string.\n 197→ ml_val: ML metric value string.\n 198→ better: Direction that is \"better\" — \"lower\" or \"higher\".\n 199→ \"\"\"\n 200→ self._hpa_lbl.configure(text=f\"HPA: {hpa_val}\")\n 201→ self._ml_lbl.configure(text=f\"ML: {ml_val}\")\n 202→\n 203→ # Parse numeric values for delta\n 204→ try:\n 205→ hpa_num = float(hpa_val.replace(\"%\", \"\").replace(\"+\", \"\").replace(\",\", \"\"))\n 206→ ml_num = float(ml_val.replace(\"%\", \"\").replace(\"+\", \"\").replace(\",\", \"\"))\n 207→ diff = ml_num - hpa_num\n 208→ if abs(diff) < 0.01:\n 209→ self._delta_lbl.configure(text=\"= equal\", text_color=C[\"text_dim\"])\n 210→ else:\n 211→ is_better = (diff < 0) if better == \"lower\" else (diff > 0)\n 212→ arrow = \"v\" if diff < 0 else \"^\"\n 213→ color = C[\"green\"] if is_better else C[\"red\"]\n 214→ self._delta_lbl.configure(\n 215→ text=f\"{arrow} {abs(diff):.1f}\", text_color=color\n 216→ )\n 217→ except (ValueError, AttributeError):\n 218→ self._delta_lbl.configure(text=\"\", text_color=C[\"text_dim\"])\n 219→\n 220→\n 221→# ---------------------------------------------------------------------------\n 222→# Pipeline Step block (enhanced with tooltip)\n 223→# ---------------------------------------------------------------------------\n 224→\n 225→\n 226→class PipelineBlock(ctk.CTkFrame):\n 227→ \"\"\"One step in the 9-step scaling algorithm visualization.\"\"\"\n 228→\n 229→ # Descriptions for each pipeline step\n 230→ STEP_TOOLTIPS = {\n 231→ 1: \"EMA: Exponential moving average smoothing. \"\n 232→ \"Smoothes predicted CPU to remove noise.\",\n 233→ 2: \"Hysteresis: Dead-zone check. \"\n 234→ \"Determines UP/DOWN/HOLD based on thresholds.\",\n 235→ 3: \"Desired: Calculates target replica count \"\n 236→ \"using ceil(current * smoothed / target).\",\n 237→ 4: \"Clamp: Restricts replicas to [min, max] range.\",\n 238→ 5: \"Rate Limit: Limits change speed. \"\n 239→ \"Max +2 up, -1 down per step.\",\n 240→ 6: \"Cooldown: Prevents rapid re-scaling. \"\n 241→ \"60s for up, 300s for down.\",\n 242→ 7: \"Stabilize: Requires 3 consecutive low \"\n 243→ \"readings before scaling down.\",\n 244→ 8: \"No Change: Skips execution if desired \"\n 245→ \"equals current replicas.\",\n 246→ 9: \"Commit: Applies the scaling decision \"\n 247→ \"and updates cooldown timers.\",\n 248→ }\n 249→\n 250→ def __init__(self, master: ctk.CTkFrame, step_num: int, title: str, **kw: object):\n 251→ super().__init__(\n 252→ master,\n 253→ fg_color=C[\"bg_card\"],\n 254→ corner_radius=6,\n 255→ border_width=1,\n 256→ border_color=C[\"grid\"],\n 257→ **kw,\n 258→ )\n 259→ self.configure(width=115, height=80)\n 260→ self.pack_propagate(False)\n 261→ self._step_num = step_num\n 262→ ctk.CTkLabel(\n 263→ self,\n 264→ text=f\"Step {step_num}\",\n 265→ font=(\"Consolas\", 8),\n 266→ text_color=C[\"text_dim\"],\n 267→ ).pack(pady=(4, 0))\n 268→ ctk.CTkLabel(\n 269→ self,\n 270→ text=title,\n 271→ font=(\"Consolas\", 9, \"bold\"),\n 272→ text_color=C[\"text\"],\n 273→ ).pack()\n 274→ self._value_lbl = ctk.CTkLabel(\n 275→ self,\n 276→ text=\"\\u2014\",\n 277→ font=(\"Consolas\", 10),\n 278→ text_color=C[\"text_dim\"],\n 279→ wraplength=100,\n 280→ )\n 281→ self._value_lbl.pack(pady=(2, 4))\n 282→\n 283→ # Add tooltip\n 284→ tooltip_text = self.STEP_TOOLTIPS.get(step_num, \"\")\n 285→ if tooltip_text:\n 286→ Tooltip(self, tooltip_text)\n 287→\n 288→ def update(self, text: str, status: str = \"active\") -> None:\n 289→ \"\"\"Update block display text and status color.\"\"\"\n 290→ colors = {\n 291→ \"active\": C[\"green\"],\n 292→ \"blocked\": C[\"yellow\"],\n 293→ \"skipped\": C[\"text_dim\"],\n 294→ \"executed\": C[\"accent\"],\n 295→ }\n 296→ self._value_lbl.configure(\n 297→ text=text, text_color=colors.get(status, C[\"text\"])\n 298→ )\n 299→ border = {\n 300→ \"active\": C[\"green\"],\n 301→ \"blocked\": C[\"yellow\"],\n 302→ \"executed\": C[\"accent\"],\n 303→ }\n 304→ self.configure(border_color=border.get(status, C[\"grid\"]))\n 305→\n 306→\n 307→# ---------------------------------------------------------------------------\n 308→# Status Indicator (animated dot)\n 309→# ---------------------------------------------------------------------------\n 310→\n 311→\n 312→class StatusDot(ctk.CTkFrame):\n 313→ \"\"\"Animated status dot indicator.\"\"\"\n 314→\n 315→ def __init__(self, master: ctk.CTkFrame, **kw: object):\n 316→ super().__init__(master, fg_color=\"transparent\", **kw)\n 317→ self._dot = ctk.CTkLabel(\n 318→ self, text=\"\\u25cf\", font=(\"Consolas\", 14), text_color=C[\"text_dim\"]\n 319→ )\n 320→ self._dot.pack(side=\"left\", padx=2)\n 321→ self._label = ctk.CTkLabel(\n 322→ self, text=\"IDLE\", font=(\"Consolas\", 10), text_color=C[\"text_dim\"]\n 323→ )\n 324→ self._label.pack(side=\"left\", padx=2)\n 325→ self._pulse_on = True\n 326→\n 327→ def set_status(self, status: str, text: str) -> None:\n 328→ \"\"\"Update status indicator.\n 329→\n 330→ Args:\n 331→ status: One of 'idle', 'running', 'ready', 'warning'.\n 332→ text: Label text to display.\n 333→ \"\"\"\n 334→ color_map = {\n 335→ \"idle\": C[\"text_dim\"],\n 336→ \"running\": C[\"green\"],\n 337→ \"ready\": C[\"blue\"],\n 338→ \"warning\": C[\"yellow\"],\n 339→ \"error\": C[\"red\"],\n 340→ }\n 341→ color = color_map.get(status, C[\"text_dim\"])\n 342→ self._dot.configure(text_color=color)\n 343→ self._label.configure(text=text, text_color=color)\n 344→\n 345→\n 346→# ---------------------------------------------------------------------------\n 347→# Notification Item\n 348→# ---------------------------------------------------------------------------\n 349→\n 350→\n 351→class NotificationItem(ctk.CTkFrame):\n 352→ \"\"\"Single notification entry in the feed.\"\"\"\n 353→\n 354→ def __init__(\n 355→ self,\n 356→ master: ctk.CTkFrame,\n 357→ text: str,\n 358→ level: str = \"info\",\n 359→ **kw: object,\n 360→ ):\n 361→ super().__init__(\n 362→ master, fg_color=C[\"bg_card\"], corner_radius=4, height=24, **kw\n 363→ )\n 364→ self.pack_propagate(False)\n 365→ color = {\n 366→ \"info\": C[\"blue\"],\n 367→ \"up\": C[\"green\"],\n 368→ \"down\": C[\"orange\"],\n 369→ \"warning\": C[\"yellow\"],\n 370→ }.get(level, C[\"text_dim\"])\n 371→ icon = {\n 372→ \"info\": \"\\u25b6\",\n 373→ \"up\": \"\\u25b2\",\n 374→ \"down\": \"\\u25bc\",\n 375→ \"warning\": \"\\u26a0\",\n 376→ }.get(level, \"\\u25cf\")\n 377→ ctk.CTkLabel(\n 378→ self,\n 379→ text=f\" {icon} {text}\",\n 380→ font=(\"Consolas\", 9),\n 381→ text_color=color,\n 382→ anchor=\"w\",\n 383→ ).pack(fill=\"x\", padx=4, pady=2)\n 384→\n 385→\n 386→# ---------------------------------------------------------------------------\n 387→# Main Application\n 388→# ---------------------------------------------------------------------------\n 389→\n 390→\n 391→class ExperimentApp(ctk.CTk):\n 392→ \"\"\"Main GUI application for the ML Autoscaler experiment dashboard.\"\"\"\n 393→\n 394→ def __init__(self) -> None:\n 395→ super().__init__()\n 396→ self.title(\"ML Autoscaler \\u2014 Experiment Dashboard\")\n 397→ self.geometry(\"1480x960\")\n 398→ ctk.set_appearance_mode(\"dark\")\n 399→\n 400→ # State\n 401→ self._scenarios: list[Scenario] = []\n 402→ self._current_scenario_idx = 0\n 403→ self._step = 0\n 404→ self._playing = False\n 405→ self._speed_ms = 200\n 406→ self._history: list[StepRecord] = []\n 407→\n 408→ # ML simulator state for step-by-step\n 409→ self._ml_sim: MLAutoscalerSimulator | None = None\n 410→ self._hpa_replicas_trace: list[int] = []\n 411→ self._ml_replicas_trace: list[int] = []\n 412→ self._actual_trace: list[float] = []\n 413→ self._predicted_trace: list[float] = []\n 414→ self._smoothed_trace: list[float] = []\n 415→ self._decisions_log: list[str] = []\n 416→\n 417→ # Notifications queue\n 418→ self._notifications: list[tuple[str, str]] = []\n 419→\n 420→ # Build UI\n 421→ self._build_controls()\n 422→ self._build_sidebar()\n 423→ self._build_tabview()\n 424→\n 425→ # Bind keyboard shortcuts\n 426→ self._bind_shortcuts()\n 427→\n 428→ # Load data\n 429→ self._load_scenarios()\n 430→\n 431→ # -------------------------------------------------------------------\n 432→ # Keyboard shortcuts\n 433→ # -------------------------------------------------------------------\n 434→\n 435→ def _bind_shortcuts(self) -> None:\n 436→ \"\"\"Bind keyboard shortcuts for common actions.\"\"\"\n 437→ self.bind(\"<space>\", lambda e: self._toggle_play())\n 438→ self.bind(\"<Right>\", lambda e: self._do_step())\n 439→ self.bind(\"<r>\", lambda e: self._do_reset())\n 440→ self.bind(\"<R>\", lambda e: self._do_reset())\n 441→ self.bind(\"<1>\", lambda e: self._select_scenario(0))\n 442→ self.bind(\"<2>\", lambda e: self._select_scenario(1))\n 443→ self.bind(\"<3>\", lambda e: self._select_scenario(2))\n 444→ self.bind(\"<4>\", lambda e: self._select_scenario(3))\n 445→ self.bind(\n 446→ \"<Control-e>\", lambda e: self._export_data()\n 447→ )\n 448→ self.bind(\n 449→ \"<Control-s>\", lambda e: self._export_charts()\n 450→ )\n 451→\n 452→ # -------------------------------------------------------------------\n 453→ # Data loading\n 454→ # -------------------------------------------------------------------\n 455→\n 456→ def _load_scenarios(self) -> None:\n 457→ try:\n 458→ self._scenarios = get_all_scenarios(\n 459→ ROOT / \"data\" / \"processed\" / \"test.csv\",\n 460→ ROOT / \"data\" / \"processed\" / \"scaler.pkl\",\n 461→ ROOT / \"data\" / \"processed\" / \"metadata.json\",\n 462→ )\n 463→ except Exception:\n 464→ self._scenarios = [self._synthetic_fallback()]\n 465→ # Ensure we have at least 4 scenario buttons\n 466→ while len(self._scenarios) < 4:\n 467→ self._scenarios.append(self._synthetic_fallback())\n 468→ self._select_scenario(0)\n 469→\n 470→ def _synthetic_fallback(self) -> Scenario:\n 471→ \"\"\"Create a simple synthetic scenario if data files are missing.\"\"\"\n 472→ n = 200\n 473→ rng = np.random.RandomState(42)\n 474→ t = np.linspace(0, 4 * np.pi, n)\n 475→ actual = 50 + 25 * np.sin(t) + rng.normal(0, 3, n)\n 476→ predicted = np.roll(actual, -3)\n 477→ predicted[-3:] = actual[-3:]\n 478→ predicted += rng.normal(0, 2, n)\n 479→ return Scenario(\n 480→ name=\"synthetic_demo\",\n 481→ description=\"Synthetic sine wave demo for testing\",\n 482→ actual_cpu=np.clip(actual, 10, 100),\n 483→ predicted_cpu=np.clip(predicted, 10, 100),\n 484→ duration_hours=n * 5 / 60,\n 485→ )\n 486→\n 487→ def _select_scenario(self, idx: int) -> None:\n 488→ if idx >= len(self._scenarios):\n 489→ return\n 490→ self._current_scenario_idx = idx\n 491→ self._reset_simulation()\n 492→ self._update_all()\n 493→\n 494→ def _do_reset(self) -> None:\n 495→ \"\"\"Reset simulation (callable from shortcuts).\"\"\"\n 496→ self._reset_simulation()\n 497→ self._update_all()\n 498→\n 499→ def _reset_simulation(self) -> None:\n 500→ self._step = 0\n 501→ self._playing = False\n 502→ self._notifications = []\n 503→ sc = self._scenarios[self._current_scenario_idx]\n 504→\n 505→ # Run full experiment for Tab 3\n 506→ runner = ExperimentRunner(\n 507→ sc.actual_cpu,\n 508→ sc.predicted_cpu,\n 509→ ml_config=self._build_ml_config(),\n 510→ )\n 511→ self._history, self._full_metrics = runner.run()\n 512→\n 513→ # Reset step-by-step state\n 514→ self._ml_sim = MLAutoscalerSimulator(self._build_ml_config())\n 515→ self._hpa_replicas_trace = []\n 516→ self._ml_replicas_trace = []\n 517→ self._actual_trace = []\n 518→ self._predicted_trace = []\n 519→ self._smoothed_trace = []\n 520→ self._decisions_log = []\n 521→\n 522→ # Update play button state\n 523→ if hasattr(self, \"_play_btn\"):\n 524→ self._play_btn.configure(\n 525→ text=\"\\u25b6 Play\", fg_color=C[\"green\"]\n 526→ )\n 527→\n 528→ def _build_ml_config(self) -> MLConfig:\n 529→ return MLConfig(\n 530→ target_utilization=self._target_var.get(),\n 531→ smoothing_alpha=self._alpha_var.get(),\n 532→ safety_margin=self._safety_var.get(),\n 533→ scale_up_threshold_factor=self._up_thresh_var.get(),\n 534→ scale_down_threshold_factor=self._down_thresh_var.get(),\n 535→ min_replicas=int(self._min_rep_var.get()),\n 536→ max_replicas=int(self._max_rep_var.get()),\n 537→ )\n 538→\n 539→ # -------------------------------------------------------------------\n 540→ # Control bar (top)\n 541→ # -------------------------------------------------------------------\n 542→\n 543→ def _build_controls(self) -> None:\n 544→ bar = ctk.CTkFrame(self, fg_color=C[\"bg_card\"], height=50)\n 545→ bar.pack(fill=\"x\", padx=8, pady=(8, 4))\n 546→\n 547→ ctk.CTkLabel(\n 548→ bar,\n 549→ text=\"ML Autoscaler \\u2014 Experiment Dashboard\",\n 550→ font=(\"Consolas\", 16, \"bold\"),\n 551→ text_color=C[\"text\"],\n 552→ ).pack(side=\"left\", padx=12)\n 553→\n 554→ # Status dot\n 555→ self._status_dot = StatusDot(bar)\n 556→ self._status_dot.pack(side=\"left\", padx=8)\n 557→\n 558→ # Transport controls\n 559→ self._play_btn = ctk.CTkButton(\n 560→ bar,\n 561→ text=\"\\u25b6 Play\",\n 562→ width=90,\n 563→ fg_color=C[\"green\"],\n 564→ text_color=\"#000\",\n 565→ hover_color=\"#5ea554\",\n 566→ command=self._toggle_play,\n 567→ )\n 568→ self._play_btn.pack(side=\"left\", padx=4)\n 569→\n 570→ ctk.CTkButton(\n 571→ bar,\n 572→ text=\"\\u23ed Step\",\n 573→ width=90,\n 574→ fg_color=C[\"blue\"],\n 575→ text_color=\"#000\",\n 576→ hover_color=\"#4678c9\",\n 577→ command=self._do_step,\n 578→ ).pack(side=\"left\", padx=4)\n 579→\n 580→ ctk.CTkButton(\n 581→ bar,\n 582→ text=\"\\u21ba Reset\",\n 583→ width=90,\n 584→ fg_color=C[\"orange\"],\n 585→ text_color=\"#000\",\n 586→ hover_color=\"#d97e24\",\n 587→ command=self._do_reset,\n 588→ ).pack(side=\"left\", padx=4)\n 589→\n 590→ # Export buttons\n 591→ ctk.CTkButton(\n 592→ bar,\n 593→ text=\"\\u2913 Export\",\n 594→ width=80,\n 595→ fg_color=C[\"purple\"],\n 596→ text_color=\"#fff\",\n 597→ hover_color=\"#9b5fbf\",\n 598→ command=self._export_data,\n 599→ ).pack(side=\"left\", padx=4)\n 600→\n 601→ # Step counter + time display\n 602→ self._time_lbl = ctk.CTkLabel(\n 603→ bar,\n 604→ text=\"00:00\",\n 605→ font=(\"Consolas\", 11),\n 606→ text_color=C[\"text_dim\"],\n 607→ )\n 608→ self._time_lbl.pack(side=\"right\", padx=4)\n 609→\n 610→ self._step_lbl = ctk.CTkLabel(\n 611→ bar,\n 612→ text=\"Step: 0 / 0\",\n 613→ font=(\"Consolas\", 12),\n 614→ text_color=C[\"text_dim\"],\n 615→ )\n 616→ self._step_lbl.pack(side=\"right\", padx=8)\n 617→\n 618→ # Speed slider\n 619→ ctk.CTkLabel(bar, text=\"Speed:\", text_color=C[\"text_dim\"]).pack(\n 620→ side=\"right\"\n 621→ )\n 622→ self._speed_slider = ctk.CTkSlider(\n 623→ bar,\n 624→ from_=50,\n 625→ to=1000,\n 626→ width=120,\n 627→ command=lambda v: setattr(self, \"_speed_ms\", int(v)),\n 628→ )\n 629→ self._speed_slider.set(200)\n 630→ self._speed_slider.pack(side=\"right\", padx=4)\n 631→\n 632→ # Shortcuts hint\n 633→ Tooltip(\n 634→ self._play_btn,\n 635→ \"Space: Play/Pause | Right: Step | R: Reset | 1-4: Scenario | \"\n 636→ \"Ctrl+E: Export data | Ctrl+S: Save charts\",\n 637→ )\n 638→\n 639→ # -------------------------------------------------------------------\n 640→ # Timeline progress bar\n 641→ # -------------------------------------------------------------------\n 642→\n 643→ def _build_timeline(self) -> None:\n 644→ \"\"\"Build timeline progress bar below tabs.\"\"\"\n 645→ frame = ctk.CTkFrame(self, fg_color=C[\"bg_card\"], height=30)\n 646→ frame.pack(fill=\"x\", padx=8, pady=(0, 2))\n 647→ frame.pack_propagate(False)\n 648→\n 649→ self._timeline_bar = ctk.CTkProgressBar(\n 650→ frame, width=600, height=8, progress_color=C[\"blue\"]\n 651→ )\n 652→ self._timeline_bar.pack(side=\"left\", fill=\"x\", expand=True, padx=12, pady=10)\n 653→ self._timeline_bar.set(0)\n 654→\n 655→ self._timeline_pct = ctk.CTkLabel(\n 656→ frame, text=\"0%\", font=(\"Consolas\", 9), text_color=C[\"text_dim\"]\n 657→ )\n 658→ self._timeline_pct.pack(side=\"right\", padx=8)\n 659→\n 660→ def _toggle_play(self) -> None:\n 661→ self._playing = not self._playing\n 662→ self._play_btn.configure(\n 663→ text=\"\\u23f8 Pause\" if self._playing else \"\\u25b6 Play\",\n 664→ fg_color=C[\"yellow\"] if self._playing else C[\"green\"],\n 665→ )\n 666→ self._status_dot.set_status(\n 667→ \"running\" if self._playing else \"idle\",\n 668→ \"RUNNING\" if self._playing else \"PAUSED\",\n 669→ )\n 670→ if self._playing:\n 671→ self._auto_step()\n 672→\n 673→ def _auto_step(self) -> None:\n 674→ if not self._playing:\n 675→ return\n 676→ sc = self._scenarios[self._current_scenario_idx]\n 677→ if self._step < len(sc.actual_cpu):\n 678→ self._do_step()\n 679→ self.after(self._speed_ms, self._auto_step)\n 680→ else:\n 681→ self._playing = False\n 682→ self._play_btn.configure(\n 683→ text=\"\\u25b6 Play\", fg_color=C[\"green\"]\n 684→ )\n 685→ self._status_dot.set_status(\"ready\", \"FINISHED\")\n 686→\n 687→ def _do_step(self) -> None:\n 688→ sc = self._scenarios[self._current_scenario_idx]\n 689→ if self._step >= len(sc.actual_cpu):\n 690→ return\n 691→\n 692→ t = self._step\n 693→ actual = float(sc.actual_cpu[t])\n 694→ predicted = float(sc.predicted_cpu[t])\n 695→\n 696→ # ML autoscaler step\n 697→ ml_replicas, ml_dir = self._ml_sim.step(predicted, t)\n 698→ smoothed = self._ml_sim._smoothed or predicted\n 699→\n 700→ # HPA (from pre-computed history)\n 701→ hpa_replicas = (\n 702→ self._history[t].hpa_replicas if t < len(self._history) else 2\n 703→ )\n 704→\n 705→ self._actual_trace.append(actual)\n 706→ self._predicted_trace.append(predicted)\n 707→ self._smoothed_trace.append(smoothed)\n 708→ self._ml_replicas_trace.append(ml_replicas)\n 709→ self._hpa_replicas_trace.append(hpa_replicas)\n 710→ self._decisions_log.append(\n 711→ f\"[t={t:>3}] {ml_dir:>4} | ML:{ml_replicas} \"\n 712→ f\"HPA:{hpa_replicas} | \"\n 713→ f\"actual={actual:.1f}% pred={predicted:.1f}% \"\n 714→ f\"smooth={smoothed:.1f}%\"\n 715→ )\n 716→\n 717→ # Generate notification for scaling events\n 718→ if ml_dir == \"up\":\n 719→ prev = self._ml_replicas_trace[-2] if len(self._ml_replicas_trace) > 1 else 2\n 720→ self._add_notification(\n 721→ f\"t={t}: ML SCALE UP {prev} -> {ml_replicas}\", \"up\"\n 722→ )\n 723→ elif ml_dir == \"down\":\n 724→ prev = self._ml_replicas_trace[-2] if len(self._ml_replicas_trace) > 1 else 2\n 725→ self._add_notification(\n 726→ f\"t={t}: ML SCALE DOWN {prev} -> {ml_replicas}\", \"down\"\n 727→ )\n 728→\n 729→ # Warn if CPU is high\n 730→ if actual > 85:\n 731→ self._add_notification(\n 732→ f\"t={t}: High CPU alert: {actual:.1f}%\", \"warning\"\n 733→ )\n 734→\n 735→ self._step += 1\n 736→ self._update_all()\n 737→\n 738→ # -------------------------------------------------------------------\n 739→ # Notifications\n 740→ # -------------------------------------------------------------------\n 741→\n 742→ def _add_notification(self, text: str, level: str = \"info\") -> None:\n 743→ \"\"\"Add a notification to the feed (max 50 kept).\"\"\"\n 744→ self._notifications.append((text, level))\n 745→ if len(self._notifications) > 50:\n 746→ self._notifications = self._notifications[-50:]\n 747→\n 748→ # -------------------------------------------------------------------\n 749→ # Export functionality\n 750→ # -------------------------------------------------------------------\n 751→\n 752→ def _export_data(self) -> None:\n 753→ \"\"\"Export simulation data to CSV file.\"\"\"\n 754→ if not self._actual_trace:\n 755→ return\n 756→ filepath = filedialog.asksaveasfilename(\n 757→ defaultextension=\".csv\",\n 758→ filetypes=[(\"CSV files\", \"*.csv\"), (\"All files\", \"*.*\")],\n 759→ title=\"Export simulation data\",\n 760→ )\n 761→ if not filepath:\n 762→ return\n 763→ with open(filepath, \"w\", newline=\"\") as f:\n 764→ writer = csv.writer(f)\n 765→ writer.writerow(\n 766→ [\n 767→ \"step\",\n 768→ \"actual_cpu\",\n 769→ \"predicted_cpu\",\n 770→ \"smoothed_cpu\",\n 771→ \"ml_replicas\",\n 772→ \"hpa_replicas\",\n 773→ ]\n 774→ )\n 775→ for i in range(len(self._actual_trace)):\n 776→ writer.writerow(\n 777→ [\n 778→ i,\n 779→ f\"{self._actual_trace[i]:.2f}\",\n 780→ f\"{self._predicted_trace[i]:.2f}\",\n 781→ f\"{self._smoothed_trace[i]:.2f}\",\n 782→ self._ml_replicas_trace[i],\n 783→ self._hpa_replicas_trace[i],\n 784→ ]\n 785→ )\n 786→ self._add_notification(f\"Data exported to {filepath}\", \"info\")\n 787→\n 788→ def _export_charts(self) -> None:\n 789→ \"\"\"Export current charts as PNG images.\"\"\"\n 790→ filepath = filedialog.asksaveasfilename(\n 791→ defaultextension=\".png\",\n 792→ filetypes=[(\"PNG images\", \"*.png\"), (\"All files\", \"*.*\")],\n 793→ title=\"Save charts\",\n 794→ )\n 795→ if not filepath:\n 796→ return\n 797→ # Save all figures\n 798→ base = Path(filepath).stem\n 799→ parent = Path(filepath).parent\n 800→ figures = {\n 801→ \"nn\": self._nn_fig,\n 802→ \"algo_cpu\": self._algo_cpu_fig,\n 803→ \"algo_rep\": self._algo_rep_fig,\n 804→ \"experiment\": self._exp_fig,\n 805→ }\n 806→ for name, fig in figures.items():\n 807→ out = parent / f\"{base}_{name}.png\"\n 808→ fig.savefig(str(out), dpi=150, bbox_inches=\"tight\")\n 809→ self._add_notification(\n 810→ f\"Charts saved to {parent}/{base}_*.png\", \"info\"\n 811→ )\n 812→\n 813→ # -------------------------------------------------------------------\n 814→ # Sidebar (parameters)\n 815→ # -------------------------------------------------------------------\n 816→\n 817→ def _build_sidebar(self) -> None:\n 818→ sidebar = ctk.CTkScrollableFrame(\n 819→ self, fg_color=C[\"sidebar\"], width=220, corner_radius=0\n 820→ )\n 821→ sidebar.pack(side=\"left\", fill=\"y\", padx=0, pady=0)\n 822→\n 823→ ctk.CTkLabel(\n 824→ sidebar,\n 825→ text=\"Parameters\",\n 826→ font=(\"Consolas\", 14, \"bold\"),\n 827→ text_color=C[\"text\"],\n 828→ ).pack(pady=(12, 8))\n 829→\n 830→ def make_slider(\n 831→ parent: ctk.CTkFrame,\n 832→ label: str,\n 833→ from_: float,\n 834→ to: float,\n 835→ default: float,\n 836→ ) -> ctk.DoubleVar:\n 837→ ctk.CTkLabel(\n 838→ parent,\n 839→ text=label,\n 840→ font=(\"Consolas\", 10),\n 841→ text_color=C[\"text_dim\"],\n 842→ ).pack(pady=(8, 0), padx=8, anchor=\"w\")\n 843→ var = ctk.DoubleVar(value=default)\n 844→ val_lbl = ctk.CTkLabel(\n 845→ parent,\n 846→ text=f\"{default}\",\n 847→ font=(\"Consolas\", 10),\n 848→ text_color=C[\"text\"],\n 849→ )\n 850→ val_lbl.pack(padx=8, anchor=\"e\")\n 851→\n 852→ def on_change(v: str) -> None:\n 853→ val_lbl.configure(text=f\"{float(v):.2f}\")\n 854→\n 855→ slider = ctk.CTkSlider(\n 856→ parent,\n 857→ from_=from_,\n 858→ to=to,\n 859→ variable=var,\n 860→ width=180,\n 861→ command=on_change,\n 862→ )\n 863→ slider.pack(padx=8)\n 864→ return var\n 865→\n 866→ self._target_var = make_slider(sidebar, \"Target Util %\", 20, 90, 60)\n 867→ self._alpha_var = make_slider(sidebar, \"EMA Alpha\", 0.1, 1.0, 0.7)\n 868→ self._safety_var = make_slider(\n 869→ sidebar, \"Safety Margin\", 0.0, 0.5, 0.15\n 870→ )\n 871→ self._up_thresh_var = make_slider(\n 872→ sidebar, \"Up Threshold \\u00d7\", 1.0, 1.5, 1.1\n 873→ )\n 874→ self._down_thresh_var = make_slider(\n 875→ sidebar, \"Down Threshold \\u00d7\", 0.3, 0.9, 0.7\n 876→ )\n 877→\n 878→ # Replica range sliders\n 879→ ctk.CTkLabel(\n 880→ sidebar,\n 881→ text=\"\\u2500\\u2500\\u2500 Replica Range \\u2500\\u2500\\u2500\",\n 882→ font=(\"Consolas\", 10),\n 883→ text_color=C[\"text_dim\"],\n 884→ ).pack(pady=(12, 0), padx=8)\n 885→ self._min_rep_var = make_slider(\n 886→ sidebar, \"Min Replicas\", 1, 5, 2\n 887→ )\n 888→ self._max_rep_var = make_slider(\n 889→ sidebar, \"Max Replicas\", 4, 20, 8\n 890→ )\n 891→\n 892→ ctk.CTkButton(\n 893→ sidebar,\n 894→ text=\"Apply & Re-run\",\n 895→ fg_color=C[\"accent\"],\n 896→ text_color=\"#fff\",\n 897→ hover_color=\"#c73550\",\n 898→ command=self._do_reset,\n 899→ ).pack(pady=16, padx=12, fill=\"x\")\n 900→\n 901→ # Scenario selector\n 902→ ctk.CTkLabel(\n 903→ sidebar,\n 904→ text=\"Scenario\",\n 905→ font=(\"Consolas\", 14, \"bold\"),\n 906→ text_color=C[\"text\"],\n 907→ ).pack(pady=(16, 4))\n 908→\n 909→ scenario_info = [\n 910→ (\"Stable\", \"Low-variance workload (~45-55%)\"),\n 911→ (\"Ramp\", \"30% -> 85% gradual increase\"),\n 912→ (\"Spike\", \"3 sudden load spikes\"),\n 913→ (\"Diurnal\", \"48h day/night cycle\"),\n 914→ ]\n 915→ self._scenario_btns: list[ctk.CTkButton] = []\n 916→ for i, (name, desc) in enumerate(scenario_info):\n 917→ btn = ctk.CTkButton(\n 918→ sidebar,\n 919→ text=name,\n 920→ width=180,\n 921→ height=28,\n 922→ fg_color=C[\"bg_card\"],\n 923→ hover_color=C[\"blue\"],\n 924→ command=lambda idx=i: self._select_scenario(idx),\n 925→ )\n 926→ btn.pack(pady=2, padx=12)\n 927→ Tooltip(btn, desc)\n 928→ self._scenario_btns.append(btn)\n 929→\n 930→ # Scenario description\n 931→ self._scenario_desc = ctk.CTkLabel(\n 932→ sidebar,\n 933→ text=\"\",\n 934→ font=(\"Consolas\", 9),\n 935→ text_color=C[\"text_dim\"],\n 936→ wraplength=200,\n 937→ )\n 938→ self._scenario_desc.pack(pady=(8, 4), padx=8)\n 939→\n 940→ # Keyboard shortcuts help\n 941→ ctk.CTkLabel(\n 942→ sidebar,\n 943→ text=\"\\u2500\\u2500\\u2500 Shortcuts \\u2500\\u2500\\u2500\",\n 944→ font=(\"Consolas\", 10),\n 945→ text_color=C[\"text_dim\"],\n 946→ ).pack(pady=(16, 4), padx=8)\n 947→ shortcuts = [\n 948→ \"Space: Play/Pause\",\n 949→ \"Right Arrow: Step\",\n 950→ \"R: Reset\",\n 951→ \"1-4: Select Scenario\",\n 952→ \"Ctrl+E: Export Data\",\n 953→ \"Ctrl+S: Save Charts\",\n 954→ ]\n 955→ for sc in shortcuts:\n 956→ ctk.CTkLabel(\n 957→ sidebar,\n 958→ text=sc,\n 959→ font=(\"Consolas\", 8),\n 960→ text_color=C[\"text_dim\"],\n 961→ ).pack(padx=12, anchor=\"w\")\n 962→\n 963→ # -------------------------------------------------------------------\n 964→ # Tab view (4 tabs)\n 965→ # -------------------------------------------------------------------\n 966→\n 967→ def _build_tabview(self) -> None:\n 968→ self._tabview = ctk.CTkTabview(self, fg_color=C[\"bg\"])\n 969→ self._tabview.pack(fill=\"both\", expand=True, padx=8, pady=(0, 4))\n 970→\n 971→ self._tab_nn = self._tabview.add(\"Neural Network\")\n 972→ self._tab_algo = self._tabview.add(\"Scaling Algorithm\")\n 973→ self._tab_exp = self._tabview.add(\"Experiment\")\n 974→ self._tab_log = self._tabview.add(\"Activity Log\")\n 975→\n 976→ self._build_nn_tab()\n 977→ self._build_algo_tab()\n 978→ self._build_exp_tab()\n 979→ self._build_log_tab()\n 980→\n 981→ # Timeline at the bottom\n 982→ self._build_timeline()\n 983→\n 984→ # --- Tab 1: Neural Network ---\n 985→\n 986→ def _build_nn_tab(self) -> None:\n 987→ tab = self._tab_nn\n 988→\n 989→ # Top: pipeline diagram\n 990→ pipe_frame = ctk.CTkFrame(\n 991→ tab, fg_color=C[\"bg_card\"], corner_radius=8, height=90\n 992→ )\n 993→ pipe_frame.pack(fill=\"x\", padx=8, pady=4)\n 994→ pipe_frame.pack_propagate(False)\n 995→\n 996→ ctk.CTkLabel(\n 997→ pipe_frame,\n 998→ text=\"ONNX Inference Pipeline\",\n 999→ font=(\"Consolas\", 12, \"bold\"),\n 1000→ text_color=C[\"text\"],\n 1001→ ).pack(pady=(6, 4))\n 1002→\n 1003→ blocks_frame = ctk.CTkFrame(pipe_frame, fg_color=\"transparent\")\n 1004→ blocks_frame.pack()\n 1005→ stages = [\n 1006→ (\"Raw Metrics\", \"5 values\", C[\"green\"]),\n 1007→ (\"\\u2192\", \"\", C[\"text_dim\"]),\n 1008→ (\"Feature Eng.\", \"44 features\", C[\"purple\"]),\n 1009→ (\"\\u2192\", \"\", C[\"text_dim\"]),\n 1010→ (\"StandardScaler\", \"normalize\", C[\"orange\"]),\n 1011→ (\"\\u2192\", \"\", C[\"text_dim\"]),\n 1012→ (\"ONNX LSTM\", \"(1,24,44)\\u2192(1,1)\", C[\"blue\"]),\n 1013→ (\"\\u2192\", \"\", C[\"text_dim\"]),\n 1014→ (\"Inverse Scale\", \"\\u2192 CPU %\", C[\"green\"]),\n 1015→ ]\n 1016→ stage_tooltips = [\n 1017→ \"5 raw metrics: cpu_usage, mem_util, net_in, net_out, disk_io\",\n 1018→ \"\",\n 1019→ \"Generates 44 features: time features, lags, rolling windows\",\n 1020→ \"\",\n 1021→ \"Normalizes features using saved StandardScaler (mean/std)\",\n 1022→ \"\",\n 1023→ \"2-layer LSTM, 65K params, 0.13ms latency via ONNX Runtime\",\n 1024→ \"\",\n 1025→ \"Converts normalized prediction back to CPU percentage\",\n 1026→ ]\n 1027→ for i, (title, sub, color) in enumerate(stages):\n 1028→ if title == \"\\u2192\":\n 1029→ ctk.CTkLabel(\n 1030→ blocks_frame,\n 1031→ text=\"\\u2192\",\n 1032→ font=(\"Consolas\", 16, \"bold\"),\n 1033→ text_color=C[\"text_dim\"],\n 1034→ ).pack(side=\"left\", padx=2)\n 1035→ else:\n 1036→ f = ctk.CTkFrame(\n 1037→ blocks_frame,\n 1038→ fg_color=C[\"bg\"],\n 1039→ corner_radius=6,\n 1040→ border_width=1,\n 1041→ border_color=color,\n 1042→ )\n 1043→ f.pack(side=\"left\", padx=4)\n 1044→ ctk.CTkLabel(\n 1045→ f,\n 1046→ text=title,\n 1047→ font=(\"Consolas\", 9, \"bold\"),\n 1048→ text_color=color,\n 1049→ ).pack(padx=6, pady=(2, 0))\n 1050→ ctk.CTkLabel(\n 1051→ f,\n 1052→ text=sub,\n 1053→ font=(\"Consolas\", 8),\n 1054→ text_color=C[\"text_dim\"],\n 1055→ ).pack(padx=6, pady=(0, 2))\n 1056→ if stage_tooltips[i]:\n 1057→ Tooltip(f, stage_tooltips[i])\n 1058→\n 1059→ # Middle: prediction output + buffer + accuracy\n 1060→ mid = ctk.CTkFrame(tab, fg_color=\"transparent\")\n 1061→ mid.pack(fill=\"x\", padx=8, pady=4)\n 1062→\n 1063→ # Buffer status\n 1064→ buf_frame = ctk.CTkFrame(\n 1065→ mid, fg_color=C[\"bg_card\"], corner_radius=8, width=220\n 1066→ )\n 1067→ buf_frame.pack(side=\"left\", padx=(0, 8), fill=\"y\")\n 1068→ buf_frame.pack_propagate(False)\n 1069→ ctk.CTkLabel(\n 1070→ buf_frame,\n 1071→ text=\"Buffer Status\",\n 1072→ font=(\"Consolas\", 11, \"bold\"),\n 1073→ text_color=C[\"text\"],\n 1074→ ).pack(pady=(8, 4))\n 1075→ self._buf_progress = ctk.CTkProgressBar(buf_frame, width=180)\n 1076→ self._buf_progress.pack(pady=4, padx=12)\n 1077→ self._buf_progress.set(0)\n 1078→ self._buf_lbl = ctk.CTkLabel(\n 1079→ buf_frame,\n 1080→ text=\"0 / 24 NOT READY\",\n 1081→ font=(\"Consolas\", 10),\n 1082→ text_color=C[\"red\"],\n 1083→ )\n 1084→ self._buf_lbl.pack()\n 1085→ Tooltip(\n 1086→ buf_frame,\n 1087→ \"LSTM requires 24 timesteps (2h of 5-min data) before predictions.\",\n 1088→ )\n 1089→\n 1090→ # Prediction big number\n 1091→ pred_frame = ctk.CTkFrame(\n 1092→ mid, fg_color=C[\"bg_card\"], corner_radius=8, width=180\n 1093→ )\n 1094→ pred_frame.pack(side=\"left\", padx=8, fill=\"y\")\n 1095→ pred_frame.pack_propagate(False)\n 1096→ ctk.CTkLabel(\n 1097→ pred_frame,\n 1098→ text=\"ML Prediction\",\n 1099→ font=(\"Consolas\", 11, \"bold\"),\n 1100→ text_color=C[\"text\"],\n 1101→ ).pack(pady=(8, 2))\n 1102→ self._pred_big = ctk.CTkLabel(\n 1103→ pred_frame,\n 1104→ text=\"\\u2014\",\n 1105→ font=(\"Consolas\", 28, \"bold\"),\n 1106→ text_color=C[\"blue\"],\n 1107→ )\n 1108→ self._pred_big.pack(pady=4)\n 1109→ self._pred_actual = ctk.CTkLabel(\n 1110→ pred_frame,\n 1111→ text=\"Actual: \\u2014\",\n 1112→ font=(\"Consolas\", 10),\n 1113→ text_color=C[\"text_dim\"],\n 1114→ )\n 1115→ self._pred_actual.pack()\n 1116→\n 1117→ # Prediction accuracy panel\n 1118→ acc_frame = ctk.CTkFrame(\n 1119→ mid, fg_color=C[\"bg_card\"], corner_radius=8, width=200\n 1120→ )\n 1121→ acc_frame.pack(side=\"left\", padx=8, fill=\"y\")\n 1122→ acc_frame.pack_propagate(False)\n 1123→ ctk.CTkLabel(\n 1124→ acc_frame,\n 1125→ text=\"Prediction Accuracy\",\n 1126→ font=(\"Consolas\", 11, \"bold\"),\n 1127→ text_color=C[\"text\"],\n 1128→ ).pack(pady=(8, 4))\n 1129→ self._acc_mae_lbl = ctk.CTkLabel(\n 1130→ acc_frame,\n 1131→ text=\"MAE: \\u2014\",\n 1132→ font=(\"Consolas\", 10),\n 1133→ text_color=C[\"text_dim\"],\n 1134→ )\n 1135→ self._acc_mae_lbl.pack(anchor=\"w\", padx=12)\n 1136→ self._acc_rmse_lbl = ctk.CTkLabel(\n 1137→ acc_frame,\n 1138→ text=\"RMSE: \\u2014\",\n 1139→ font=(\"Consolas\", 10),\n 1140→ text_color=C[\"text_dim\"],\n 1141→ )\n 1142→ self._acc_rmse_lbl.pack(anchor=\"w\", padx=12)\n 1143→ self._acc_err_lbl = ctk.CTkLabel(\n 1144→ acc_frame,\n 1145→ text=\"Error: \\u2014\",\n 1146→ font=(\"Consolas\", 10),\n 1147→ text_color=C[\"text_dim\"],\n 1148→ )\n 1149→ self._acc_err_lbl.pack(anchor=\"w\", padx=12)\n 1150→ Tooltip(\n 1151→ acc_frame,\n 1152→ \"MAE: Mean Absolute Error\\n\"\n 1153→ \"RMSE: Root Mean Squared Error\\n\"\n 1154→ \"Error: Current |predicted - actual|\",\n 1155→ )\n 1156→\n 1157→ # Raw metrics panel\n 1158→ raw_frame = ctk.CTkFrame(\n 1159→ mid, fg_color=C[\"bg_card\"], corner_radius=8\n 1160→ )\n 1161→ raw_frame.pack(side=\"left\", fill=\"both\", expand=True, padx=8)\n 1162→ ctk.CTkLabel(\n 1163→ raw_frame,\n 1164→ text=\"Current Metrics\",\n 1165→ font=(\"Consolas\", 11, \"bold\"),\n 1166→ text_color=C[\"text\"],\n 1167→ ).pack(pady=(8, 4))\n 1168→ self._raw_labels: dict[str, ctk.CTkLabel] = {}\n 1169→ metrics_info = {\n 1170→ \"cpu_usage\": \"CPU utilization (%)\",\n 1171→ \"mem_util\": \"Memory utilization (%)\",\n 1172→ \"net_in\": \"Network inbound (KB/s)\",\n 1173→ \"net_out\": \"Network outbound (KB/s)\",\n 1174→ \"disk_io\": \"Disk I/O utilization (%)\",\n 1175→ }\n 1176→ for metric, desc in metrics_info.items():\n 1177→ row = ctk.CTkFrame(raw_frame, fg_color=\"transparent\")\n 1178→ row.pack(fill=\"x\", padx=12)\n 1179→ lbl = ctk.CTkLabel(\n 1180→ row,\n 1181→ text=f\"{metric}: \\u2014\",\n 1182→ font=(\"Consolas\", 10),\n 1183→ text_color=C[\"text_dim\"],\n 1184→ )\n 1185→ lbl.pack(side=\"left\")\n 1186→ self._raw_labels[metric] = lbl\n 1187→ # Mini progress bar for each metric\n 1188→ bar = ctk.CTkProgressBar(row, width=60, height=6)\n 1189→ bar.pack(side=\"right\", padx=4)\n 1190→ bar.set(0)\n 1191→ self._raw_labels[f\"{metric}_bar\"] = bar\n 1192→ Tooltip(raw_frame, \"Simulated from CPU data. In production, \"\n 1193→ \"these come from Prometheus.\")\n 1194→\n 1195→ # Bottom: prediction history chart\n 1196→ self._nn_fig, self._nn_ax = plt.subplots(\n 1197→ 1, 1, figsize=(10, 3), dpi=80\n 1198→ )\n 1199→ self._nn_canvas = embed_figure(tab, self._nn_fig)\n 1200→\n 1201→ # --- Tab 2: Scaling Algorithm ---\n 1202→\n 1203→ def _build_algo_tab(self) -> None:\n 1204→ tab = self._tab_algo\n 1205→\n 1206→ # 9-step pipeline\n 1207→ pipe = ctk.CTkFrame(tab, fg_color=\"transparent\")\n 1208→ pipe.pack(fill=\"x\", padx=4, pady=4)\n 1209→\n 1210→ step_names = [\n 1211→ \"EMA\",\n 1212→ \"Hysteresis\",\n 1213→ \"Desired\",\n 1214→ \"Clamp\",\n 1215→ \"Rate Limit\",\n 1216→ \"Cooldown\",\n 1217→ \"Stabilize\",\n 1218→ \"No Change?\",\n 1219→ \"Commit\",\n 1220→ ]\n 1221→ self._algo_blocks: list[PipelineBlock] = []\n 1222→ for i, name in enumerate(step_names):\n 1223→ if i > 0:\n 1224→ ctk.CTkLabel(\n 1225→ pipe,\n 1226→ text=\"\\u2192\",\n 1227→ font=(\"Consolas\", 14, \"bold\"),\n 1228→ text_color=C[\"text_dim\"],\n 1229→ ).pack(side=\"left\", padx=1)\n 1230→ block = PipelineBlock(pipe, i + 1, name)\n 1231→ block.pack(side=\"left\", padx=2)\n 1232→ self._algo_blocks.append(block)\n 1233→\n 1234→ # State panel\n 1235→ state_frame = ctk.CTkFrame(\n 1236→ tab, fg_color=C[\"bg_card\"], corner_radius=8, height=40\n 1237→ )\n 1238→ state_frame.pack(fill=\"x\", padx=8, pady=4)\n 1239→ self._state_lbl = ctk.CTkLabel(\n 1240→ state_frame,\n 1241→ text=(\n 1242→ \"Cooldown UP: \\u2014 | Cooldown DOWN: \\u2014 | \"\n 1243→ \"Consec. Below: \\u2014 | Direction: \\u2014\"\n 1244→ ),\n 1245→ font=(\"Consolas\", 10),\n 1246→ text_color=C[\"text_dim\"],\n 1247→ )\n 1248→ self._state_lbl.pack(pady=6)\n 1249→\n 1250→ # Charts: CPU + Replicas side by side\n 1251→ charts = ctk.CTkFrame(tab, fg_color=\"transparent\")\n 1252→ charts.pack(fill=\"both\", expand=True, padx=4, pady=4)\n 1253→\n 1254→ left = ctk.CTkFrame(charts, fg_color=\"transparent\")\n 1255→ left.pack(side=\"left\", fill=\"both\", expand=True)\n 1256→ self._algo_cpu_fig, self._algo_cpu_ax = plt.subplots(\n 1257→ 1, 1, figsize=(5.5, 3), dpi=80\n 1258→ )\n 1259→ self._algo_cpu_canvas = embed_figure(left, self._algo_cpu_fig)\n 1260→\n 1261→ right = ctk.CTkFrame(charts, fg_color=\"transparent\")\n 1262→ right.pack(side=\"left\", fill=\"both\", expand=True)\n 1263→ self._algo_rep_fig, self._algo_rep_ax = plt.subplots(\n 1264→ 1, 1, figsize=(5.5, 3), dpi=80\n 1265→ )\n 1266→ self._algo_rep_canvas = embed_figure(right, self._algo_rep_fig)\n 1267→\n 1268→ # Decision log\n 1269→ self._decision_log = ctk.CTkTextbox(\n 1270→ tab,\n 1271→ height=100,\n 1272→ font=(\"Consolas\", 9),\n 1273→ fg_color=C[\"bg\"],\n 1274→ text_color=C[\"text_dim\"],\n 1275→ )\n 1276→ self._decision_log.pack(fill=\"x\", padx=8, pady=(0, 4))\n 1277→\n 1278→ # --- Tab 3: Experiment Comparison ---\n 1279→\n 1280→ def _build_exp_tab(self) -> None:\n 1281→ tab = self._tab_exp\n 1282→\n 1283→ # Main comparison chart\n 1284→ self._exp_fig, self._exp_axes = plt.subplots(\n 1285→ 3,\n 1286→ 1,\n 1287→ figsize=(10, 6),\n 1288→ dpi=80,\n 1289→ gridspec_kw={\"height_ratios\": [2, 1, 1]},\n 1290→ )\n 1291→ self._exp_canvas = embed_figure(tab, self._exp_fig)\n 1292→\n 1293→ # KPI cards grid\n 1294→ cards_frame = ctk.CTkFrame(tab, fg_color=\"transparent\")\n 1295→ cards_frame.pack(fill=\"x\", padx=8, pady=4)\n 1296→\n 1297→ kpi_info = [\n 1298→ (\"Avg Replicas\", \"lower\", \"Average number of running replicas\"),\n 1299→ (\"Replica-Min\", \"lower\", \"Total replica-minutes consumed\"),\n 1300→ (\n 1301→ \"SLA Violations\",\n 1302→ \"lower\",\n 1303→ \"Steps where CPU exceeded SLA threshold\",\n 1304→ ),\n 1305→ (\n 1306→ \"Scaling Events\",\n 1307→ \"lower\",\n 1308→ \"Number of replica count changes\",\n 1309→ ),\n 1310→ (\n 1311→ \"Over-Prov %\",\n 1312→ \"lower\",\n 1313→ \"Percentage of over-provisioned capacity\",\n 1314→ ),\n 1315→ (\n 1316→ \"Under-Prov %\",\n 1317→ \"lower\",\n 1318→ \"Percentage of under-provisioned capacity\",\n 1319→ ),\n 1320→ (\n 1321→ \"Reaction Time\",\n 1322→ \"lower\",\n 1323→ \"Avg steps to respond to demand spike\",\n 1324→ ),\n 1325→ (\n 1326→ \"Cost Savings %\",\n 1327→ \"higher\",\n 1328→ \"ML cost savings vs HPA (negative = higher cost)\",\n 1329→ ),\n 1330→ ]\n 1331→ self._kpi_cards: list[KPICard] = []\n 1332→ self._kpi_better: list[str] = []\n 1333→ for i, (name, better, tooltip) in enumerate(kpi_info):\n 1334→ card = KPICard(cards_frame, name, tooltip=tooltip)\n 1335→ card.grid(\n 1336→ row=i // 4, column=i % 4, padx=4, pady=4, sticky=\"nsew\"\n 1337→ )\n 1338→ self._kpi_cards.append(card)\n 1339→ self._kpi_better.append(better)\n 1340→ for c in range(4):\n 1341→ cards_frame.columnconfigure(c, weight=1)\n 1342→\n 1343→ # --- Tab 4: Activity Log ---\n 1344→\n 1345→ def _build_log_tab(self) -> None:\n 1346→ tab = self._tab_log\n 1347→\n 1348→ ctk.CTkLabel(\n 1349→ tab,\n 1350→ text=\"Activity & Notification Feed\",\n 1351→ font=(\"Consolas\", 14, \"bold\"),\n 1352→ text_color=C[\"text\"],\n 1353→ ).pack(pady=(8, 4), padx=8, anchor=\"w\")\n 1354→\n 1355→ ctk.CTkLabel(\n 1356→ tab,\n 1357→ text=\"Scaling events, alerts, and system notifications\",\n 1358→ font=(\"Consolas\", 10),\n 1359→ text_color=C[\"text_dim\"],\n 1360→ ).pack(padx=8, anchor=\"w\")\n 1361→\n 1362→ self._notif_frame = ctk.CTkScrollableFrame(\n 1363→ tab, fg_color=C[\"bg\"], corner_radius=8\n 1364→ )\n 1365→ self._notif_frame.pack(\n 1366→ fill=\"both\", expand=True, padx=8, pady=8\n 1367→ )\n 1368→\n 1369→ # -------------------------------------------------------------------\n 1370→ # Update all visuals\n 1371→ # -------------------------------------------------------------------\n 1372→\n 1373→ def _update_all(self) -> None:\n 1374→ sc = self._scenarios[self._current_scenario_idx]\n 1375→ total = len(sc.actual_cpu)\n 1376→ self._step_lbl.configure(text=f\"Step: {self._step} / {total}\")\n 1377→\n 1378→ # Time display (5 min per step)\n 1379→ minutes = self._step * 5\n 1380→ hours = minutes // 60\n 1381→ mins = minutes % 60\n 1382→ self._time_lbl.configure(text=f\"{hours:02d}:{mins:02d}\")\n 1383→\n 1384→ # Timeline\n 1385→ progress = self._step / total if total > 0 else 0\n 1386→ self._timeline_bar.set(progress)\n 1387→ self._timeline_pct.configure(text=f\"{progress * 100:.0f}%\")\n 1388→\n 1389→ # Highlight active scenario button\n 1390→ for i, btn in enumerate(self._scenario_btns):\n 1391→ btn.configure(\n 1392→ fg_color=C[\"blue\"]\n 1393→ if i == self._current_scenario_idx\n 1394→ else C[\"bg_card\"]\n 1395→ )\n 1396→\n 1397→ # Update scenario description\n 1398→ self._scenario_desc.configure(text=sc.description)\n 1399→\n 1400→ self._update_nn_tab()\n 1401→ self._update_algo_tab()\n 1402→ self._update_exp_tab()\n 1403→ self._update_log_tab()\n 1404→\n 1405→ def _update_nn_tab(self) -> None:\n 1406→ n = self._step\n 1407→\n 1408→ # Buffer status\n 1409→ buf_fill = min(n, 24)\n 1410→ self._buf_progress.set(buf_fill / 24)\n 1411→ ready = n >= 24\n 1412→ self._buf_lbl.configure(\n 1413→ text=f\"{min(n, 24)} / 24 {'READY' if ready else 'FILLING...'}\",\n 1414→ text_color=C[\"green\"] if ready else C[\"red\"],\n 1415→ )\n 1416→\n 1417→ # Current prediction\n 1418→ if n > 0:\n 1419→ pred = self._predicted_trace[-1]\n 1420→ actual = self._actual_trace[-1]\n 1421→ color = (\n 1422→ C[\"green\"]\n 1423→ if pred < 42\n 1424→ else C[\"yellow\"]\n 1425→ if pred < 66\n 1426→ else C[\"orange\"]\n 1427→ if pred < 85\n 1428→ else C[\"red\"]\n 1429→ )\n 1430→ self._pred_big.configure(\n 1431→ text=f\"{pred:.1f}%\", text_color=color\n 1432→ )\n 1433→ self._pred_actual.configure(text=f\"Actual: {actual:.1f}%\")\n 1434→\n 1435→ # Raw metrics (simulated from CPU)\n 1436→ metrics_vals = {\n 1437→ \"cpu_usage\": (actual, 100),\n 1438→ \"mem_util\": (actual * 0.6, 100),\n 1439→ \"net_in\": (actual * 1.2, 120),\n 1440→ \"net_out\": (actual * 0.8, 80),\n 1441→ \"disk_io\": (actual * 0.3, 30),\n 1442→ }\n 1443→ for metric, (val, max_val) in metrics_vals.items():\n 1444→ unit = \"%\" if \"usage\" in metric or \"util\" in metric or \"io\" in metric else \" KB/s\"\n 1445→ self._raw_labels[metric].configure(\n 1446→ text=f\"{metric}: {val:.1f}{unit}\"\n 1447→ )\n 1448→ bar_key = f\"{metric}_bar\"\n 1449→ if bar_key in self._raw_labels:\n 1450→ bar_val = min(val / max_val, 1.0) if max_val > 0 else 0\n 1451→ self._raw_labels[bar_key].set(bar_val)\n 1452→\n 1453→ # Prediction accuracy\n 1454→ errors = np.abs(\n 1455→ np.array(self._actual_trace)\n 1456→ - np.array(self._predicted_trace)\n 1457→ )\n 1458→ mae = float(np.mean(errors))\n 1459→ rmse = float(np.sqrt(np.mean(errors**2)))\n 1460→ cur_err = abs(actual - pred)\n 1461→\n 1462→ mae_color = (\n 1463→ C[\"green\"]\n 1464→ if mae < 5\n 1465→ else C[\"yellow\"]\n 1466→ if mae < 10\n 1467→ else C[\"red\"]\n 1468→ )\n 1469→ self._acc_mae_lbl.configure(\n 1470→ text=f\"MAE: {mae:.2f}%\", text_color=mae_color\n 1471→ )\n 1472→ self._acc_rmse_lbl.configure(\n 1473→ text=f\"RMSE: {rmse:.2f}%\", text_color=mae_color\n 1474→ )\n 1475→ err_color = (\n 1476→ C[\"green\"]\n 1477→ if cur_err < 5\n 1478→ else C[\"yellow\"]\n 1479→ if cur_err < 10\n 1480→ else C[\"red\"]\n 1481→ )\n 1482→ self._acc_err_lbl.configure(\n 1483→ text=f\"Error: {cur_err:.2f}%\", text_color=err_color\n 1484→ )\n 1485→\n 1486→ # Prediction history chart\n 1487→ self._nn_ax.clear()\n 1488→ if n > 1:\n 1489→ window = 80\n 1490→ x = list(range(max(0, n - window), n))\n 1491→ a_slice = self._actual_trace[max(0, n - window):]\n 1492→ p_slice = self._predicted_trace[max(0, n - window):]\n 1493→ self._nn_ax.plot(\n 1494→ x, a_slice, color=C[\"green\"], lw=1.5, label=\"Actual CPU\"\n 1495→ )\n 1496→ self._nn_ax.plot(\n 1497→ x,\n 1498→ p_slice,\n 1499→ color=C[\"purple\"],\n 1500→ lw=1.5,\n 1501→ ls=\"--\",\n 1502→ label=\"Predicted\",\n 1503→ )\n 1504→ # Fill error band\n 1505→ self._nn_ax.fill_between(\n 1506→ x, a_slice, p_slice, alpha=0.12, color=C[\"purple\"]\n 1507→ )\n 1508→ self._nn_ax.axhline(\n 1509→ 60, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.5\n 1510→ )\n 1511→ self._nn_ax.set_ylabel(\"CPU %\")\n 1512→ self._nn_ax.set_ylim(0, 100)\n 1513→ self._nn_ax.legend(loc=\"upper right\", fontsize=8)\n 1514→ self._nn_ax.grid(True, alpha=0.2)\n 1515→ self._nn_ax.set_title(\n 1516→ f\"Prediction History (last 80 steps) | Step {n}\",\n 1517→ fontsize=10,\n 1518→ )\n 1519→ self._nn_fig.tight_layout()\n 1520→ self._nn_canvas.draw_idle()\n 1521→\n 1522→ def _update_algo_tab(self) -> None:\n 1523→ n = self._step\n 1524→ if n == 0:\n 1525→ for block in self._algo_blocks:\n 1526→ block.update(\"\\u2014\", \"skipped\")\n 1527→ return\n 1528→\n 1529→ # Get current step details\n 1530→ pred = self._predicted_trace[-1]\n 1531→ smoothed = self._smoothed_trace[-1]\n 1532→ ml_replicas = self._ml_replicas_trace[-1]\n 1533→ prev_replicas = (\n 1534→ self._ml_replicas_trace[-2] if n > 1 else 2\n 1535→ )\n 1536→\n 1537→ target = self._target_var.get()\n 1538→ up_thresh = target * self._up_thresh_var.get()\n 1539→ down_thresh = target * self._down_thresh_var.get()\n 1540→\n 1541→ # Determine direction\n 1542→ if smoothed > up_thresh:\n 1543→ direction = \"UP\"\n 1544→ elif smoothed < down_thresh:\n 1545→ direction = \"DOWN\"\n 1546→ else:\n 1547→ direction = \"HOLD\"\n 1548→\n 1549→ # Update blocks\n 1550→ self._algo_blocks[0].update(\n 1551→ f\"{pred:.1f}\\u2192{smoothed:.1f}%\", \"active\"\n 1552→ )\n 1553→ cmp_sym = (\n 1554→ \">\"\n 1555→ if direction == \"UP\"\n 1556→ else \"<\"\n 1557→ if direction == \"DOWN\"\n 1558→ else \"in\"\n 1559→ )\n 1560→ self._algo_blocks[1].update(\n 1561→ f\"{smoothed:.0f}% {cmp_sym} \"\n 1562→ f\"[{down_thresh:.0f},{up_thresh:.0f}]\"\n 1563→ f\"\\n\\u2192{direction}\",\n 1564→ \"active\" if direction != \"HOLD\" else \"blocked\",\n 1565→ )\n 1566→\n 1567→ min_r = int(self._min_rep_var.get())\n 1568→ max_r = int(self._max_rep_var.get())\n 1569→\n 1570→ if direction == \"HOLD\":\n 1571→ for i in range(2, 9):\n 1572→ self._algo_blocks[i].update(\"\\u2014\", \"skipped\")\n 1573→ else:\n 1574→ raw_desired = math.ceil(prev_replicas * smoothed / target)\n 1575→ if direction == \"UP\":\n 1576→ raw_desired = math.ceil(\n 1577→ raw_desired * (1 + self._safety_var.get())\n 1578→ )\n 1579→ self._algo_blocks[2].update(\n 1580→ f\"{prev_replicas}\\u00d7{smoothed:.0f}\"\n 1581→ f\"/{target:.0f}\\n={raw_desired}\",\n 1582→ \"active\",\n 1583→ )\n 1584→ clamped = max(min_r, min(max_r, raw_desired))\n 1585→ self._algo_blocks[3].update(\n 1586→ f\"[{min_r},{max_r}]\\u2192{clamped}\", \"active\"\n 1587→ )\n 1588→ rate_lim = (\n 1589→ min(clamped, prev_replicas + 2)\n 1590→ if direction == \"UP\"\n 1591→ else max(clamped, prev_replicas - 1)\n 1592→ )\n 1593→ self._algo_blocks[4].update(\n 1594→ f\"\\u00b1limit\\u2192{rate_lim}\", \"active\"\n 1595→ )\n 1596→ self._algo_blocks[5].update(\"OFF \\u2713\", \"active\")\n 1597→ self._algo_blocks[6].update(\n 1598→ \"N/A\" if direction == \"UP\" else \"check\", \"active\"\n 1599→ )\n 1600→\n 1601→ executed = ml_replicas != prev_replicas\n 1602→ eq_sym = \"\\u2260\" if executed else \"=\"\n 1603→ self._algo_blocks[7].update(\n 1604→ f\"{ml_replicas}{eq_sym}{prev_replicas}\",\n 1605→ \"active\" if executed else \"blocked\",\n 1606→ )\n 1607→ if executed:\n 1608→ self._algo_blocks[8].update(\n 1609→ f\"{prev_replicas}\\u2192{ml_replicas}\", \"executed\"\n 1610→ )\n 1611→ else:\n 1612→ self._algo_blocks[8].update(\"hold\", \"blocked\")\n 1613→\n 1614→ # State panel\n 1615→ sim = self._ml_sim\n 1616→ dir_color = {\n 1617→ \"UP\": C[\"green\"],\n 1618→ \"DOWN\": C[\"orange\"],\n 1619→ \"HOLD\": C[\"text_dim\"],\n 1620→ }.get(direction, C[\"text_dim\"])\n 1621→ self._state_lbl.configure(\n 1622→ text=f\"Direction: {direction} | \"\n 1623→ f\"Consec. Below: {sim._consecutive_below} | \"\n 1624→ f\"Replicas: {ml_replicas} | \"\n 1625→ f\"Smoothed: {smoothed:.1f}%\",\n 1626→ text_color=dir_color,\n 1627→ )\n 1628→\n 1629→ # CPU chart\n 1630→ self._algo_cpu_ax.clear()\n 1631→ if n > 1:\n 1632→ x = list(range(n))\n 1633→ self._algo_cpu_ax.plot(\n 1634→ x,\n 1635→ self._smoothed_trace,\n 1636→ color=C[\"blue\"],\n 1637→ lw=1.5,\n 1638→ label=\"Smoothed\",\n 1639→ )\n 1640→ self._algo_cpu_ax.plot(\n 1641→ x,\n 1642→ self._actual_trace,\n 1643→ color=C[\"green\"],\n 1644→ lw=1,\n 1645→ alpha=0.5,\n 1646→ label=\"Actual\",\n 1647→ )\n 1648→ self._algo_cpu_ax.axhline(\n 1649→ up_thresh,\n 1650→ color=C[\"red\"],\n 1651→ ls=\"--\",\n 1652→ lw=0.8,\n 1653→ label=f\"Up {up_thresh:.0f}%\",\n 1654→ )\n 1655→ self._algo_cpu_ax.axhline(\n 1656→ down_thresh,\n 1657→ color=C[\"orange\"],\n 1658→ ls=\"--\",\n 1659→ lw=0.8,\n 1660→ label=f\"Down {down_thresh:.0f}%\",\n 1661→ )\n 1662→ self._algo_cpu_ax.axhline(\n 1663→ target, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.6\n 1664→ )\n 1665→ self._algo_cpu_ax.axhspan(\n 1666→ down_thresh, up_thresh, alpha=0.08, color=C[\"yellow\"]\n 1667→ )\n 1668→ self._algo_cpu_ax.set_ylim(0, 100)\n 1669→ self._algo_cpu_ax.set_ylabel(\"CPU %\")\n 1670→ self._algo_cpu_ax.legend(loc=\"upper right\", fontsize=7)\n 1671→ self._algo_cpu_ax.grid(True, alpha=0.2)\n 1672→ self._algo_cpu_ax.set_title(\"CPU + Thresholds\", fontsize=10)\n 1673→ self._algo_cpu_fig.tight_layout()\n 1674→ self._algo_cpu_canvas.draw_idle()\n 1675→\n 1676→ # Replica chart\n 1677→ self._algo_rep_ax.clear()\n 1678→ if n > 1:\n 1679→ x = list(range(n))\n 1680→ self._algo_rep_ax.step(\n 1681→ x,\n 1682→ self._ml_replicas_trace,\n 1683→ color=C[\"blue\"],\n 1684→ lw=2,\n 1685→ where=\"post\",\n 1686→ label=\"ML Replicas\",\n 1687→ )\n 1688→ self._algo_rep_ax.step(\n 1689→ x,\n 1690→ self._hpa_replicas_trace,\n 1691→ color=C[\"red\"],\n 1692→ lw=1.5,\n 1693→ where=\"post\",\n 1694→ alpha=0.6,\n 1695→ label=\"HPA Replicas\",\n 1696→ )\n 1697→ self._algo_rep_ax.axhline(\n 1698→ min_r,\n 1699→ color=C[\"text_dim\"],\n 1700→ ls=\":\",\n 1701→ lw=0.7,\n 1702→ alpha=0.5,\n 1703→ )\n 1704→ self._algo_rep_ax.axhline(\n 1705→ max_r,\n 1706→ color=C[\"text_dim\"],\n 1707→ ls=\":\",\n 1708→ lw=0.7,\n 1709→ alpha=0.5,\n 1710→ )\n 1711→ self._algo_rep_ax.set_ylim(0, max_r + 2)\n 1712→ self._algo_rep_ax.set_ylabel(\"Replicas\")\n 1713→ self._algo_rep_ax.legend(loc=\"upper right\", fontsize=7)\n 1714→ self._algo_rep_ax.grid(True, alpha=0.2)\n 1715→ self._algo_rep_ax.set_title(\"Replica Count\", fontsize=10)\n 1716→ self._algo_rep_fig.tight_layout()\n 1717→ self._algo_rep_canvas.draw_idle()\n 1718→\n 1719→ # Decision log\n 1720→ self._decision_log.configure(state=\"normal\")\n 1721→ self._decision_log.delete(\"1.0\", \"end\")\n 1722→ for line in self._decisions_log[-20:]:\n 1723→ self._decision_log.insert(\"end\", line + \"\\n\")\n 1724→ self._decision_log.configure(state=\"disabled\")\n 1725→ self._decision_log.see(\"end\")\n 1726→\n 1727→ def _update_exp_tab(self) -> None:\n 1728→ if not hasattr(self, \"_full_metrics\"):\n 1729→ return\n 1730→ metrics = self._full_metrics\n 1731→ history = self._history\n 1732→\n 1733→ ax1, ax2, ax3 = self._exp_axes\n 1734→ ax1.clear()\n 1735→ ax2.clear()\n 1736→ ax3.clear()\n 1737→\n 1738→ n = len(history)\n 1739→ if n > 0:\n 1740→ x = list(range(n))\n 1741→ actual = [r.actual_cpu for r in history]\n 1742→ hpa_r = [r.hpa_replicas for r in history]\n 1743→ ml_r = [r.ml_replicas for r in history]\n 1744→\n 1745→ # Chart 1: CPU + Replicas overlay\n 1746→ ax1.fill_between(\n 1747→ x, actual, alpha=0.15, color=C[\"text_dim\"]\n 1748→ )\n 1749→ ax1.plot(\n 1750→ x,\n 1751→ actual,\n 1752→ color=C[\"text_dim\"],\n 1753→ lw=0.8,\n 1754→ alpha=0.5,\n 1755→ label=\"Actual CPU\",\n 1756→ )\n 1757→ ax1.step(\n 1758→ x,\n 1759→ [r * 10 for r in hpa_r],\n 1760→ color=C[\"red\"],\n 1761→ lw=1.5,\n 1762→ where=\"post\",\n 1763→ label=\"HPA Replicas (\\u00d710)\",\n 1764→ )\n 1765→ ax1.step(\n 1766→ x,\n 1767→ [r * 10 for r in ml_r],\n 1768→ color=C[\"blue\"],\n 1769→ lw=2,\n 1770→ where=\"post\",\n 1771→ label=\"ML Replicas (\\u00d710)\",\n 1772→ )\n 1773→ target = self._target_var.get()\n 1774→ ax1.axhline(\n 1775→ target * 1.2,\n 1776→ color=C[\"red\"],\n 1777→ ls=\":\",\n 1778→ lw=0.8,\n 1779→ alpha=0.5,\n 1780→ label=\"SLA Limit\",\n 1781→ )\n 1782→ ax1.set_ylabel(\"CPU % / Replicas\\u00d710\")\n 1783→ ax1.set_ylim(0, 100)\n 1784→ ax1.legend(loc=\"upper right\", fontsize=7, ncol=2)\n 1785→ sc = self._scenarios[self._current_scenario_idx]\n 1786→ ax1.set_title(\n 1787→ f\"Experiment: {sc.name}\", fontsize=11\n 1788→ )\n 1789→ ax1.grid(True, alpha=0.2)\n 1790→\n 1791→ # Chart 2: Replica comparison (no multiplier)\n 1792→ ax2.step(\n 1793→ x,\n 1794→ hpa_r,\n 1795→ color=C[\"red\"],\n 1796→ lw=1.5,\n 1797→ where=\"post\",\n 1798→ label=\"HPA\",\n 1799→ )\n 1800→ ax2.step(\n 1801→ x,\n 1802→ ml_r,\n 1803→ color=C[\"blue\"],\n 1804→ lw=2,\n 1805→ where=\"post\",\n 1806→ label=\"ML\",\n 1807→ )\n 1808→ ax2.set_ylabel(\"Replicas\")\n 1809→ ax2.legend(loc=\"upper right\", fontsize=7)\n 1810→ ax2.grid(True, alpha=0.2)\n 1811→ ax2.set_title(\"Replica Count Comparison\", fontsize=9)\n 1812→\n 1813→ # Chart 3: Replica diff\n 1814→ diff = [m - h for m, h in zip(ml_r, hpa_r)]\n 1815→ colors_bar = [\n 1816→ C[\"green\"]\n 1817→ if d < 0\n 1818→ else C[\"red\"]\n 1819→ if d > 0\n 1820→ else C[\"text_dim\"]\n 1821→ for d in diff\n 1822→ ]\n 1823→ ax3.bar(x, diff, color=colors_bar, alpha=0.7, width=1.0)\n 1824→ ax3.axhline(0, color=C[\"text_dim\"], lw=0.5)\n 1825→ ax3.set_ylabel(\"ML - HPA\")\n 1826→ ax3.set_xlabel(\"Step\")\n 1827→ ax3.grid(True, alpha=0.2)\n 1828→ ax3.set_title(\n 1829→ \"Replica Difference (negative = ML uses fewer)\",\n 1830→ fontsize=9,\n 1831→ )\n 1832→\n 1833→ self._exp_fig.tight_layout()\n 1834→ self._exp_canvas.draw_idle()\n 1835→\n 1836→ # KPI cards\n 1837→ kpi_data = [\n 1838→ (\n 1839→ f\"{metrics.hpa_avg_replicas:.1f}\",\n 1840→ f\"{metrics.ml_avg_replicas:.1f}\",\n 1841→ ),\n 1842→ (\n 1843→ f\"{metrics.hpa_replica_minutes:.0f}\",\n 1844→ f\"{metrics.ml_replica_minutes:.0f}\",\n 1845→ ),\n 1846→ (\n 1847→ f\"{metrics.hpa_sla_violations}\",\n 1848→ f\"{metrics.ml_sla_violations}\",\n 1849→ ),\n 1850→ (\n 1851→ f\"{metrics.hpa_scaling_events}\",\n 1852→ f\"{metrics.ml_scaling_events}\",\n 1853→ ),\n 1854→ (\n 1855→ f\"{metrics.hpa_over_provisioning_pct:.1f}%\",\n 1856→ f\"{metrics.ml_over_provisioning_pct:.1f}%\",\n 1857→ ),\n 1858→ (\n 1859→ f\"{metrics.hpa_under_provisioning_pct:.1f}%\",\n 1860→ f\"{metrics.ml_under_provisioning_pct:.1f}%\",\n 1861→ ),\n 1862→ (\n 1863→ f\"{metrics.hpa_avg_reaction_time:.1f}\",\n 1864→ f\"{metrics.ml_avg_reaction_time:.1f}\",\n 1865→ ),\n 1866→ (\n 1867→ \"\\u2014\",\n 1868→ f\"{metrics.cost_savings_pct:+.1f}%\",\n 1869→ ),\n 1870→ ]\n 1871→ for i, (hpa_val, ml_val) in enumerate(kpi_data):\n 1872→ self._kpi_cards[i].set_values(\n 1873→ hpa_val, ml_val, better=self._kpi_better[i]\n 1874→ )\n 1875→\n 1876→ def _update_log_tab(self) -> None:\n 1877→ \"\"\"Update the activity log / notification feed tab.\"\"\"\n 1878→ # Clear and rebuild (only last 30 for performance)\n 1879→ for widget in self._notif_frame.winfo_children():\n 1880→ widget.destroy()\n 1881→ for text, level in self._notifications[-30:]:\n 1882→ NotificationItem(self._notif_frame, text, level).pack(\n 1883→ fill=\"x\", padx=4, pady=1\n 1884→ )\n 1885→\n 1886→\n 1887→# ---------------------------------------------------------------------------\n 1888→# Entry point\n 1889→# ---------------------------------------------------------------------------\n 1890→\n 1891→\n 1892→def main() -> None:\n 1893→ \"\"\"Launch the experiment dashboard application.\"\"\"\n 1894→ app = ExperimentApp()\n 1895→ app.mainloop()\n 1896→\n 1897→\n 1898→if __name__ == \"__main__\":\n 1899→ main()\n 1900→" | |
| } | |
| ] | |
| }, | |
| "parent_tool_use_id": null, | |
| "session_id": "ce1ab353-0721-47e7-a373-84e062534c7f", | |
| "uuid": "ac4a01d5-bc37-4ebe-9491-b56f20149dfa", | |
| "tool_use_result": { | |
| "type": "update", | |
| "filePath": "/tmp/gh-issue-solver-1770487990892/scripts/experiment_gui.py", | |
| "content": "\"\"\"ML Autoscaler — Experiment Dashboard (CustomTkinter).\n\nInteractive visualization of:\n - Neural network internals (buffer, features, ONNX inference)\n - Scaling algorithm 9-step pipeline\n - HPA vs ML autoscaler comparison\n\nFeatures:\n - Keyboard shortcuts (Space=play/pause, Right=step, R=reset, 1-4=scenario)\n - Tooltips on pipeline blocks and KPI cards\n - Live prediction accuracy metrics (MAE, RMSE)\n - Animated status indicators\n - Scenario description panel\n - Timeline progress bar\n - Export charts / data to file\n - Notification feed for scaling events\n - Configurable min/max replica range\n\nUsage:\n python scripts/experiment_gui.py\n\"\"\"\n\nfrom __future__ import annotations\n\nimport csv\nimport math\nimport sys\nimport time\nfrom pathlib import Path\nfrom tkinter import filedialog\n\nimport customtkinter as ctk\nimport matplotlib\nimport numpy as np\n\nmatplotlib.use(\"TkAgg\")\nimport matplotlib.pyplot as plt # noqa: E402\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg # noqa: E402\nfrom matplotlib.figure import Figure # noqa: E402\n\n# Add project root to path for imports\nROOT = Path(__file__).resolve().parent.parent\nsys.path.insert(0, str(ROOT / \"src\"))\nsys.path.insert(0, str(ROOT))\n\nfrom scripts.experiment_scenarios import Scenario, get_all_scenarios # noqa: E402\nfrom scripts.experiment_simulator import ( # noqa: E402\n ExperimentRunner,\n MLAutoscalerSimulator,\n MLConfig,\n StepRecord,\n)\n\n# ---------------------------------------------------------------------------\n# Color palette (unified with Grafana dashboards)\n# ---------------------------------------------------------------------------\n\nC = {\n \"bg\": \"#1a1a2e\",\n \"bg_card\": \"#16213e\",\n \"sidebar\": \"#0f3460\",\n \"accent\": \"#e94560\",\n \"green\": \"#73BF69\",\n \"yellow\": \"#FADE2A\",\n \"orange\": \"#FF9830\",\n \"red\": \"#F2495C\",\n \"blue\": \"#5794F2\",\n \"purple\": \"#B877D9\",\n \"cyan\": \"#8AB8FF\",\n \"text\": \"#e0e0e0\",\n \"text_dim\": \"#8892b0\",\n \"grid\": \"#2a2a4a\",\n \"success\": \"#73BF69\",\n \"warning\": \"#FADE2A\",\n \"danger\": \"#F2495C\",\n}\n\n# Matplotlib dark style\nplt.rcParams.update(\n {\n \"figure.facecolor\": C[\"bg_card\"],\n \"axes.facecolor\": C[\"bg\"],\n \"axes.edgecolor\": C[\"grid\"],\n \"axes.labelcolor\": C[\"text\"],\n \"text.color\": C[\"text\"],\n \"xtick.color\": C[\"text_dim\"],\n \"ytick.color\": C[\"text_dim\"],\n \"grid.color\": C[\"grid\"],\n \"grid.alpha\": 0.3,\n \"font.size\": 9,\n \"legend.facecolor\": C[\"bg_card\"],\n \"legend.edgecolor\": C[\"grid\"],\n }\n)\n\n\n# ---------------------------------------------------------------------------\n# Utility: embed a matplotlib figure in a CTkFrame\n# ---------------------------------------------------------------------------\n\n\ndef embed_figure(parent: ctk.CTkFrame, fig: Figure) -> FigureCanvasTkAgg:\n \"\"\"Embed a matplotlib Figure into a CustomTkinter frame.\"\"\"\n canvas = FigureCanvasTkAgg(fig, master=parent)\n canvas.draw()\n canvas.get_tk_widget().pack(fill=\"both\", expand=True)\n return canvas\n\n\n# ---------------------------------------------------------------------------\n# Tooltip widget\n# ---------------------------------------------------------------------------\n\n\nclass Tooltip:\n \"\"\"Hover tooltip for any widget.\"\"\"\n\n def __init__(self, widget: ctk.CTkBaseClass, text: str) -> None:\n self._widget = widget\n self._text = text\n self._tip_window: ctk.CTkToplevel | None = None\n widget.bind(\"<Enter>\", self._show)\n widget.bind(\"<Leave>\", self._hide)\n\n def _show(self, event: object = None) -> None:\n if self._tip_window:\n return\n x = self._widget.winfo_rootx() + 20\n y = self._widget.winfo_rooty() + self._widget.winfo_height() + 4\n tw = ctk.CTkToplevel(self._widget)\n tw.wm_overrideredirect(True)\n tw.wm_geometry(f\"+{x}+{y}\")\n tw.attributes(\"-topmost\", True)\n lbl = ctk.CTkLabel(\n tw,\n text=self._text,\n font=(\"Consolas\", 10),\n fg_color=C[\"bg_card\"],\n text_color=C[\"text\"],\n corner_radius=6,\n wraplength=280,\n )\n lbl.pack(padx=6, pady=4)\n self._tip_window = tw\n\n def _hide(self, event: object = None) -> None:\n if self._tip_window:\n self._tip_window.destroy()\n self._tip_window = None\n\n\n# ---------------------------------------------------------------------------\n# KPI Card widget (enhanced with delta indicator)\n# ---------------------------------------------------------------------------\n\n\nclass KPICard(ctk.CTkFrame):\n \"\"\"Compact KPI card: title + HPA value + ML value + delta arrow.\"\"\"\n\n def __init__(self, master: ctk.CTkFrame, title: str, tooltip: str = \"\", **kw: object):\n super().__init__(master, fg_color=C[\"bg_card\"], corner_radius=8, **kw)\n self._title_lbl = ctk.CTkLabel(\n self, text=title, font=(\"Consolas\", 11), text_color=C[\"text_dim\"]\n )\n self._title_lbl.pack(pady=(6, 2))\n\n row = ctk.CTkFrame(self, fg_color=\"transparent\")\n row.pack(pady=(0, 2))\n self._hpa_lbl = ctk.CTkLabel(\n row, text=\"HPA: --\", font=(\"Consolas\", 13, \"bold\"), text_color=C[\"red\"]\n )\n self._hpa_lbl.pack(side=\"left\", padx=6)\n self._ml_lbl = ctk.CTkLabel(\n row, text=\"ML: --\", font=(\"Consolas\", 13, \"bold\"), text_color=C[\"blue\"]\n )\n self._ml_lbl.pack(side=\"left\", padx=6)\n\n # Delta indicator\n self._delta_lbl = ctk.CTkLabel(\n self, text=\"\", font=(\"Consolas\", 9), text_color=C[\"text_dim\"]\n )\n self._delta_lbl.pack(pady=(0, 4))\n\n if tooltip:\n Tooltip(self, tooltip)\n\n def set_values(\n self,\n hpa_val: str,\n ml_val: str,\n better: str = \"lower\",\n ) -> None:\n \"\"\"Update card values with automatic delta comparison.\n\n Args:\n hpa_val: HPA metric value string.\n ml_val: ML metric value string.\n better: Direction that is \"better\" — \"lower\" or \"higher\".\n \"\"\"\n self._hpa_lbl.configure(text=f\"HPA: {hpa_val}\")\n self._ml_lbl.configure(text=f\"ML: {ml_val}\")\n\n # Parse numeric values for delta\n try:\n hpa_num = float(hpa_val.replace(\"%\", \"\").replace(\"+\", \"\").replace(\",\", \"\"))\n ml_num = float(ml_val.replace(\"%\", \"\").replace(\"+\", \"\").replace(\",\", \"\"))\n diff = ml_num - hpa_num\n if abs(diff) < 0.01:\n self._delta_lbl.configure(text=\"= equal\", text_color=C[\"text_dim\"])\n else:\n is_better = (diff < 0) if better == \"lower\" else (diff > 0)\n arrow = \"v\" if diff < 0 else \"^\"\n color = C[\"green\"] if is_better else C[\"red\"]\n self._delta_lbl.configure(\n text=f\"{arrow} {abs(diff):.1f}\", text_color=color\n )\n except (ValueError, AttributeError):\n self._delta_lbl.configure(text=\"\", text_color=C[\"text_dim\"])\n\n\n# ---------------------------------------------------------------------------\n# Pipeline Step block (enhanced with tooltip)\n# ---------------------------------------------------------------------------\n\n\nclass PipelineBlock(ctk.CTkFrame):\n \"\"\"One step in the 9-step scaling algorithm visualization.\"\"\"\n\n # Descriptions for each pipeline step\n STEP_TOOLTIPS = {\n 1: \"EMA: Exponential moving average smoothing. \"\n \"Smoothes predicted CPU to remove noise.\",\n 2: \"Hysteresis: Dead-zone check. \"\n \"Determines UP/DOWN/HOLD based on thresholds.\",\n 3: \"Desired: Calculates target replica count \"\n \"using ceil(current * smoothed / target).\",\n 4: \"Clamp: Restricts replicas to [min, max] range.\",\n 5: \"Rate Limit: Limits change speed. \"\n \"Max +2 up, -1 down per step.\",\n 6: \"Cooldown: Prevents rapid re-scaling. \"\n \"60s for up, 300s for down.\",\n 7: \"Stabilize: Requires 3 consecutive low \"\n \"readings before scaling down.\",\n 8: \"No Change: Skips execution if desired \"\n \"equals current replicas.\",\n 9: \"Commit: Applies the scaling decision \"\n \"and updates cooldown timers.\",\n }\n\n def __init__(self, master: ctk.CTkFrame, step_num: int, title: str, **kw: object):\n super().__init__(\n master,\n fg_color=C[\"bg_card\"],\n corner_radius=6,\n border_width=1,\n border_color=C[\"grid\"],\n **kw,\n )\n self.configure(width=115, height=80)\n self.pack_propagate(False)\n self._step_num = step_num\n ctk.CTkLabel(\n self,\n text=f\"Step {step_num}\",\n font=(\"Consolas\", 8),\n text_color=C[\"text_dim\"],\n ).pack(pady=(4, 0))\n ctk.CTkLabel(\n self,\n text=title,\n font=(\"Consolas\", 9, \"bold\"),\n text_color=C[\"text\"],\n ).pack()\n self._value_lbl = ctk.CTkLabel(\n self,\n text=\"\\u2014\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n wraplength=100,\n )\n self._value_lbl.pack(pady=(2, 4))\n\n # Add tooltip\n tooltip_text = self.STEP_TOOLTIPS.get(step_num, \"\")\n if tooltip_text:\n Tooltip(self, tooltip_text)\n\n def update(self, text: str, status: str = \"active\") -> None:\n \"\"\"Update block display text and status color.\"\"\"\n colors = {\n \"active\": C[\"green\"],\n \"blocked\": C[\"yellow\"],\n \"skipped\": C[\"text_dim\"],\n \"executed\": C[\"accent\"],\n }\n self._value_lbl.configure(\n text=text, text_color=colors.get(status, C[\"text\"])\n )\n border = {\n \"active\": C[\"green\"],\n \"blocked\": C[\"yellow\"],\n \"executed\": C[\"accent\"],\n }\n self.configure(border_color=border.get(status, C[\"grid\"]))\n\n\n# ---------------------------------------------------------------------------\n# Status Indicator (animated dot)\n# ---------------------------------------------------------------------------\n\n\nclass StatusDot(ctk.CTkFrame):\n \"\"\"Animated status dot indicator.\"\"\"\n\n def __init__(self, master: ctk.CTkFrame, **kw: object):\n super().__init__(master, fg_color=\"transparent\", **kw)\n self._dot = ctk.CTkLabel(\n self, text=\"\\u25cf\", font=(\"Consolas\", 14), text_color=C[\"text_dim\"]\n )\n self._dot.pack(side=\"left\", padx=2)\n self._label = ctk.CTkLabel(\n self, text=\"IDLE\", font=(\"Consolas\", 10), text_color=C[\"text_dim\"]\n )\n self._label.pack(side=\"left\", padx=2)\n self._pulse_on = True\n\n def set_status(self, status: str, text: str) -> None:\n \"\"\"Update status indicator.\n\n Args:\n status: One of 'idle', 'running', 'ready', 'warning'.\n text: Label text to display.\n \"\"\"\n color_map = {\n \"idle\": C[\"text_dim\"],\n \"running\": C[\"green\"],\n \"ready\": C[\"blue\"],\n \"warning\": C[\"yellow\"],\n \"error\": C[\"red\"],\n }\n color = color_map.get(status, C[\"text_dim\"])\n self._dot.configure(text_color=color)\n self._label.configure(text=text, text_color=color)\n\n\n# ---------------------------------------------------------------------------\n# Notification Item\n# ---------------------------------------------------------------------------\n\n\nclass NotificationItem(ctk.CTkFrame):\n \"\"\"Single notification entry in the feed.\"\"\"\n\n def __init__(\n self,\n master: ctk.CTkFrame,\n text: str,\n level: str = \"info\",\n **kw: object,\n ):\n super().__init__(\n master, fg_color=C[\"bg_card\"], corner_radius=4, height=24, **kw\n )\n self.pack_propagate(False)\n color = {\n \"info\": C[\"blue\"],\n \"up\": C[\"green\"],\n \"down\": C[\"orange\"],\n \"warning\": C[\"yellow\"],\n }.get(level, C[\"text_dim\"])\n icon = {\n \"info\": \"\\u25b6\",\n \"up\": \"\\u25b2\",\n \"down\": \"\\u25bc\",\n \"warning\": \"\\u26a0\",\n }.get(level, \"\\u25cf\")\n ctk.CTkLabel(\n self,\n text=f\" {icon} {text}\",\n font=(\"Consolas\", 9),\n text_color=color,\n anchor=\"w\",\n ).pack(fill=\"x\", padx=4, pady=2)\n\n\n# ---------------------------------------------------------------------------\n# Main Application\n# ---------------------------------------------------------------------------\n\n\nclass ExperimentApp(ctk.CTk):\n \"\"\"Main GUI application for the ML Autoscaler experiment dashboard.\"\"\"\n\n def __init__(self) -> None:\n super().__init__()\n self.title(\"ML Autoscaler \\u2014 Experiment Dashboard\")\n self.geometry(\"1480x960\")\n ctk.set_appearance_mode(\"dark\")\n\n # State\n self._scenarios: list[Scenario] = []\n self._current_scenario_idx = 0\n self._step = 0\n self._playing = False\n self._speed_ms = 200\n self._history: list[StepRecord] = []\n\n # ML simulator state for step-by-step\n self._ml_sim: MLAutoscalerSimulator | None = None\n self._hpa_replicas_trace: list[int] = []\n self._ml_replicas_trace: list[int] = []\n self._actual_trace: list[float] = []\n self._predicted_trace: list[float] = []\n self._smoothed_trace: list[float] = []\n self._decisions_log: list[str] = []\n\n # Notifications queue\n self._notifications: list[tuple[str, str]] = []\n\n # Build UI\n self._build_controls()\n self._build_sidebar()\n self._build_tabview()\n\n # Bind keyboard shortcuts\n self._bind_shortcuts()\n\n # Load data\n self._load_scenarios()\n\n # -------------------------------------------------------------------\n # Keyboard shortcuts\n # -------------------------------------------------------------------\n\n def _bind_shortcuts(self) -> None:\n \"\"\"Bind keyboard shortcuts for common actions.\"\"\"\n self.bind(\"<space>\", lambda e: self._toggle_play())\n self.bind(\"<Right>\", lambda e: self._do_step())\n self.bind(\"<r>\", lambda e: self._do_reset())\n self.bind(\"<R>\", lambda e: self._do_reset())\n self.bind(\"<1>\", lambda e: self._select_scenario(0))\n self.bind(\"<2>\", lambda e: self._select_scenario(1))\n self.bind(\"<3>\", lambda e: self._select_scenario(2))\n self.bind(\"<4>\", lambda e: self._select_scenario(3))\n self.bind(\n \"<Control-e>\", lambda e: self._export_data()\n )\n self.bind(\n \"<Control-s>\", lambda e: self._export_charts()\n )\n\n # -------------------------------------------------------------------\n # Data loading\n # -------------------------------------------------------------------\n\n def _load_scenarios(self) -> None:\n try:\n self._scenarios = get_all_scenarios(\n ROOT / \"data\" / \"processed\" / \"test.csv\",\n ROOT / \"data\" / \"processed\" / \"scaler.pkl\",\n ROOT / \"data\" / \"processed\" / \"metadata.json\",\n )\n except Exception:\n self._scenarios = [self._synthetic_fallback()]\n # Ensure we have at least 4 scenario buttons\n while len(self._scenarios) < 4:\n self._scenarios.append(self._synthetic_fallback())\n self._select_scenario(0)\n\n def _synthetic_fallback(self) -> Scenario:\n \"\"\"Create a simple synthetic scenario if data files are missing.\"\"\"\n n = 200\n rng = np.random.RandomState(42)\n t = np.linspace(0, 4 * np.pi, n)\n actual = 50 + 25 * np.sin(t) + rng.normal(0, 3, n)\n predicted = np.roll(actual, -3)\n predicted[-3:] = actual[-3:]\n predicted += rng.normal(0, 2, n)\n return Scenario(\n name=\"synthetic_demo\",\n description=\"Synthetic sine wave demo for testing\",\n actual_cpu=np.clip(actual, 10, 100),\n predicted_cpu=np.clip(predicted, 10, 100),\n duration_hours=n * 5 / 60,\n )\n\n def _select_scenario(self, idx: int) -> None:\n if idx >= len(self._scenarios):\n return\n self._current_scenario_idx = idx\n self._reset_simulation()\n self._update_all()\n\n def _do_reset(self) -> None:\n \"\"\"Reset simulation (callable from shortcuts).\"\"\"\n self._reset_simulation()\n self._update_all()\n\n def _reset_simulation(self) -> None:\n self._step = 0\n self._playing = False\n self._notifications = []\n sc = self._scenarios[self._current_scenario_idx]\n\n # Run full experiment for Tab 3\n runner = ExperimentRunner(\n sc.actual_cpu,\n sc.predicted_cpu,\n ml_config=self._build_ml_config(),\n )\n self._history, self._full_metrics = runner.run()\n\n # Reset step-by-step state\n self._ml_sim = MLAutoscalerSimulator(self._build_ml_config())\n self._hpa_replicas_trace = []\n self._ml_replicas_trace = []\n self._actual_trace = []\n self._predicted_trace = []\n self._smoothed_trace = []\n self._decisions_log = []\n\n # Update play button state\n if hasattr(self, \"_play_btn\"):\n self._play_btn.configure(\n text=\"\\u25b6 Play\", fg_color=C[\"green\"]\n )\n\n def _build_ml_config(self) -> MLConfig:\n return MLConfig(\n target_utilization=self._target_var.get(),\n smoothing_alpha=self._alpha_var.get(),\n safety_margin=self._safety_var.get(),\n scale_up_threshold_factor=self._up_thresh_var.get(),\n scale_down_threshold_factor=self._down_thresh_var.get(),\n min_replicas=int(self._min_rep_var.get()),\n max_replicas=int(self._max_rep_var.get()),\n )\n\n # -------------------------------------------------------------------\n # Control bar (top)\n # -------------------------------------------------------------------\n\n def _build_controls(self) -> None:\n bar = ctk.CTkFrame(self, fg_color=C[\"bg_card\"], height=50)\n bar.pack(fill=\"x\", padx=8, pady=(8, 4))\n\n ctk.CTkLabel(\n bar,\n text=\"ML Autoscaler \\u2014 Experiment Dashboard\",\n font=(\"Consolas\", 16, \"bold\"),\n text_color=C[\"text\"],\n ).pack(side=\"left\", padx=12)\n\n # Status dot\n self._status_dot = StatusDot(bar)\n self._status_dot.pack(side=\"left\", padx=8)\n\n # Transport controls\n self._play_btn = ctk.CTkButton(\n bar,\n text=\"\\u25b6 Play\",\n width=90,\n fg_color=C[\"green\"],\n text_color=\"#000\",\n hover_color=\"#5ea554\",\n command=self._toggle_play,\n )\n self._play_btn.pack(side=\"left\", padx=4)\n\n ctk.CTkButton(\n bar,\n text=\"\\u23ed Step\",\n width=90,\n fg_color=C[\"blue\"],\n text_color=\"#000\",\n hover_color=\"#4678c9\",\n command=self._do_step,\n ).pack(side=\"left\", padx=4)\n\n ctk.CTkButton(\n bar,\n text=\"\\u21ba Reset\",\n width=90,\n fg_color=C[\"orange\"],\n text_color=\"#000\",\n hover_color=\"#d97e24\",\n command=self._do_reset,\n ).pack(side=\"left\", padx=4)\n\n # Export buttons\n ctk.CTkButton(\n bar,\n text=\"\\u2913 Export\",\n width=80,\n fg_color=C[\"purple\"],\n text_color=\"#fff\",\n hover_color=\"#9b5fbf\",\n command=self._export_data,\n ).pack(side=\"left\", padx=4)\n\n # Step counter + time display\n self._time_lbl = ctk.CTkLabel(\n bar,\n text=\"00:00\",\n font=(\"Consolas\", 11),\n text_color=C[\"text_dim\"],\n )\n self._time_lbl.pack(side=\"right\", padx=4)\n\n self._step_lbl = ctk.CTkLabel(\n bar,\n text=\"Step: 0 / 0\",\n font=(\"Consolas\", 12),\n text_color=C[\"text_dim\"],\n )\n self._step_lbl.pack(side=\"right\", padx=8)\n\n # Speed slider\n ctk.CTkLabel(bar, text=\"Speed:\", text_color=C[\"text_dim\"]).pack(\n side=\"right\"\n )\n self._speed_slider = ctk.CTkSlider(\n bar,\n from_=50,\n to=1000,\n width=120,\n command=lambda v: setattr(self, \"_speed_ms\", int(v)),\n )\n self._speed_slider.set(200)\n self._speed_slider.pack(side=\"right\", padx=4)\n\n # Shortcuts hint\n Tooltip(\n self._play_btn,\n \"Space: Play/Pause | Right: Step | R: Reset | 1-4: Scenario | \"\n \"Ctrl+E: Export data | Ctrl+S: Save charts\",\n )\n\n # -------------------------------------------------------------------\n # Timeline progress bar\n # -------------------------------------------------------------------\n\n def _build_timeline(self) -> None:\n \"\"\"Build timeline progress bar below tabs.\"\"\"\n frame = ctk.CTkFrame(self, fg_color=C[\"bg_card\"], height=30)\n frame.pack(fill=\"x\", padx=8, pady=(0, 2))\n frame.pack_propagate(False)\n\n self._timeline_bar = ctk.CTkProgressBar(\n frame, width=600, height=8, progress_color=C[\"blue\"]\n )\n self._timeline_bar.pack(side=\"left\", fill=\"x\", expand=True, padx=12, pady=10)\n self._timeline_bar.set(0)\n\n self._timeline_pct = ctk.CTkLabel(\n frame, text=\"0%\", font=(\"Consolas\", 9), text_color=C[\"text_dim\"]\n )\n self._timeline_pct.pack(side=\"right\", padx=8)\n\n def _toggle_play(self) -> None:\n self._playing = not self._playing\n self._play_btn.configure(\n text=\"\\u23f8 Pause\" if self._playing else \"\\u25b6 Play\",\n fg_color=C[\"yellow\"] if self._playing else C[\"green\"],\n )\n self._status_dot.set_status(\n \"running\" if self._playing else \"idle\",\n \"RUNNING\" if self._playing else \"PAUSED\",\n )\n if self._playing:\n self._auto_step()\n\n def _auto_step(self) -> None:\n if not self._playing:\n return\n sc = self._scenarios[self._current_scenario_idx]\n if self._step < len(sc.actual_cpu):\n self._do_step()\n self.after(self._speed_ms, self._auto_step)\n else:\n self._playing = False\n self._play_btn.configure(\n text=\"\\u25b6 Play\", fg_color=C[\"green\"]\n )\n self._status_dot.set_status(\"ready\", \"FINISHED\")\n\n def _do_step(self) -> None:\n sc = self._scenarios[self._current_scenario_idx]\n if self._step >= len(sc.actual_cpu):\n return\n\n t = self._step\n actual = float(sc.actual_cpu[t])\n predicted = float(sc.predicted_cpu[t])\n\n # ML autoscaler step\n ml_replicas, ml_dir = self._ml_sim.step(predicted, t)\n smoothed = self._ml_sim._smoothed or predicted\n\n # HPA (from pre-computed history)\n hpa_replicas = (\n self._history[t].hpa_replicas if t < len(self._history) else 2\n )\n\n self._actual_trace.append(actual)\n self._predicted_trace.append(predicted)\n self._smoothed_trace.append(smoothed)\n self._ml_replicas_trace.append(ml_replicas)\n self._hpa_replicas_trace.append(hpa_replicas)\n self._decisions_log.append(\n f\"[t={t:>3}] {ml_dir:>4} | ML:{ml_replicas} \"\n f\"HPA:{hpa_replicas} | \"\n f\"actual={actual:.1f}% pred={predicted:.1f}% \"\n f\"smooth={smoothed:.1f}%\"\n )\n\n # Generate notification for scaling events\n if ml_dir == \"up\":\n prev = self._ml_replicas_trace[-2] if len(self._ml_replicas_trace) > 1 else 2\n self._add_notification(\n f\"t={t}: ML SCALE UP {prev} -> {ml_replicas}\", \"up\"\n )\n elif ml_dir == \"down\":\n prev = self._ml_replicas_trace[-2] if len(self._ml_replicas_trace) > 1 else 2\n self._add_notification(\n f\"t={t}: ML SCALE DOWN {prev} -> {ml_replicas}\", \"down\"\n )\n\n # Warn if CPU is high\n if actual > 85:\n self._add_notification(\n f\"t={t}: High CPU alert: {actual:.1f}%\", \"warning\"\n )\n\n self._step += 1\n self._update_all()\n\n # -------------------------------------------------------------------\n # Notifications\n # -------------------------------------------------------------------\n\n def _add_notification(self, text: str, level: str = \"info\") -> None:\n \"\"\"Add a notification to the feed (max 50 kept).\"\"\"\n self._notifications.append((text, level))\n if len(self._notifications) > 50:\n self._notifications = self._notifications[-50:]\n\n # -------------------------------------------------------------------\n # Export functionality\n # -------------------------------------------------------------------\n\n def _export_data(self) -> None:\n \"\"\"Export simulation data to CSV file.\"\"\"\n if not self._actual_trace:\n return\n filepath = filedialog.asksaveasfilename(\n defaultextension=\".csv\",\n filetypes=[(\"CSV files\", \"*.csv\"), (\"All files\", \"*.*\")],\n title=\"Export simulation data\",\n )\n if not filepath:\n return\n with open(filepath, \"w\", newline=\"\") as f:\n writer = csv.writer(f)\n writer.writerow(\n [\n \"step\",\n \"actual_cpu\",\n \"predicted_cpu\",\n \"smoothed_cpu\",\n \"ml_replicas\",\n \"hpa_replicas\",\n ]\n )\n for i in range(len(self._actual_trace)):\n writer.writerow(\n [\n i,\n f\"{self._actual_trace[i]:.2f}\",\n f\"{self._predicted_trace[i]:.2f}\",\n f\"{self._smoothed_trace[i]:.2f}\",\n self._ml_replicas_trace[i],\n self._hpa_replicas_trace[i],\n ]\n )\n self._add_notification(f\"Data exported to {filepath}\", \"info\")\n\n def _export_charts(self) -> None:\n \"\"\"Export current charts as PNG images.\"\"\"\n filepath = filedialog.asksaveasfilename(\n defaultextension=\".png\",\n filetypes=[(\"PNG images\", \"*.png\"), (\"All files\", \"*.*\")],\n title=\"Save charts\",\n )\n if not filepath:\n return\n # Save all figures\n base = Path(filepath).stem\n parent = Path(filepath).parent\n figures = {\n \"nn\": self._nn_fig,\n \"algo_cpu\": self._algo_cpu_fig,\n \"algo_rep\": self._algo_rep_fig,\n \"experiment\": self._exp_fig,\n }\n for name, fig in figures.items():\n out = parent / f\"{base}_{name}.png\"\n fig.savefig(str(out), dpi=150, bbox_inches=\"tight\")\n self._add_notification(\n f\"Charts saved to {parent}/{base}_*.png\", \"info\"\n )\n\n # -------------------------------------------------------------------\n # Sidebar (parameters)\n # -------------------------------------------------------------------\n\n def _build_sidebar(self) -> None:\n sidebar = ctk.CTkScrollableFrame(\n self, fg_color=C[\"sidebar\"], width=220, corner_radius=0\n )\n sidebar.pack(side=\"left\", fill=\"y\", padx=0, pady=0)\n\n ctk.CTkLabel(\n sidebar,\n text=\"Parameters\",\n font=(\"Consolas\", 14, \"bold\"),\n text_color=C[\"text\"],\n ).pack(pady=(12, 8))\n\n def make_slider(\n parent: ctk.CTkFrame,\n label: str,\n from_: float,\n to: float,\n default: float,\n ) -> ctk.DoubleVar:\n ctk.CTkLabel(\n parent,\n text=label,\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n ).pack(pady=(8, 0), padx=8, anchor=\"w\")\n var = ctk.DoubleVar(value=default)\n val_lbl = ctk.CTkLabel(\n parent,\n text=f\"{default}\",\n font=(\"Consolas\", 10),\n text_color=C[\"text\"],\n )\n val_lbl.pack(padx=8, anchor=\"e\")\n\n def on_change(v: str) -> None:\n val_lbl.configure(text=f\"{float(v):.2f}\")\n\n slider = ctk.CTkSlider(\n parent,\n from_=from_,\n to=to,\n variable=var,\n width=180,\n command=on_change,\n )\n slider.pack(padx=8)\n return var\n\n self._target_var = make_slider(sidebar, \"Target Util %\", 20, 90, 60)\n self._alpha_var = make_slider(sidebar, \"EMA Alpha\", 0.1, 1.0, 0.7)\n self._safety_var = make_slider(\n sidebar, \"Safety Margin\", 0.0, 0.5, 0.15\n )\n self._up_thresh_var = make_slider(\n sidebar, \"Up Threshold \\u00d7\", 1.0, 1.5, 1.1\n )\n self._down_thresh_var = make_slider(\n sidebar, \"Down Threshold \\u00d7\", 0.3, 0.9, 0.7\n )\n\n # Replica range sliders\n ctk.CTkLabel(\n sidebar,\n text=\"\\u2500\\u2500\\u2500 Replica Range \\u2500\\u2500\\u2500\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n ).pack(pady=(12, 0), padx=8)\n self._min_rep_var = make_slider(\n sidebar, \"Min Replicas\", 1, 5, 2\n )\n self._max_rep_var = make_slider(\n sidebar, \"Max Replicas\", 4, 20, 8\n )\n\n ctk.CTkButton(\n sidebar,\n text=\"Apply & Re-run\",\n fg_color=C[\"accent\"],\n text_color=\"#fff\",\n hover_color=\"#c73550\",\n command=self._do_reset,\n ).pack(pady=16, padx=12, fill=\"x\")\n\n # Scenario selector\n ctk.CTkLabel(\n sidebar,\n text=\"Scenario\",\n font=(\"Consolas\", 14, \"bold\"),\n text_color=C[\"text\"],\n ).pack(pady=(16, 4))\n\n scenario_info = [\n (\"Stable\", \"Low-variance workload (~45-55%)\"),\n (\"Ramp\", \"30% -> 85% gradual increase\"),\n (\"Spike\", \"3 sudden load spikes\"),\n (\"Diurnal\", \"48h day/night cycle\"),\n ]\n self._scenario_btns: list[ctk.CTkButton] = []\n for i, (name, desc) in enumerate(scenario_info):\n btn = ctk.CTkButton(\n sidebar,\n text=name,\n width=180,\n height=28,\n fg_color=C[\"bg_card\"],\n hover_color=C[\"blue\"],\n command=lambda idx=i: self._select_scenario(idx),\n )\n btn.pack(pady=2, padx=12)\n Tooltip(btn, desc)\n self._scenario_btns.append(btn)\n\n # Scenario description\n self._scenario_desc = ctk.CTkLabel(\n sidebar,\n text=\"\",\n font=(\"Consolas\", 9),\n text_color=C[\"text_dim\"],\n wraplength=200,\n )\n self._scenario_desc.pack(pady=(8, 4), padx=8)\n\n # Keyboard shortcuts help\n ctk.CTkLabel(\n sidebar,\n text=\"\\u2500\\u2500\\u2500 Shortcuts \\u2500\\u2500\\u2500\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n ).pack(pady=(16, 4), padx=8)\n shortcuts = [\n \"Space: Play/Pause\",\n \"Right Arrow: Step\",\n \"R: Reset\",\n \"1-4: Select Scenario\",\n \"Ctrl+E: Export Data\",\n \"Ctrl+S: Save Charts\",\n ]\n for sc in shortcuts:\n ctk.CTkLabel(\n sidebar,\n text=sc,\n font=(\"Consolas\", 8),\n text_color=C[\"text_dim\"],\n ).pack(padx=12, anchor=\"w\")\n\n # -------------------------------------------------------------------\n # Tab view (4 tabs)\n # -------------------------------------------------------------------\n\n def _build_tabview(self) -> None:\n self._tabview = ctk.CTkTabview(self, fg_color=C[\"bg\"])\n self._tabview.pack(fill=\"both\", expand=True, padx=8, pady=(0, 4))\n\n self._tab_nn = self._tabview.add(\"Neural Network\")\n self._tab_algo = self._tabview.add(\"Scaling Algorithm\")\n self._tab_exp = self._tabview.add(\"Experiment\")\n self._tab_log = self._tabview.add(\"Activity Log\")\n\n self._build_nn_tab()\n self._build_algo_tab()\n self._build_exp_tab()\n self._build_log_tab()\n\n # Timeline at the bottom\n self._build_timeline()\n\n # --- Tab 1: Neural Network ---\n\n def _build_nn_tab(self) -> None:\n tab = self._tab_nn\n\n # Top: pipeline diagram\n pipe_frame = ctk.CTkFrame(\n tab, fg_color=C[\"bg_card\"], corner_radius=8, height=90\n )\n pipe_frame.pack(fill=\"x\", padx=8, pady=4)\n pipe_frame.pack_propagate(False)\n\n ctk.CTkLabel(\n pipe_frame,\n text=\"ONNX Inference Pipeline\",\n font=(\"Consolas\", 12, \"bold\"),\n text_color=C[\"text\"],\n ).pack(pady=(6, 4))\n\n blocks_frame = ctk.CTkFrame(pipe_frame, fg_color=\"transparent\")\n blocks_frame.pack()\n stages = [\n (\"Raw Metrics\", \"5 values\", C[\"green\"]),\n (\"\\u2192\", \"\", C[\"text_dim\"]),\n (\"Feature Eng.\", \"44 features\", C[\"purple\"]),\n (\"\\u2192\", \"\", C[\"text_dim\"]),\n (\"StandardScaler\", \"normalize\", C[\"orange\"]),\n (\"\\u2192\", \"\", C[\"text_dim\"]),\n (\"ONNX LSTM\", \"(1,24,44)\\u2192(1,1)\", C[\"blue\"]),\n (\"\\u2192\", \"\", C[\"text_dim\"]),\n (\"Inverse Scale\", \"\\u2192 CPU %\", C[\"green\"]),\n ]\n stage_tooltips = [\n \"5 raw metrics: cpu_usage, mem_util, net_in, net_out, disk_io\",\n \"\",\n \"Generates 44 features: time features, lags, rolling windows\",\n \"\",\n \"Normalizes features using saved StandardScaler (mean/std)\",\n \"\",\n \"2-layer LSTM, 65K params, 0.13ms latency via ONNX Runtime\",\n \"\",\n \"Converts normalized prediction back to CPU percentage\",\n ]\n for i, (title, sub, color) in enumerate(stages):\n if title == \"\\u2192\":\n ctk.CTkLabel(\n blocks_frame,\n text=\"\\u2192\",\n font=(\"Consolas\", 16, \"bold\"),\n text_color=C[\"text_dim\"],\n ).pack(side=\"left\", padx=2)\n else:\n f = ctk.CTkFrame(\n blocks_frame,\n fg_color=C[\"bg\"],\n corner_radius=6,\n border_width=1,\n border_color=color,\n )\n f.pack(side=\"left\", padx=4)\n ctk.CTkLabel(\n f,\n text=title,\n font=(\"Consolas\", 9, \"bold\"),\n text_color=color,\n ).pack(padx=6, pady=(2, 0))\n ctk.CTkLabel(\n f,\n text=sub,\n font=(\"Consolas\", 8),\n text_color=C[\"text_dim\"],\n ).pack(padx=6, pady=(0, 2))\n if stage_tooltips[i]:\n Tooltip(f, stage_tooltips[i])\n\n # Middle: prediction output + buffer + accuracy\n mid = ctk.CTkFrame(tab, fg_color=\"transparent\")\n mid.pack(fill=\"x\", padx=8, pady=4)\n\n # Buffer status\n buf_frame = ctk.CTkFrame(\n mid, fg_color=C[\"bg_card\"], corner_radius=8, width=220\n )\n buf_frame.pack(side=\"left\", padx=(0, 8), fill=\"y\")\n buf_frame.pack_propagate(False)\n ctk.CTkLabel(\n buf_frame,\n text=\"Buffer Status\",\n font=(\"Consolas\", 11, \"bold\"),\n text_color=C[\"text\"],\n ).pack(pady=(8, 4))\n self._buf_progress = ctk.CTkProgressBar(buf_frame, width=180)\n self._buf_progress.pack(pady=4, padx=12)\n self._buf_progress.set(0)\n self._buf_lbl = ctk.CTkLabel(\n buf_frame,\n text=\"0 / 24 NOT READY\",\n font=(\"Consolas\", 10),\n text_color=C[\"red\"],\n )\n self._buf_lbl.pack()\n Tooltip(\n buf_frame,\n \"LSTM requires 24 timesteps (2h of 5-min data) before predictions.\",\n )\n\n # Prediction big number\n pred_frame = ctk.CTkFrame(\n mid, fg_color=C[\"bg_card\"], corner_radius=8, width=180\n )\n pred_frame.pack(side=\"left\", padx=8, fill=\"y\")\n pred_frame.pack_propagate(False)\n ctk.CTkLabel(\n pred_frame,\n text=\"ML Prediction\",\n font=(\"Consolas\", 11, \"bold\"),\n text_color=C[\"text\"],\n ).pack(pady=(8, 2))\n self._pred_big = ctk.CTkLabel(\n pred_frame,\n text=\"\\u2014\",\n font=(\"Consolas\", 28, \"bold\"),\n text_color=C[\"blue\"],\n )\n self._pred_big.pack(pady=4)\n self._pred_actual = ctk.CTkLabel(\n pred_frame,\n text=\"Actual: \\u2014\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n )\n self._pred_actual.pack()\n\n # Prediction accuracy panel\n acc_frame = ctk.CTkFrame(\n mid, fg_color=C[\"bg_card\"], corner_radius=8, width=200\n )\n acc_frame.pack(side=\"left\", padx=8, fill=\"y\")\n acc_frame.pack_propagate(False)\n ctk.CTkLabel(\n acc_frame,\n text=\"Prediction Accuracy\",\n font=(\"Consolas\", 11, \"bold\"),\n text_color=C[\"text\"],\n ).pack(pady=(8, 4))\n self._acc_mae_lbl = ctk.CTkLabel(\n acc_frame,\n text=\"MAE: \\u2014\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n )\n self._acc_mae_lbl.pack(anchor=\"w\", padx=12)\n self._acc_rmse_lbl = ctk.CTkLabel(\n acc_frame,\n text=\"RMSE: \\u2014\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n )\n self._acc_rmse_lbl.pack(anchor=\"w\", padx=12)\n self._acc_err_lbl = ctk.CTkLabel(\n acc_frame,\n text=\"Error: \\u2014\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n )\n self._acc_err_lbl.pack(anchor=\"w\", padx=12)\n Tooltip(\n acc_frame,\n \"MAE: Mean Absolute Error\\n\"\n \"RMSE: Root Mean Squared Error\\n\"\n \"Error: Current |predicted - actual|\",\n )\n\n # Raw metrics panel\n raw_frame = ctk.CTkFrame(\n mid, fg_color=C[\"bg_card\"], corner_radius=8\n )\n raw_frame.pack(side=\"left\", fill=\"both\", expand=True, padx=8)\n ctk.CTkLabel(\n raw_frame,\n text=\"Current Metrics\",\n font=(\"Consolas\", 11, \"bold\"),\n text_color=C[\"text\"],\n ).pack(pady=(8, 4))\n self._raw_labels: dict[str, ctk.CTkLabel] = {}\n metrics_info = {\n \"cpu_usage\": \"CPU utilization (%)\",\n \"mem_util\": \"Memory utilization (%)\",\n \"net_in\": \"Network inbound (KB/s)\",\n \"net_out\": \"Network outbound (KB/s)\",\n \"disk_io\": \"Disk I/O utilization (%)\",\n }\n for metric, desc in metrics_info.items():\n row = ctk.CTkFrame(raw_frame, fg_color=\"transparent\")\n row.pack(fill=\"x\", padx=12)\n lbl = ctk.CTkLabel(\n row,\n text=f\"{metric}: \\u2014\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n )\n lbl.pack(side=\"left\")\n self._raw_labels[metric] = lbl\n # Mini progress bar for each metric\n bar = ctk.CTkProgressBar(row, width=60, height=6)\n bar.pack(side=\"right\", padx=4)\n bar.set(0)\n self._raw_labels[f\"{metric}_bar\"] = bar\n Tooltip(raw_frame, \"Simulated from CPU data. In production, \"\n \"these come from Prometheus.\")\n\n # Bottom: prediction history chart\n self._nn_fig, self._nn_ax = plt.subplots(\n 1, 1, figsize=(10, 3), dpi=80\n )\n self._nn_canvas = embed_figure(tab, self._nn_fig)\n\n # --- Tab 2: Scaling Algorithm ---\n\n def _build_algo_tab(self) -> None:\n tab = self._tab_algo\n\n # 9-step pipeline\n pipe = ctk.CTkFrame(tab, fg_color=\"transparent\")\n pipe.pack(fill=\"x\", padx=4, pady=4)\n\n step_names = [\n \"EMA\",\n \"Hysteresis\",\n \"Desired\",\n \"Clamp\",\n \"Rate Limit\",\n \"Cooldown\",\n \"Stabilize\",\n \"No Change?\",\n \"Commit\",\n ]\n self._algo_blocks: list[PipelineBlock] = []\n for i, name in enumerate(step_names):\n if i > 0:\n ctk.CTkLabel(\n pipe,\n text=\"\\u2192\",\n font=(\"Consolas\", 14, \"bold\"),\n text_color=C[\"text_dim\"],\n ).pack(side=\"left\", padx=1)\n block = PipelineBlock(pipe, i + 1, name)\n block.pack(side=\"left\", padx=2)\n self._algo_blocks.append(block)\n\n # State panel\n state_frame = ctk.CTkFrame(\n tab, fg_color=C[\"bg_card\"], corner_radius=8, height=40\n )\n state_frame.pack(fill=\"x\", padx=8, pady=4)\n self._state_lbl = ctk.CTkLabel(\n state_frame,\n text=(\n \"Cooldown UP: \\u2014 | Cooldown DOWN: \\u2014 | \"\n \"Consec. Below: \\u2014 | Direction: \\u2014\"\n ),\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n )\n self._state_lbl.pack(pady=6)\n\n # Charts: CPU + Replicas side by side\n charts = ctk.CTkFrame(tab, fg_color=\"transparent\")\n charts.pack(fill=\"both\", expand=True, padx=4, pady=4)\n\n left = ctk.CTkFrame(charts, fg_color=\"transparent\")\n left.pack(side=\"left\", fill=\"both\", expand=True)\n self._algo_cpu_fig, self._algo_cpu_ax = plt.subplots(\n 1, 1, figsize=(5.5, 3), dpi=80\n )\n self._algo_cpu_canvas = embed_figure(left, self._algo_cpu_fig)\n\n right = ctk.CTkFrame(charts, fg_color=\"transparent\")\n right.pack(side=\"left\", fill=\"both\", expand=True)\n self._algo_rep_fig, self._algo_rep_ax = plt.subplots(\n 1, 1, figsize=(5.5, 3), dpi=80\n )\n self._algo_rep_canvas = embed_figure(right, self._algo_rep_fig)\n\n # Decision log\n self._decision_log = ctk.CTkTextbox(\n tab,\n height=100,\n font=(\"Consolas\", 9),\n fg_color=C[\"bg\"],\n text_color=C[\"text_dim\"],\n )\n self._decision_log.pack(fill=\"x\", padx=8, pady=(0, 4))\n\n # --- Tab 3: Experiment Comparison ---\n\n def _build_exp_tab(self) -> None:\n tab = self._tab_exp\n\n # Main comparison chart\n self._exp_fig, self._exp_axes = plt.subplots(\n 3,\n 1,\n figsize=(10, 6),\n dpi=80,\n gridspec_kw={\"height_ratios\": [2, 1, 1]},\n )\n self._exp_canvas = embed_figure(tab, self._exp_fig)\n\n # KPI cards grid\n cards_frame = ctk.CTkFrame(tab, fg_color=\"transparent\")\n cards_frame.pack(fill=\"x\", padx=8, pady=4)\n\n kpi_info = [\n (\"Avg Replicas\", \"lower\", \"Average number of running replicas\"),\n (\"Replica-Min\", \"lower\", \"Total replica-minutes consumed\"),\n (\n \"SLA Violations\",\n \"lower\",\n \"Steps where CPU exceeded SLA threshold\",\n ),\n (\n \"Scaling Events\",\n \"lower\",\n \"Number of replica count changes\",\n ),\n (\n \"Over-Prov %\",\n \"lower\",\n \"Percentage of over-provisioned capacity\",\n ),\n (\n \"Under-Prov %\",\n \"lower\",\n \"Percentage of under-provisioned capacity\",\n ),\n (\n \"Reaction Time\",\n \"lower\",\n \"Avg steps to respond to demand spike\",\n ),\n (\n \"Cost Savings %\",\n \"higher\",\n \"ML cost savings vs HPA (negative = higher cost)\",\n ),\n ]\n self._kpi_cards: list[KPICard] = []\n self._kpi_better: list[str] = []\n for i, (name, better, tooltip) in enumerate(kpi_info):\n card = KPICard(cards_frame, name, tooltip=tooltip)\n card.grid(\n row=i // 4, column=i % 4, padx=4, pady=4, sticky=\"nsew\"\n )\n self._kpi_cards.append(card)\n self._kpi_better.append(better)\n for c in range(4):\n cards_frame.columnconfigure(c, weight=1)\n\n # --- Tab 4: Activity Log ---\n\n def _build_log_tab(self) -> None:\n tab = self._tab_log\n\n ctk.CTkLabel(\n tab,\n text=\"Activity & Notification Feed\",\n font=(\"Consolas\", 14, \"bold\"),\n text_color=C[\"text\"],\n ).pack(pady=(8, 4), padx=8, anchor=\"w\")\n\n ctk.CTkLabel(\n tab,\n text=\"Scaling events, alerts, and system notifications\",\n font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"],\n ).pack(padx=8, anchor=\"w\")\n\n self._notif_frame = ctk.CTkScrollableFrame(\n tab, fg_color=C[\"bg\"], corner_radius=8\n )\n self._notif_frame.pack(\n fill=\"both\", expand=True, padx=8, pady=8\n )\n\n # -------------------------------------------------------------------\n # Update all visuals\n # -------------------------------------------------------------------\n\n def _update_all(self) -> None:\n sc = self._scenarios[self._current_scenario_idx]\n total = len(sc.actual_cpu)\n self._step_lbl.configure(text=f\"Step: {self._step} / {total}\")\n\n # Time display (5 min per step)\n minutes = self._step * 5\n hours = minutes // 60\n mins = minutes % 60\n self._time_lbl.configure(text=f\"{hours:02d}:{mins:02d}\")\n\n # Timeline\n progress = self._step / total if total > 0 else 0\n self._timeline_bar.set(progress)\n self._timeline_pct.configure(text=f\"{progress * 100:.0f}%\")\n\n # Highlight active scenario button\n for i, btn in enumerate(self._scenario_btns):\n btn.configure(\n fg_color=C[\"blue\"]\n if i == self._current_scenario_idx\n else C[\"bg_card\"]\n )\n\n # Update scenario description\n self._scenario_desc.configure(text=sc.description)\n\n self._update_nn_tab()\n self._update_algo_tab()\n self._update_exp_tab()\n self._update_log_tab()\n\n def _update_nn_tab(self) -> None:\n n = self._step\n\n # Buffer status\n buf_fill = min(n, 24)\n self._buf_progress.set(buf_fill / 24)\n ready = n >= 24\n self._buf_lbl.configure(\n text=f\"{min(n, 24)} / 24 {'READY' if ready else 'FILLING...'}\",\n text_color=C[\"green\"] if ready else C[\"red\"],\n )\n\n # Current prediction\n if n > 0:\n pred = self._predicted_trace[-1]\n actual = self._actual_trace[-1]\n color = (\n C[\"green\"]\n if pred < 42\n else C[\"yellow\"]\n if pred < 66\n else C[\"orange\"]\n if pred < 85\n else C[\"red\"]\n )\n self._pred_big.configure(\n text=f\"{pred:.1f}%\", text_color=color\n )\n self._pred_actual.configure(text=f\"Actual: {actual:.1f}%\")\n\n # Raw metrics (simulated from CPU)\n metrics_vals = {\n \"cpu_usage\": (actual, 100),\n \"mem_util\": (actual * 0.6, 100),\n \"net_in\": (actual * 1.2, 120),\n \"net_out\": (actual * 0.8, 80),\n \"disk_io\": (actual * 0.3, 30),\n }\n for metric, (val, max_val) in metrics_vals.items():\n unit = \"%\" if \"usage\" in metric or \"util\" in metric or \"io\" in metric else \" KB/s\"\n self._raw_labels[metric].configure(\n text=f\"{metric}: {val:.1f}{unit}\"\n )\n bar_key = f\"{metric}_bar\"\n if bar_key in self._raw_labels:\n bar_val = min(val / max_val, 1.0) if max_val > 0 else 0\n self._raw_labels[bar_key].set(bar_val)\n\n # Prediction accuracy\n errors = np.abs(\n np.array(self._actual_trace)\n - np.array(self._predicted_trace)\n )\n mae = float(np.mean(errors))\n rmse = float(np.sqrt(np.mean(errors**2)))\n cur_err = abs(actual - pred)\n\n mae_color = (\n C[\"green\"]\n if mae < 5\n else C[\"yellow\"]\n if mae < 10\n else C[\"red\"]\n )\n self._acc_mae_lbl.configure(\n text=f\"MAE: {mae:.2f}%\", text_color=mae_color\n )\n self._acc_rmse_lbl.configure(\n text=f\"RMSE: {rmse:.2f}%\", text_color=mae_color\n )\n err_color = (\n C[\"green\"]\n if cur_err < 5\n else C[\"yellow\"]\n if cur_err < 10\n else C[\"red\"]\n )\n self._acc_err_lbl.configure(\n text=f\"Error: {cur_err:.2f}%\", text_color=err_color\n )\n\n # Prediction history chart\n self._nn_ax.clear()\n if n > 1:\n window = 80\n x = list(range(max(0, n - window), n))\n a_slice = self._actual_trace[max(0, n - window):]\n p_slice = self._predicted_trace[max(0, n - window):]\n self._nn_ax.plot(\n x, a_slice, color=C[\"green\"], lw=1.5, label=\"Actual CPU\"\n )\n self._nn_ax.plot(\n x,\n p_slice,\n color=C[\"purple\"],\n lw=1.5,\n ls=\"--\",\n label=\"Predicted\",\n )\n # Fill error band\n self._nn_ax.fill_between(\n x, a_slice, p_slice, alpha=0.12, color=C[\"purple\"]\n )\n self._nn_ax.axhline(\n 60, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.5\n )\n self._nn_ax.set_ylabel(\"CPU %\")\n self._nn_ax.set_ylim(0, 100)\n self._nn_ax.legend(loc=\"upper right\", fontsize=8)\n self._nn_ax.grid(True, alpha=0.2)\n self._nn_ax.set_title(\n f\"Prediction History (last 80 steps) | Step {n}\",\n fontsize=10,\n )\n self._nn_fig.tight_layout()\n self._nn_canvas.draw_idle()\n\n def _update_algo_tab(self) -> None:\n n = self._step\n if n == 0:\n for block in self._algo_blocks:\n block.update(\"\\u2014\", \"skipped\")\n return\n\n # Get current step details\n pred = self._predicted_trace[-1]\n smoothed = self._smoothed_trace[-1]\n ml_replicas = self._ml_replicas_trace[-1]\n prev_replicas = (\n self._ml_replicas_trace[-2] if n > 1 else 2\n )\n\n target = self._target_var.get()\n up_thresh = target * self._up_thresh_var.get()\n down_thresh = target * self._down_thresh_var.get()\n\n # Determine direction\n if smoothed > up_thresh:\n direction = \"UP\"\n elif smoothed < down_thresh:\n direction = \"DOWN\"\n else:\n direction = \"HOLD\"\n\n # Update blocks\n self._algo_blocks[0].update(\n f\"{pred:.1f}\\u2192{smoothed:.1f}%\", \"active\"\n )\n cmp_sym = (\n \">\"\n if direction == \"UP\"\n else \"<\"\n if direction == \"DOWN\"\n else \"in\"\n )\n self._algo_blocks[1].update(\n f\"{smoothed:.0f}% {cmp_sym} \"\n f\"[{down_thresh:.0f},{up_thresh:.0f}]\"\n f\"\\n\\u2192{direction}\",\n \"active\" if direction != \"HOLD\" else \"blocked\",\n )\n\n min_r = int(self._min_rep_var.get())\n max_r = int(self._max_rep_var.get())\n\n if direction == \"HOLD\":\n for i in range(2, 9):\n self._algo_blocks[i].update(\"\\u2014\", \"skipped\")\n else:\n raw_desired = math.ceil(prev_replicas * smoothed / target)\n if direction == \"UP\":\n raw_desired = math.ceil(\n raw_desired * (1 + self._safety_var.get())\n )\n self._algo_blocks[2].update(\n f\"{prev_replicas}\\u00d7{smoothed:.0f}\"\n f\"/{target:.0f}\\n={raw_desired}\",\n \"active\",\n )\n clamped = max(min_r, min(max_r, raw_desired))\n self._algo_blocks[3].update(\n f\"[{min_r},{max_r}]\\u2192{clamped}\", \"active\"\n )\n rate_lim = (\n min(clamped, prev_replicas + 2)\n if direction == \"UP\"\n else max(clamped, prev_replicas - 1)\n )\n self._algo_blocks[4].update(\n f\"\\u00b1limit\\u2192{rate_lim}\", \"active\"\n )\n self._algo_blocks[5].update(\"OFF \\u2713\", \"active\")\n self._algo_blocks[6].update(\n \"N/A\" if direction == \"UP\" else \"check\", \"active\"\n )\n\n executed = ml_replicas != prev_replicas\n eq_sym = \"\\u2260\" if executed else \"=\"\n self._algo_blocks[7].update(\n f\"{ml_replicas}{eq_sym}{prev_replicas}\",\n \"active\" if executed else \"blocked\",\n )\n if executed:\n self._algo_blocks[8].update(\n f\"{prev_replicas}\\u2192{ml_replicas}\", \"executed\"\n )\n else:\n self._algo_blocks[8].update(\"hold\", \"blocked\")\n\n # State panel\n sim = self._ml_sim\n dir_color = {\n \"UP\": C[\"green\"],\n \"DOWN\": C[\"orange\"],\n \"HOLD\": C[\"text_dim\"],\n }.get(direction, C[\"text_dim\"])\n self._state_lbl.configure(\n text=f\"Direction: {direction} | \"\n f\"Consec. Below: {sim._consecutive_below} | \"\n f\"Replicas: {ml_replicas} | \"\n f\"Smoothed: {smoothed:.1f}%\",\n text_color=dir_color,\n )\n\n # CPU chart\n self._algo_cpu_ax.clear()\n if n > 1:\n x = list(range(n))\n self._algo_cpu_ax.plot(\n x,\n self._smoothed_trace,\n color=C[\"blue\"],\n lw=1.5,\n label=\"Smoothed\",\n )\n self._algo_cpu_ax.plot(\n x,\n self._actual_trace,\n color=C[\"green\"],\n lw=1,\n alpha=0.5,\n label=\"Actual\",\n )\n self._algo_cpu_ax.axhline(\n up_thresh,\n color=C[\"red\"],\n ls=\"--\",\n lw=0.8,\n label=f\"Up {up_thresh:.0f}%\",\n )\n self._algo_cpu_ax.axhline(\n down_thresh,\n color=C[\"orange\"],\n ls=\"--\",\n lw=0.8,\n label=f\"Down {down_thresh:.0f}%\",\n )\n self._algo_cpu_ax.axhline(\n target, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.6\n )\n self._algo_cpu_ax.axhspan(\n down_thresh, up_thresh, alpha=0.08, color=C[\"yellow\"]\n )\n self._algo_cpu_ax.set_ylim(0, 100)\n self._algo_cpu_ax.set_ylabel(\"CPU %\")\n self._algo_cpu_ax.legend(loc=\"upper right\", fontsize=7)\n self._algo_cpu_ax.grid(True, alpha=0.2)\n self._algo_cpu_ax.set_title(\"CPU + Thresholds\", fontsize=10)\n self._algo_cpu_fig.tight_layout()\n self._algo_cpu_canvas.draw_idle()\n\n # Replica chart\n self._algo_rep_ax.clear()\n if n > 1:\n x = list(range(n))\n self._algo_rep_ax.step(\n x,\n self._ml_replicas_trace,\n color=C[\"blue\"],\n lw=2,\n where=\"post\",\n label=\"ML Replicas\",\n )\n self._algo_rep_ax.step(\n x,\n self._hpa_replicas_trace,\n color=C[\"red\"],\n lw=1.5,\n where=\"post\",\n alpha=0.6,\n label=\"HPA Replicas\",\n )\n self._algo_rep_ax.axhline(\n min_r,\n color=C[\"text_dim\"],\n ls=\":\",\n lw=0.7,\n alpha=0.5,\n )\n self._algo_rep_ax.axhline(\n max_r,\n color=C[\"text_dim\"],\n ls=\":\",\n lw=0.7,\n alpha=0.5,\n )\n self._algo_rep_ax.set_ylim(0, max_r + 2)\n self._algo_rep_ax.set_ylabel(\"Replicas\")\n self._algo_rep_ax.legend(loc=\"upper right\", fontsize=7)\n self._algo_rep_ax.grid(True, alpha=0.2)\n self._algo_rep_ax.set_title(\"Replica Count\", fontsize=10)\n self._algo_rep_fig.tight_layout()\n self._algo_rep_canvas.draw_idle()\n\n # Decision log\n self._decision_log.configure(state=\"normal\")\n self._decision_log.delete(\"1.0\", \"end\")\n for line in self._decisions_log[-20:]:\n self._decision_log.insert(\"end\", line + \"\\n\")\n self._decision_log.configure(state=\"disabled\")\n self._decision_log.see(\"end\")\n\n def _update_exp_tab(self) -> None:\n if not hasattr(self, \"_full_metrics\"):\n return\n metrics = self._full_metrics\n history = self._history\n\n ax1, ax2, ax3 = self._exp_axes\n ax1.clear()\n ax2.clear()\n ax3.clear()\n\n n = len(history)\n if n > 0:\n x = list(range(n))\n actual = [r.actual_cpu for r in history]\n hpa_r = [r.hpa_replicas for r in history]\n ml_r = [r.ml_replicas for r in history]\n\n # Chart 1: CPU + Replicas overlay\n ax1.fill_between(\n x, actual, alpha=0.15, color=C[\"text_dim\"]\n )\n ax1.plot(\n x,\n actual,\n color=C[\"text_dim\"],\n lw=0.8,\n alpha=0.5,\n label=\"Actual CPU\",\n )\n ax1.step(\n x,\n [r * 10 for r in hpa_r],\n color=C[\"red\"],\n lw=1.5,\n where=\"post\",\n label=\"HPA Replicas (\\u00d710)\",\n )\n ax1.step(\n x,\n [r * 10 for r in ml_r],\n color=C[\"blue\"],\n lw=2,\n where=\"post\",\n label=\"ML Replicas (\\u00d710)\",\n )\n target = self._target_var.get()\n ax1.axhline(\n target * 1.2,\n color=C[\"red\"],\n ls=\":\",\n lw=0.8,\n alpha=0.5,\n label=\"SLA Limit\",\n )\n ax1.set_ylabel(\"CPU % / Replicas\\u00d710\")\n ax1.set_ylim(0, 100)\n ax1.legend(loc=\"upper right\", fontsize=7, ncol=2)\n sc = self._scenarios[self._current_scenario_idx]\n ax1.set_title(\n f\"Experiment: {sc.name}\", fontsize=11\n )\n ax1.grid(True, alpha=0.2)\n\n # Chart 2: Replica comparison (no multiplier)\n ax2.step(\n x,\n hpa_r,\n color=C[\"red\"],\n lw=1.5,\n where=\"post\",\n label=\"HPA\",\n )\n ax2.step(\n x,\n ml_r,\n color=C[\"blue\"],\n lw=2,\n where=\"post\",\n label=\"ML\",\n )\n ax2.set_ylabel(\"Replicas\")\n ax2.legend(loc=\"upper right\", fontsize=7)\n ax2.grid(True, alpha=0.2)\n ax2.set_title(\"Replica Count Comparison\", fontsize=9)\n\n # Chart 3: Replica diff\n diff = [m - h for m, h in zip(ml_r, hpa_r)]\n colors_bar = [\n C[\"green\"]\n if d < 0\n else C[\"red\"]\n if d > 0\n else C[\"text_dim\"]\n for d in diff\n ]\n ax3.bar(x, diff, color=colors_bar, alpha=0.7, width=1.0)\n ax3.axhline(0, color=C[\"text_dim\"], lw=0.5)\n ax3.set_ylabel(\"ML - HPA\")\n ax3.set_xlabel(\"Step\")\n ax3.grid(True, alpha=0.2)\n ax3.set_title(\n \"Replica Difference (negative = ML uses fewer)\",\n fontsize=9,\n )\n\n self._exp_fig.tight_layout()\n self._exp_canvas.draw_idle()\n\n # KPI cards\n kpi_data = [\n (\n f\"{metrics.hpa_avg_replicas:.1f}\",\n f\"{metrics.ml_avg_replicas:.1f}\",\n ),\n (\n f\"{metrics.hpa_replica_minutes:.0f}\",\n f\"{metrics.ml_replica_minutes:.0f}\",\n ),\n (\n f\"{metrics.hpa_sla_violations}\",\n f\"{metrics.ml_sla_violations}\",\n ),\n (\n f\"{metrics.hpa_scaling_events}\",\n f\"{metrics.ml_scaling_events}\",\n ),\n (\n f\"{metrics.hpa_over_provisioning_pct:.1f}%\",\n f\"{metrics.ml_over_provisioning_pct:.1f}%\",\n ),\n (\n f\"{metrics.hpa_under_provisioning_pct:.1f}%\",\n f\"{metrics.ml_under_provisioning_pct:.1f}%\",\n ),\n (\n f\"{metrics.hpa_avg_reaction_time:.1f}\",\n f\"{metrics.ml_avg_reaction_time:.1f}\",\n ),\n (\n \"\\u2014\",\n f\"{metrics.cost_savings_pct:+.1f}%\",\n ),\n ]\n for i, (hpa_val, ml_val) in enumerate(kpi_data):\n self._kpi_cards[i].set_values(\n hpa_val, ml_val, better=self._kpi_better[i]\n )\n\n def _update_log_tab(self) -> None:\n \"\"\"Update the activity log / notification feed tab.\"\"\"\n # Clear and rebuild (only last 30 for performance)\n for widget in self._notif_frame.winfo_children():\n widget.destroy()\n for text, level in self._notifications[-30:]:\n NotificationItem(self._notif_frame, text, level).pack(\n fill=\"x\", padx=4, pady=1\n )\n\n\n# ---------------------------------------------------------------------------\n# Entry point\n# ---------------------------------------------------------------------------\n\n\ndef main() -> None:\n \"\"\"Launch the experiment dashboard application.\"\"\"\n app = ExperimentApp()\n app.mainloop()\n\n\nif __name__ == \"__main__\":\n main()\n", | |
| "structuredPatch": [ | |
| { | |
| "oldStart": 5, | |
| "oldLines": 38, | |
| "newStart": 5, | |
| "newLines": 50, | |
| "lines": [ | |
| " - Scaling algorithm 9-step pipeline", | |
| " - HPA vs ML autoscaler comparison", | |
| " ", | |
| "+Features:", | |
| "+ - Keyboard shortcuts (Space=play/pause, Right=step, R=reset, 1-4=scenario)", | |
| "+ - Tooltips on pipeline blocks and KPI cards", | |
| "+ - Live prediction accuracy metrics (MAE, RMSE)", | |
| "+ - Animated status indicators", | |
| "+ - Scenario description panel", | |
| "+ - Timeline progress bar", | |
| "+ - Export charts / data to file", | |
| "+ - Notification feed for scaling events", | |
| "+ - Configurable min/max replica range", | |
| "+", | |
| " Usage:", | |
| " python scripts/experiment_gui.py", | |
| " \"\"\"", | |
| " ", | |
| " from __future__ import annotations", | |
| " ", | |
| "+import csv", | |
| " import math", | |
| " import sys", | |
| "+import time", | |
| " from pathlib import Path", | |
| "+from tkinter import filedialog", | |
| " ", | |
| " import customtkinter as ctk", | |
| " import matplotlib", | |
| " import numpy as np", | |
| " ", | |
| " matplotlib.use(\"TkAgg\")", | |
| "-import matplotlib.pyplot as plt", | |
| "-from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg", | |
| "-from matplotlib.figure import Figure", | |
| "+import matplotlib.pyplot as plt # noqa: E402", | |
| "+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg # noqa: E402", | |
| "+from matplotlib.figure import Figure # noqa: E402", | |
| " ", | |
| " # Add project root to path for imports", | |
| " ROOT = Path(__file__).resolve().parent.parent", | |
| " sys.path.insert(0, str(ROOT / \"src\"))", | |
| " sys.path.insert(0, str(ROOT))", | |
| " ", | |
| "-from scripts.experiment_scenarios import get_all_scenarios, Scenario", | |
| "-from scripts.experiment_simulator import (", | |
| "+from scripts.experiment_scenarios import Scenario, get_all_scenarios # noqa: E402", | |
| "+from scripts.experiment_simulator import ( # noqa: E402", | |
| " ExperimentRunner,", | |
| "- HPAConfig,", | |
| " MLAutoscalerSimulator,", | |
| " MLConfig,", | |
| " StepRecord,", | |
| "- compute_metrics,", | |
| " )", | |
| " ", | |
| " # ---------------------------------------------------------------------------" | |
| ] | |
| }, | |
| { | |
| "oldStart": 44, | |
| "oldLines": 36, | |
| "newStart": 56, | |
| "newLines": 42, | |
| "lines": [ | |
| " # ---------------------------------------------------------------------------", | |
| " ", | |
| " C = {", | |
| "- \"bg\": \"#1a1a2e\",", | |
| "- \"bg_card\": \"#16213e\",", | |
| "- \"sidebar\": \"#0f3460\",", | |
| "- \"accent\": \"#e94560\",", | |
| "- \"green\": \"#73BF69\",", | |
| "- \"yellow\": \"#FADE2A\",", | |
| "- \"orange\": \"#FF9830\",", | |
| "- \"red\": \"#F2495C\",", | |
| "- \"blue\": \"#5794F2\",", | |
| "- \"purple\": \"#B877D9\",", | |
| "- \"text\": \"#e0e0e0\",", | |
| "- \"text_dim\": \"#8892b0\",", | |
| "- \"grid\": \"#2a2a4a\",", | |
| "+ \"bg\": \"#1a1a2e\",", | |
| "+ \"bg_card\": \"#16213e\",", | |
| "+ \"sidebar\": \"#0f3460\",", | |
| "+ \"accent\": \"#e94560\",", | |
| "+ \"green\": \"#73BF69\",", | |
| "+ \"yellow\": \"#FADE2A\",", | |
| "+ \"orange\": \"#FF9830\",", | |
| "+ \"red\": \"#F2495C\",", | |
| "+ \"blue\": \"#5794F2\",", | |
| "+ \"purple\": \"#B877D9\",", | |
| "+ \"cyan\": \"#8AB8FF\",", | |
| "+ \"text\": \"#e0e0e0\",", | |
| "+ \"text_dim\": \"#8892b0\",", | |
| "+ \"grid\": \"#2a2a4a\",", | |
| "+ \"success\": \"#73BF69\",", | |
| "+ \"warning\": \"#FADE2A\",", | |
| "+ \"danger\": \"#F2495C\",", | |
| " }", | |
| " ", | |
| " # Matplotlib dark style", | |
| "-plt.rcParams.update({", | |
| "- \"figure.facecolor\": C[\"bg_card\"],", | |
| "- \"axes.facecolor\": C[\"bg\"],", | |
| "- \"axes.edgecolor\": C[\"grid\"],", | |
| "- \"axes.labelcolor\": C[\"text\"],", | |
| "- \"text.color\": C[\"text\"],", | |
| "- \"xtick.color\": C[\"text_dim\"],", | |
| "- \"ytick.color\": C[\"text_dim\"],", | |
| "- \"grid.color\": C[\"grid\"],", | |
| "- \"grid.alpha\": 0.3,", | |
| "- \"font.size\": 9,", | |
| "- \"legend.facecolor\": C[\"bg_card\"],", | |
| "- \"legend.edgecolor\": C[\"grid\"],", | |
| "-})", | |
| "+plt.rcParams.update(", | |
| "+ {", | |
| "+ \"figure.facecolor\": C[\"bg_card\"],", | |
| "+ \"axes.facecolor\": C[\"bg\"],", | |
| "+ \"axes.edgecolor\": C[\"grid\"],", | |
| "+ \"axes.labelcolor\": C[\"text\"],", | |
| "+ \"text.color\": C[\"text\"],", | |
| "+ \"xtick.color\": C[\"text_dim\"],", | |
| "+ \"ytick.color\": C[\"text_dim\"],", | |
| "+ \"grid.color\": C[\"grid\"],", | |
| "+ \"grid.alpha\": 0.3,", | |
| "+ \"font.size\": 9,", | |
| "+ \"legend.facecolor\": C[\"bg_card\"],", | |
| "+ \"legend.edgecolor\": C[\"grid\"],", | |
| "+ }", | |
| "+)", | |
| " ", | |
| " ", | |
| " # ---------------------------------------------------------------------------" | |
| ] | |
| }, | |
| { | |
| "oldStart": 82, | |
| "oldLines": 6, | |
| "newStart": 100, | |
| "newLines": 7, | |
| "lines": [ | |
| " ", | |
| " ", | |
| " def embed_figure(parent: ctk.CTkFrame, fig: Figure) -> FigureCanvasTkAgg:", | |
| "+ \"\"\"Embed a matplotlib Figure into a CustomTkinter frame.\"\"\"", | |
| " canvas = FigureCanvasTkAgg(fig, master=parent)", | |
| " canvas.draw()", | |
| " canvas.get_tk_widget().pack(fill=\"both\", expand=True)" | |
| ] | |
| }, | |
| { | |
| "oldStart": 89, | |
| "oldLines": 14, | |
| "newStart": 108, | |
| "newLines": 56, | |
| "lines": [ | |
| " ", | |
| " ", | |
| " # ---------------------------------------------------------------------------", | |
| "-# KPI Card widget", | |
| "+# Tooltip widget", | |
| " # ---------------------------------------------------------------------------", | |
| " ", | |
| " ", | |
| "+class Tooltip:", | |
| "+ \"\"\"Hover tooltip for any widget.\"\"\"", | |
| "+", | |
| "+ def __init__(self, widget: ctk.CTkBaseClass, text: str) -> None:", | |
| "+ self._widget = widget", | |
| "+ self._text = text", | |
| "+ self._tip_window: ctk.CTkToplevel | None = None", | |
| "+ widget.bind(\"<Enter>\", self._show)", | |
| "+ widget.bind(\"<Leave>\", self._hide)", | |
| "+", | |
| "+ def _show(self, event: object = None) -> None:", | |
| "+ if self._tip_window:", | |
| "+ return", | |
| "+ x = self._widget.winfo_rootx() + 20", | |
| "+ y = self._widget.winfo_rooty() + self._widget.winfo_height() + 4", | |
| "+ tw = ctk.CTkToplevel(self._widget)", | |
| "+ tw.wm_overrideredirect(True)", | |
| "+ tw.wm_geometry(f\"+{x}+{y}\")", | |
| "+ tw.attributes(\"-topmost\", True)", | |
| "+ lbl = ctk.CTkLabel(", | |
| "+ tw,", | |
| "+ text=self._text,", | |
| "+ font=(\"Consolas\", 10),", | |
| "+ fg_color=C[\"bg_card\"],", | |
| "+ text_color=C[\"text\"],", | |
| "+ corner_radius=6,", | |
| "+ wraplength=280,", | |
| "+ )", | |
| "+ lbl.pack(padx=6, pady=4)", | |
| "+ self._tip_window = tw", | |
| "+", | |
| "+ def _hide(self, event: object = None) -> None:", | |
| "+ if self._tip_window:", | |
| "+ self._tip_window.destroy()", | |
| "+ self._tip_window = None", | |
| "+", | |
| "+", | |
| "+# ---------------------------------------------------------------------------", | |
| "+# KPI Card widget (enhanced with delta indicator)", | |
| "+# ---------------------------------------------------------------------------", | |
| "+", | |
| "+", | |
| " class KPICard(ctk.CTkFrame):", | |
| "- \"\"\"Compact KPI card: title + HPA value + ML value.\"\"\"", | |
| "+ \"\"\"Compact KPI card: title + HPA value + ML value + delta arrow.\"\"\"", | |
| " ", | |
| "- def __init__(self, master, title: str, **kw):", | |
| "+ def __init__(self, master: ctk.CTkFrame, title: str, tooltip: str = \"\", **kw: object):", | |
| " super().__init__(master, fg_color=C[\"bg_card\"], corner_radius=8, **kw)", | |
| " self._title_lbl = ctk.CTkLabel(", | |
| " self, text=title, font=(\"Consolas\", 11), text_color=C[\"text_dim\"]" | |
| ] | |
| }, | |
| { | |
| "oldStart": 104, | |
| "oldLines": 7, | |
| "newStart": 165, | |
| "newLines": 7, | |
| "lines": [ | |
| " self._title_lbl.pack(pady=(6, 2))", | |
| " ", | |
| " row = ctk.CTkFrame(self, fg_color=\"transparent\")", | |
| "- row.pack(pady=(0, 6))", | |
| "+ row.pack(pady=(0, 2))", | |
| " self._hpa_lbl = ctk.CTkLabel(", | |
| " row, text=\"HPA: --\", font=(\"Consolas\", 13, \"bold\"), text_color=C[\"red\"]", | |
| " )" | |
| ] | |
| }, | |
| { | |
| "oldStart": 114, | |
| "oldLines": 46, | |
| "newStart": 175, | |
| "newLines": 127, | |
| "lines": [ | |
| " )", | |
| " self._ml_lbl.pack(side=\"left\", padx=6)", | |
| " ", | |
| "- def set_values(self, hpa_val: str, ml_val: str):", | |
| "+ # Delta indicator", | |
| "+ self._delta_lbl = ctk.CTkLabel(", | |
| "+ self, text=\"\", font=(\"Consolas\", 9), text_color=C[\"text_dim\"]", | |
| "+ )", | |
| "+ self._delta_lbl.pack(pady=(0, 4))", | |
| "+", | |
| "+ if tooltip:", | |
| "+ Tooltip(self, tooltip)", | |
| "+", | |
| "+ def set_values(", | |
| "+ self,", | |
| "+ hpa_val: str,", | |
| "+ ml_val: str,", | |
| "+ better: str = \"lower\",", | |
| "+ ) -> None:", | |
| "+ \"\"\"Update card values with automatic delta comparison.", | |
| "+", | |
| "+ Args:", | |
| "+ hpa_val: HPA metric value string.", | |
| "+ ml_val: ML metric value string.", | |
| "+ better: Direction that is \"better\" — \"lower\" or \"higher\".", | |
| "+ \"\"\"", | |
| " self._hpa_lbl.configure(text=f\"HPA: {hpa_val}\")", | |
| " self._ml_lbl.configure(text=f\"ML: {ml_val}\")", | |
| " ", | |
| "+ # Parse numeric values for delta", | |
| "+ try:", | |
| "+ hpa_num = float(hpa_val.replace(\"%\", \"\").replace(\"+\", \"\").replace(\",\", \"\"))", | |
| "+ ml_num = float(ml_val.replace(\"%\", \"\").replace(\"+\", \"\").replace(\",\", \"\"))", | |
| "+ diff = ml_num - hpa_num", | |
| "+ if abs(diff) < 0.01:", | |
| "+ self._delta_lbl.configure(text=\"= equal\", text_color=C[\"text_dim\"])", | |
| "+ else:", | |
| "+ is_better = (diff < 0) if better == \"lower\" else (diff > 0)", | |
| "+ arrow = \"v\" if diff < 0 else \"^\"", | |
| "+ color = C[\"green\"] if is_better else C[\"red\"]", | |
| "+ self._delta_lbl.configure(", | |
| "+ text=f\"{arrow} {abs(diff):.1f}\", text_color=color", | |
| "+ )", | |
| "+ except (ValueError, AttributeError):", | |
| "+ self._delta_lbl.configure(text=\"\", text_color=C[\"text_dim\"])", | |
| " ", | |
| "+", | |
| " # ---------------------------------------------------------------------------", | |
| "-# Pipeline Step block", | |
| "+# Pipeline Step block (enhanced with tooltip)", | |
| " # ---------------------------------------------------------------------------", | |
| " ", | |
| " ", | |
| " class PipelineBlock(ctk.CTkFrame):", | |
| " \"\"\"One step in the 9-step scaling algorithm visualization.\"\"\"", | |
| " ", | |
| "- def __init__(self, master, step_num: int, title: str, **kw):", | |
| "- super().__init__(master, fg_color=C[\"bg_card\"], corner_radius=6,", | |
| "- border_width=1, border_color=C[\"grid\"], **kw)", | |
| "+ # Descriptions for each pipeline step", | |
| "+ STEP_TOOLTIPS = {", | |
| "+ 1: \"EMA: Exponential moving average smoothing. \"", | |
| "+ \"Smoothes predicted CPU to remove noise.\",", | |
| "+ 2: \"Hysteresis: Dead-zone check. \"", | |
| "+ \"Determines UP/DOWN/HOLD based on thresholds.\",", | |
| "+ 3: \"Desired: Calculates target replica count \"", | |
| "+ \"using ceil(current * smoothed / target).\",", | |
| "+ 4: \"Clamp: Restricts replicas to [min, max] range.\",", | |
| "+ 5: \"Rate Limit: Limits change speed. \"", | |
| "+ \"Max +2 up, -1 down per step.\",", | |
| "+ 6: \"Cooldown: Prevents rapid re-scaling. \"", | |
| "+ \"60s for up, 300s for down.\",", | |
| "+ 7: \"Stabilize: Requires 3 consecutive low \"", | |
| "+ \"readings before scaling down.\",", | |
| "+ 8: \"No Change: Skips execution if desired \"", | |
| "+ \"equals current replicas.\",", | |
| "+ 9: \"Commit: Applies the scaling decision \"", | |
| "+ \"and updates cooldown timers.\",", | |
| "+ }", | |
| "+", | |
| "+ def __init__(self, master: ctk.CTkFrame, step_num: int, title: str, **kw: object):", | |
| "+ super().__init__(", | |
| "+ master,", | |
| "+ fg_color=C[\"bg_card\"],", | |
| "+ corner_radius=6,", | |
| "+ border_width=1,", | |
| "+ border_color=C[\"grid\"],", | |
| "+ **kw,", | |
| "+ )", | |
| " self.configure(width=115, height=80)", | |
| " self.pack_propagate(False)", | |
| "+ self._step_num = step_num", | |
| " ctk.CTkLabel(", | |
| "- self, text=f\"Step {step_num}\", font=(\"Consolas\", 8),", | |
| "- text_color=C[\"text_dim\"]", | |
| "+ self,", | |
| "+ text=f\"Step {step_num}\",", | |
| "+ font=(\"Consolas\", 8),", | |
| "+ text_color=C[\"text_dim\"],", | |
| " ).pack(pady=(4, 0))", | |
| " ctk.CTkLabel(", | |
| "- self, text=title, font=(\"Consolas\", 9, \"bold\"),", | |
| "- text_color=C[\"text\"]", | |
| "+ self,", | |
| "+ text=title,", | |
| "+ font=(\"Consolas\", 9, \"bold\"),", | |
| "+ text_color=C[\"text\"],", | |
| " ).pack()", | |
| " self._value_lbl = ctk.CTkLabel(", | |
| "- self, text=\"—\", font=(\"Consolas\", 10), text_color=C[\"text_dim\"],", | |
| "- wraplength=100", | |
| "+ self,", | |
| "+ text=\"\\u2014\",", | |
| "+ font=(\"Consolas\", 10),", | |
| "+ text_color=C[\"text_dim\"],", | |
| "+ wraplength=100,", | |
| " )", | |
| " self._value_lbl.pack(pady=(2, 4))", | |
| " ", | |
| "- def update(self, text: str, status: str = \"active\"):", | |
| "+ # Add tooltip", | |
| "+ tooltip_text = self.STEP_TOOLTIPS.get(step_num, \"\")", | |
| "+ if tooltip_text:", | |
| "+ Tooltip(self, tooltip_text)", | |
| "+", | |
| "+ def update(self, text: str, status: str = \"active\") -> None:", | |
| "+ \"\"\"Update block display text and status color.\"\"\"", | |
| " colors = {", | |
| " \"active\": C[\"green\"],", | |
| " \"blocked\": C[\"yellow\"],", | |
| " \"skipped\": C[\"text_dim\"],", | |
| " \"executed\": C[\"accent\"],", | |
| " }", | |
| "- self._value_lbl.configure(text=text, text_color=colors.get(status, C[\"text\"]))", | |
| "+ self._value_lbl.configure(", | |
| "+ text=text, text_color=colors.get(status, C[\"text\"])", | |
| "+ )", | |
| " border = {", | |
| " \"active\": C[\"green\"],", | |
| " \"blocked\": C[\"yellow\"]," | |
| ] | |
| }, | |
| { | |
| "oldStart": 163, | |
| "oldLines": 15, | |
| "newStart": 305, | |
| "newLines": 96, | |
| "lines": [ | |
| " ", | |
| " ", | |
| " # ---------------------------------------------------------------------------", | |
| "+# Status Indicator (animated dot)", | |
| "+# ---------------------------------------------------------------------------", | |
| "+", | |
| "+", | |
| "+class StatusDot(ctk.CTkFrame):", | |
| "+ \"\"\"Animated status dot indicator.\"\"\"", | |
| "+", | |
| "+ def __init__(self, master: ctk.CTkFrame, **kw: object):", | |
| "+ super().__init__(master, fg_color=\"transparent\", **kw)", | |
| "+ self._dot = ctk.CTkLabel(", | |
| "+ self, text=\"\\u25cf\", font=(\"Consolas\", 14), text_color=C[\"text_dim\"]", | |
| "+ )", | |
| "+ self._dot.pack(side=\"left\", padx=2)", | |
| "+ self._label = ctk.CTkLabel(", | |
| "+ self, text=\"IDLE\", font=(\"Consolas\", 10), text_color=C[\"text_dim\"]", | |
| "+ )", | |
| "+ self._label.pack(side=\"left\", padx=2)", | |
| "+ self._pulse_on = True", | |
| "+", | |
| "+ def set_status(self, status: str, text: str) -> None:", | |
| "+ \"\"\"Update status indicator.", | |
| "+", | |
| "+ Args:", | |
| "+ status: One of 'idle', 'running', 'ready', 'warning'.", | |
| "+ text: Label text to display.", | |
| "+ \"\"\"", | |
| "+ color_map = {", | |
| "+ \"idle\": C[\"text_dim\"],", | |
| "+ \"running\": C[\"green\"],", | |
| "+ \"ready\": C[\"blue\"],", | |
| "+ \"warning\": C[\"yellow\"],", | |
| "+ \"error\": C[\"red\"],", | |
| "+ }", | |
| "+ color = color_map.get(status, C[\"text_dim\"])", | |
| "+ self._dot.configure(text_color=color)", | |
| "+ self._label.configure(text=text, text_color=color)", | |
| "+", | |
| "+", | |
| "+# ---------------------------------------------------------------------------", | |
| "+# Notification Item", | |
| "+# ---------------------------------------------------------------------------", | |
| "+", | |
| "+", | |
| "+class NotificationItem(ctk.CTkFrame):", | |
| "+ \"\"\"Single notification entry in the feed.\"\"\"", | |
| "+", | |
| "+ def __init__(", | |
| "+ self,", | |
| "+ master: ctk.CTkFrame,", | |
| "+ text: str,", | |
| "+ level: str = \"info\",", | |
| "+ **kw: object,", | |
| "+ ):", | |
| "+ super().__init__(", | |
| "+ master, fg_color=C[\"bg_card\"], corner_radius=4, height=24, **kw", | |
| "+ )", | |
| "+ self.pack_propagate(False)", | |
| "+ color = {", | |
| "+ \"info\": C[\"blue\"],", | |
| "+ \"up\": C[\"green\"],", | |
| "+ \"down\": C[\"orange\"],", | |
| "+ \"warning\": C[\"yellow\"],", | |
| "+ }.get(level, C[\"text_dim\"])", | |
| "+ icon = {", | |
| "+ \"info\": \"\\u25b6\",", | |
| "+ \"up\": \"\\u25b2\",", | |
| "+ \"down\": \"\\u25bc\",", | |
| "+ \"warning\": \"\\u26a0\",", | |
| "+ }.get(level, \"\\u25cf\")", | |
| "+ ctk.CTkLabel(", | |
| "+ self,", | |
| "+ text=f\" {icon} {text}\",", | |
| "+ font=(\"Consolas\", 9),", | |
| "+ text_color=color,", | |
| "+ anchor=\"w\",", | |
| "+ ).pack(fill=\"x\", padx=4, pady=2)", | |
| "+", | |
| "+", | |
| "+# ---------------------------------------------------------------------------", | |
| " # Main Application", | |
| " # ---------------------------------------------------------------------------", | |
| " ", | |
| " ", | |
| " class ExperimentApp(ctk.CTk):", | |
| "- def __init__(self):", | |
| "+ \"\"\"Main GUI application for the ML Autoscaler experiment dashboard.\"\"\"", | |
| "+", | |
| "+ def __init__(self) -> None:", | |
| " super().__init__()", | |
| "- self.title(\"ML Autoscaler — Experiment Dashboard\")", | |
| "- self.geometry(\"1440x920\")", | |
| "+ self.title(\"ML Autoscaler \\u2014 Experiment Dashboard\")", | |
| "+ self.geometry(\"1480x960\")", | |
| " ctk.set_appearance_mode(\"dark\")", | |
| " ", | |
| " # State" | |
| ] | |
| }, | |
| { | |
| "oldStart": 191, | |
| "oldLines": 27, | |
| "newStart": 414, | |
| "newLines": 57, | |
| "lines": [ | |
| " self._smoothed_trace: list[float] = []", | |
| " self._decisions_log: list[str] = []", | |
| " ", | |
| "+ # Notifications queue", | |
| "+ self._notifications: list[tuple[str, str]] = []", | |
| "+", | |
| " # Build UI", | |
| " self._build_controls()", | |
| " self._build_sidebar()", | |
| " self._build_tabview()", | |
| " ", | |
| "+ # Bind keyboard shortcuts", | |
| "+ self._bind_shortcuts()", | |
| "+", | |
| " # Load data", | |
| " self._load_scenarios()", | |
| " ", | |
| " # -------------------------------------------------------------------", | |
| "+ # Keyboard shortcuts", | |
| "+ # -------------------------------------------------------------------", | |
| "+", | |
| "+ def _bind_shortcuts(self) -> None:", | |
| "+ \"\"\"Bind keyboard shortcuts for common actions.\"\"\"", | |
| "+ self.bind(\"<space>\", lambda e: self._toggle_play())", | |
| "+ self.bind(\"<Right>\", lambda e: self._do_step())", | |
| "+ self.bind(\"<r>\", lambda e: self._do_reset())", | |
| "+ self.bind(\"<R>\", lambda e: self._do_reset())", | |
| "+ self.bind(\"<1>\", lambda e: self._select_scenario(0))", | |
| "+ self.bind(\"<2>\", lambda e: self._select_scenario(1))", | |
| "+ self.bind(\"<3>\", lambda e: self._select_scenario(2))", | |
| "+ self.bind(\"<4>\", lambda e: self._select_scenario(3))", | |
| "+ self.bind(", | |
| "+ \"<Control-e>\", lambda e: self._export_data()", | |
| "+ )", | |
| "+ self.bind(", | |
| "+ \"<Control-s>\", lambda e: self._export_charts()", | |
| "+ )", | |
| "+", | |
| "+ # -------------------------------------------------------------------", | |
| " # Data loading", | |
| " # -------------------------------------------------------------------", | |
| " ", | |
| "- def _load_scenarios(self):", | |
| "+ def _load_scenarios(self) -> None:", | |
| " try:", | |
| " self._scenarios = get_all_scenarios(", | |
| " ROOT / \"data\" / \"processed\" / \"test.csv\",", | |
| " ROOT / \"data\" / \"processed\" / \"scaler.pkl\",", | |
| " ROOT / \"data\" / \"processed\" / \"metadata.json\",", | |
| " )", | |
| "- except Exception as e:", | |
| "+ except Exception:", | |
| " self._scenarios = [self._synthetic_fallback()]", | |
| "+ # Ensure we have at least 4 scenario buttons", | |
| "+ while len(self._scenarios) < 4:", | |
| "+ self._scenarios.append(self._synthetic_fallback())", | |
| " self._select_scenario(0)", | |
| " ", | |
| " def _synthetic_fallback(self) -> Scenario:" | |
| ] | |
| }, | |
| { | |
| "oldStart": 225, | |
| "oldLines": 25, | |
| "newStart": 478, | |
| "newLines": 34, | |
| "lines": [ | |
| " predicted += rng.normal(0, 2, n)", | |
| " return Scenario(", | |
| " name=\"synthetic_demo\",", | |
| "- description=\"Synthetic sine wave demo\",", | |
| "+ description=\"Synthetic sine wave demo for testing\",", | |
| " actual_cpu=np.clip(actual, 10, 100),", | |
| " predicted_cpu=np.clip(predicted, 10, 100),", | |
| " duration_hours=n * 5 / 60,", | |
| " )", | |
| " ", | |
| "- def _select_scenario(self, idx: int):", | |
| "+ def _select_scenario(self, idx: int) -> None:", | |
| "+ if idx >= len(self._scenarios):", | |
| "+ return", | |
| " self._current_scenario_idx = idx", | |
| " self._reset_simulation()", | |
| " self._update_all()", | |
| " ", | |
| "- def _reset_simulation(self):", | |
| "+ def _do_reset(self) -> None:", | |
| "+ \"\"\"Reset simulation (callable from shortcuts).\"\"\"", | |
| "+ self._reset_simulation()", | |
| "+ self._update_all()", | |
| "+", | |
| "+ def _reset_simulation(self) -> None:", | |
| " self._step = 0", | |
| " self._playing = False", | |
| "+ self._notifications = []", | |
| " sc = self._scenarios[self._current_scenario_idx]", | |
| " ", | |
| " # Run full experiment for Tab 3", | |
| " runner = ExperimentRunner(", | |
| "- sc.actual_cpu, sc.predicted_cpu,", | |
| "+ sc.actual_cpu,", | |
| "+ sc.predicted_cpu,", | |
| " ml_config=self._build_ml_config(),", | |
| " )", | |
| " self._history, self._full_metrics = runner.run()" | |
| ] | |
| }, | |
| { | |
| "oldStart": 257, | |
| "oldLines": 6, | |
| "newStart": 519, | |
| "newLines": 12, | |
| "lines": [ | |
| " self._smoothed_trace = []", | |
| " self._decisions_log = []", | |
| " ", | |
| "+ # Update play button state", | |
| "+ if hasattr(self, \"_play_btn\"):", | |
| "+ self._play_btn.configure(", | |
| "+ text=\"\\u25b6 Play\", fg_color=C[\"green\"]", | |
| "+ )", | |
| "+", | |
| " def _build_ml_config(self) -> MLConfig:", | |
| " return MLConfig(", | |
| " target_utilization=self._target_var.get()," | |
| ] | |
| }, | |
| { | |
| "oldStart": 264, | |
| "oldLines": 67, | |
| "newStart": 532, | |
| "newLines": 145, | |
| "lines": [ | |
| " safety_margin=self._safety_var.get(),", | |
| " scale_up_threshold_factor=self._up_thresh_var.get(),", | |
| " scale_down_threshold_factor=self._down_thresh_var.get(),", | |
| "+ min_replicas=int(self._min_rep_var.get()),", | |
| "+ max_replicas=int(self._max_rep_var.get()),", | |
| " )", | |
| " ", | |
| " # -------------------------------------------------------------------", | |
| " # Control bar (top)", | |
| " # -------------------------------------------------------------------", | |
| " ", | |
| "- def _build_controls(self):", | |
| "+ def _build_controls(self) -> None:", | |
| " bar = ctk.CTkFrame(self, fg_color=C[\"bg_card\"], height=50)", | |
| " bar.pack(fill=\"x\", padx=8, pady=(8, 4))", | |
| " ", | |
| " ctk.CTkLabel(", | |
| "- bar, text=\"ML Autoscaler — Experiment Dashboard\",", | |
| "- font=(\"Consolas\", 16, \"bold\"), text_color=C[\"text\"]", | |
| "+ bar,", | |
| "+ text=\"ML Autoscaler \\u2014 Experiment Dashboard\",", | |
| "+ font=(\"Consolas\", 16, \"bold\"),", | |
| "+ text_color=C[\"text\"],", | |
| " ).pack(side=\"left\", padx=12)", | |
| " ", | |
| "+ # Status dot", | |
| "+ self._status_dot = StatusDot(bar)", | |
| "+ self._status_dot.pack(side=\"left\", padx=8)", | |
| "+", | |
| " # Transport controls", | |
| " self._play_btn = ctk.CTkButton(", | |
| "- bar, text=\"▶ Play\", width=90, fg_color=C[\"green\"],", | |
| "- text_color=\"#000\", hover_color=\"#5ea554\",", | |
| "- command=self._toggle_play", | |
| "+ bar,", | |
| "+ text=\"\\u25b6 Play\",", | |
| "+ width=90,", | |
| "+ fg_color=C[\"green\"],", | |
| "+ text_color=\"#000\",", | |
| "+ hover_color=\"#5ea554\",", | |
| "+ command=self._toggle_play,", | |
| " )", | |
| " self._play_btn.pack(side=\"left\", padx=4)", | |
| " ", | |
| " ctk.CTkButton(", | |
| "- bar, text=\"⏭ Step\", width=90, fg_color=C[\"blue\"],", | |
| "- text_color=\"#000\", hover_color=\"#4678c9\",", | |
| "- command=self._do_step", | |
| "+ bar,", | |
| "+ text=\"\\u23ed Step\",", | |
| "+ width=90,", | |
| "+ fg_color=C[\"blue\"],", | |
| "+ text_color=\"#000\",", | |
| "+ hover_color=\"#4678c9\",", | |
| "+ command=self._do_step,", | |
| " ).pack(side=\"left\", padx=4)", | |
| " ", | |
| " ctk.CTkButton(", | |
| "- bar, text=\"↺ Reset\", width=90, fg_color=C[\"orange\"],", | |
| "- text_color=\"#000\", hover_color=\"#d97e24\",", | |
| "- command=lambda: (self._reset_simulation(), self._update_all())", | |
| "+ bar,", | |
| "+ text=\"\\u21ba Reset\",", | |
| "+ width=90,", | |
| "+ fg_color=C[\"orange\"],", | |
| "+ text_color=\"#000\",", | |
| "+ hover_color=\"#d97e24\",", | |
| "+ command=self._do_reset,", | |
| " ).pack(side=\"left\", padx=4)", | |
| " ", | |
| "- # Step counter", | |
| "+ # Export buttons", | |
| "+ ctk.CTkButton(", | |
| "+ bar,", | |
| "+ text=\"\\u2913 Export\",", | |
| "+ width=80,", | |
| "+ fg_color=C[\"purple\"],", | |
| "+ text_color=\"#fff\",", | |
| "+ hover_color=\"#9b5fbf\",", | |
| "+ command=self._export_data,", | |
| "+ ).pack(side=\"left\", padx=4)", | |
| "+", | |
| "+ # Step counter + time display", | |
| "+ self._time_lbl = ctk.CTkLabel(", | |
| "+ bar,", | |
| "+ text=\"00:00\",", | |
| "+ font=(\"Consolas\", 11),", | |
| "+ text_color=C[\"text_dim\"],", | |
| "+ )", | |
| "+ self._time_lbl.pack(side=\"right\", padx=4)", | |
| "+", | |
| " self._step_lbl = ctk.CTkLabel(", | |
| "- bar, text=\"Step: 0 / 0\", font=(\"Consolas\", 12),", | |
| "- text_color=C[\"text_dim\"]", | |
| "+ bar,", | |
| "+ text=\"Step: 0 / 0\",", | |
| "+ font=(\"Consolas\", 12),", | |
| "+ text_color=C[\"text_dim\"],", | |
| " )", | |
| "- self._step_lbl.pack(side=\"right\", padx=12)", | |
| "+ self._step_lbl.pack(side=\"right\", padx=8)", | |
| " ", | |
| " # Speed slider", | |
| "- ctk.CTkLabel(bar, text=\"Speed:\", text_color=C[\"text_dim\"]).pack(side=\"right\")", | |
| "+ ctk.CTkLabel(bar, text=\"Speed:\", text_color=C[\"text_dim\"]).pack(", | |
| "+ side=\"right\"", | |
| "+ )", | |
| " self._speed_slider = ctk.CTkSlider(", | |
| "- bar, from_=50, to=1000, width=120,", | |
| "- command=lambda v: setattr(self, \"_speed_ms\", int(v))", | |
| "+ bar,", | |
| "+ from_=50,", | |
| "+ to=1000,", | |
| "+ width=120,", | |
| "+ command=lambda v: setattr(self, \"_speed_ms\", int(v)),", | |
| " )", | |
| " self._speed_slider.set(200)", | |
| " self._speed_slider.pack(side=\"right\", padx=4)", | |
| " ", | |
| "- def _toggle_play(self):", | |
| "+ # Shortcuts hint", | |
| "+ Tooltip(", | |
| "+ self._play_btn,", | |
| "+ \"Space: Play/Pause | Right: Step | R: Reset | 1-4: Scenario | \"", | |
| "+ \"Ctrl+E: Export data | Ctrl+S: Save charts\",", | |
| "+ )", | |
| "+", | |
| "+ # -------------------------------------------------------------------", | |
| "+ # Timeline progress bar", | |
| "+ # -------------------------------------------------------------------", | |
| "+", | |
| "+ def _build_timeline(self) -> None:", | |
| "+ \"\"\"Build timeline progress bar below tabs.\"\"\"", | |
| "+ frame = ctk.CTkFrame(self, fg_color=C[\"bg_card\"], height=30)", | |
| "+ frame.pack(fill=\"x\", padx=8, pady=(0, 2))", | |
| "+ frame.pack_propagate(False)", | |
| "+", | |
| "+ self._timeline_bar = ctk.CTkProgressBar(", | |
| "+ frame, width=600, height=8, progress_color=C[\"blue\"]", | |
| "+ )", | |
| "+ self._timeline_bar.pack(side=\"left\", fill=\"x\", expand=True, padx=12, pady=10)", | |
| "+ self._timeline_bar.set(0)", | |
| "+", | |
| "+ self._timeline_pct = ctk.CTkLabel(", | |
| "+ frame, text=\"0%\", font=(\"Consolas\", 9), text_color=C[\"text_dim\"]", | |
| "+ )", | |
| "+ self._timeline_pct.pack(side=\"right\", padx=8)", | |
| "+", | |
| "+ def _toggle_play(self) -> None:", | |
| " self._playing = not self._playing", | |
| " self._play_btn.configure(", | |
| "- text=\"⏸ Pause\" if self._playing else \"▶ Play\",", | |
| "+ text=\"\\u23f8 Pause\" if self._playing else \"\\u25b6 Play\",", | |
| " fg_color=C[\"yellow\"] if self._playing else C[\"green\"],", | |
| " )", | |
| "+ self._status_dot.set_status(", | |
| "+ \"running\" if self._playing else \"idle\",", | |
| "+ \"RUNNING\" if self._playing else \"PAUSED\",", | |
| "+ )", | |
| " if self._playing:", | |
| " self._auto_step()", | |
| " ", | |
| "- def _auto_step(self):", | |
| "+ def _auto_step(self) -> None:", | |
| " if not self._playing:", | |
| " return", | |
| " sc = self._scenarios[self._current_scenario_idx]" | |
| ] | |
| }, | |
| { | |
| "oldStart": 333, | |
| "oldLines": 9, | |
| "newStart": 679, | |
| "newLines": 12, | |
| "lines": [ | |
| " self.after(self._speed_ms, self._auto_step)", | |
| " else:", | |
| " self._playing = False", | |
| "- self._play_btn.configure(text=\"▶ Play\", fg_color=C[\"green\"])", | |
| "+ self._play_btn.configure(", | |
| "+ text=\"\\u25b6 Play\", fg_color=C[\"green\"]", | |
| "+ )", | |
| "+ self._status_dot.set_status(\"ready\", \"FINISHED\")", | |
| " ", | |
| "- def _do_step(self):", | |
| "+ def _do_step(self) -> None:", | |
| " sc = self._scenarios[self._current_scenario_idx]", | |
| " if self._step >= len(sc.actual_cpu):", | |
| " return" | |
| ] | |
| }, | |
| { | |
| "oldStart": 349, | |
| "oldLines": 7, | |
| "newStart": 698, | |
| "newLines": 9, | |
| "lines": [ | |
| " smoothed = self._ml_sim._smoothed or predicted", | |
| " ", | |
| " # HPA (from pre-computed history)", | |
| "- hpa_replicas = self._history[t].hpa_replicas if t < len(self._history) else 2", | |
| "+ hpa_replicas = (", | |
| "+ self._history[t].hpa_replicas if t < len(self._history) else 2", | |
| "+ )", | |
| " ", | |
| " self._actual_trace.append(actual)", | |
| " self._predicted_trace.append(predicted)" | |
| ] | |
| }, | |
| { | |
| "oldStart": 357, | |
| "oldLines": 190, | |
| "newStart": 708, | |
| "newLines": 499, | |
| "lines": [ | |
| " self._ml_replicas_trace.append(ml_replicas)", | |
| " self._hpa_replicas_trace.append(hpa_replicas)", | |
| " self._decisions_log.append(", | |
| "- f\"[t={t:>3}] {ml_dir:>4} | ML:{ml_replicas} HPA:{hpa_replicas} | \"", | |
| "- f\"actual={actual:.1f}% pred={predicted:.1f}% smooth={smoothed:.1f}%\"", | |
| "+ f\"[t={t:>3}] {ml_dir:>4} | ML:{ml_replicas} \"", | |
| "+ f\"HPA:{hpa_replicas} | \"", | |
| "+ f\"actual={actual:.1f}% pred={predicted:.1f}% \"", | |
| "+ f\"smooth={smoothed:.1f}%\"", | |
| " )", | |
| " ", | |
| "+ # Generate notification for scaling events", | |
| "+ if ml_dir == \"up\":", | |
| "+ prev = self._ml_replicas_trace[-2] if len(self._ml_replicas_trace) > 1 else 2", | |
| "+ self._add_notification(", | |
| "+ f\"t={t}: ML SCALE UP {prev} -> {ml_replicas}\", \"up\"", | |
| "+ )", | |
| "+ elif ml_dir == \"down\":", | |
| "+ prev = self._ml_replicas_trace[-2] if len(self._ml_replicas_trace) > 1 else 2", | |
| "+ self._add_notification(", | |
| "+ f\"t={t}: ML SCALE DOWN {prev} -> {ml_replicas}\", \"down\"", | |
| "+ )", | |
| "+", | |
| "+ # Warn if CPU is high", | |
| "+ if actual > 85:", | |
| "+ self._add_notification(", | |
| "+ f\"t={t}: High CPU alert: {actual:.1f}%\", \"warning\"", | |
| "+ )", | |
| "+", | |
| " self._step += 1", | |
| " self._update_all()", | |
| " ", | |
| " # -------------------------------------------------------------------", | |
| "+ # Notifications", | |
| "+ # -------------------------------------------------------------------", | |
| "+", | |
| "+ def _add_notification(self, text: str, level: str = \"info\") -> None:", | |
| "+ \"\"\"Add a notification to the feed (max 50 kept).\"\"\"", | |
| "+ self._notifications.append((text, level))", | |
| "+ if len(self._notifications) > 50:", | |
| "+ self._notifications = self._notifications[-50:]", | |
| "+", | |
| "+ # -------------------------------------------------------------------", | |
| "+ # Export functionality", | |
| "+ # -------------------------------------------------------------------", | |
| "+", | |
| "+ def _export_data(self) -> None:", | |
| "+ \"\"\"Export simulation data to CSV file.\"\"\"", | |
| "+ if not self._actual_trace:", | |
| "+ return", | |
| "+ filepath = filedialog.asksaveasfilename(", | |
| "+ defaultextension=\".csv\",", | |
| "+ filetypes=[(\"CSV files\", \"*.csv\"), (\"All files\", \"*.*\")],", | |
| "+ title=\"Export simulation data\",", | |
| "+ )", | |
| "+ if not filepath:", | |
| "+ return", | |
| "+ with open(filepath, \"w\", newline=\"\") as f:", | |
| "+ writer = csv.writer(f)", | |
| "+ writer.writerow(", | |
| "+ [", | |
| "+ \"step\",", | |
| "+ \"actual_cpu\",", | |
| "+ \"predicted_cpu\",", | |
| "+ \"smoothed_cpu\",", | |
| "+ \"ml_replicas\",", | |
| "+ \"hpa_replicas\",", | |
| "+ ]", | |
| "+ )", | |
| "+ for i in range(len(self._actual_trace)):", | |
| "+ writer.writerow(", | |
| "+ [", | |
| "+ i,", | |
| "+ f\"{self._actual_trace[i]:.2f}\",", | |
| "+ f\"{self._predicted_trace[i]:.2f}\",", | |
| "+ f\"{self._smoothed_trace[i]:.2f}\",", | |
| "+ self._ml_replicas_trace[i],", | |
| "+ self._hpa_replicas_trace[i],", | |
| "+ ]", | |
| "+ )", | |
| "+ self._add_notification(f\"Data exported to {filepath}\", \"info\")", | |
| "+", | |
| "+ def _export_charts(self) -> None:", | |
| "+ \"\"\"Export current charts as PNG images.\"\"\"", | |
| "+ filepath = filedialog.asksaveasfilename(", | |
| "+ defaultextension=\".png\",", | |
| "+ filetypes=[(\"PNG images\", \"*.png\"), (\"All files\", \"*.*\")],", | |
| "+ title=\"Save charts\",", | |
| "+ )", | |
| "+ if not filepath:", | |
| "+ return", | |
| "+ # Save all figures", | |
| "+ base = Path(filepath).stem", | |
| "+ parent = Path(filepath).parent", | |
| "+ figures = {", | |
| "+ \"nn\": self._nn_fig,", | |
| "+ \"algo_cpu\": self._algo_cpu_fig,", | |
| "+ \"algo_rep\": self._algo_rep_fig,", | |
| "+ \"experiment\": self._exp_fig,", | |
| "+ }", | |
| "+ for name, fig in figures.items():", | |
| "+ out = parent / f\"{base}_{name}.png\"", | |
| "+ fig.savefig(str(out), dpi=150, bbox_inches=\"tight\")", | |
| "+ self._add_notification(", | |
| "+ f\"Charts saved to {parent}/{base}_*.png\", \"info\"", | |
| "+ )", | |
| "+", | |
| "+ # -------------------------------------------------------------------", | |
| " # Sidebar (parameters)", | |
| " # -------------------------------------------------------------------", | |
| " ", | |
| "- def _build_sidebar(self):", | |
| "- sidebar = ctk.CTkFrame(self, fg_color=C[\"sidebar\"], width=220, corner_radius=0)", | |
| "+ def _build_sidebar(self) -> None:", | |
| "+ sidebar = ctk.CTkScrollableFrame(", | |
| "+ self, fg_color=C[\"sidebar\"], width=220, corner_radius=0", | |
| "+ )", | |
| " sidebar.pack(side=\"left\", fill=\"y\", padx=0, pady=0)", | |
| "- sidebar.pack_propagate(False)", | |
| " ", | |
| " ctk.CTkLabel(", | |
| "- sidebar, text=\"Parameters\", font=(\"Consolas\", 14, \"bold\"),", | |
| "- text_color=C[\"text\"]", | |
| "+ sidebar,", | |
| "+ text=\"Parameters\",", | |
| "+ font=(\"Consolas\", 14, \"bold\"),", | |
| "+ text_color=C[\"text\"],", | |
| " ).pack(pady=(12, 8))", | |
| " ", | |
| "- def make_slider(parent, label, from_, to, default, resolution=None):", | |
| "+ def make_slider(", | |
| "+ parent: ctk.CTkFrame,", | |
| "+ label: str,", | |
| "+ from_: float,", | |
| "+ to: float,", | |
| "+ default: float,", | |
| "+ ) -> ctk.DoubleVar:", | |
| " ctk.CTkLabel(", | |
| "- parent, text=label, font=(\"Consolas\", 10), text_color=C[\"text_dim\"]", | |
| "+ parent,", | |
| "+ text=label,", | |
| "+ font=(\"Consolas\", 10),", | |
| "+ text_color=C[\"text_dim\"],", | |
| " ).pack(pady=(8, 0), padx=8, anchor=\"w\")", | |
| " var = ctk.DoubleVar(value=default)", | |
| " val_lbl = ctk.CTkLabel(", | |
| "- parent, text=f\"{default}\", font=(\"Consolas\", 10), text_color=C[\"text\"]", | |
| "+ parent,", | |
| "+ text=f\"{default}\",", | |
| "+ font=(\"Consolas\", 10),", | |
| "+ text_color=C[\"text\"],", | |
| " )", | |
| " val_lbl.pack(padx=8, anchor=\"e\")", | |
| " ", | |
| "- def on_change(v):", | |
| "+ def on_change(v: str) -> None:", | |
| " val_lbl.configure(text=f\"{float(v):.2f}\")", | |
| " ", | |
| " slider = ctk.CTkSlider(", | |
| "- parent, from_=from_, to=to, variable=var,", | |
| "- width=180, command=on_change", | |
| "+ parent,", | |
| "+ from_=from_,", | |
| "+ to=to,", | |
| "+ variable=var,", | |
| "+ width=180,", | |
| "+ command=on_change,", | |
| " )", | |
| " slider.pack(padx=8)", | |
| " return var", | |
| " ", | |
| " self._target_var = make_slider(sidebar, \"Target Util %\", 20, 90, 60)", | |
| " self._alpha_var = make_slider(sidebar, \"EMA Alpha\", 0.1, 1.0, 0.7)", | |
| "- self._safety_var = make_slider(sidebar, \"Safety Margin\", 0.0, 0.5, 0.15)", | |
| "- self._up_thresh_var = make_slider(sidebar, \"Up Threshold ×\", 1.0, 1.5, 1.1)", | |
| "- self._down_thresh_var = make_slider(sidebar, \"Down Threshold ×\", 0.3, 0.9, 0.7)", | |
| "+ self._safety_var = make_slider(", | |
| "+ sidebar, \"Safety Margin\", 0.0, 0.5, 0.15", | |
| "+ )", | |
| "+ self._up_thresh_var = make_slider(", | |
| "+ sidebar, \"Up Threshold \\u00d7\", 1.0, 1.5, 1.1", | |
| "+ )", | |
| "+ self._down_thresh_var = make_slider(", | |
| "+ sidebar, \"Down Threshold \\u00d7\", 0.3, 0.9, 0.7", | |
| "+ )", | |
| " ", | |
| "+ # Replica range sliders", | |
| "+ ctk.CTkLabel(", | |
| "+ sidebar,", | |
| "+ text=\"\\u2500\\u2500\\u2500 Replica Range \\u2500\\u2500\\u2500\",", | |
| "+ font=(\"Consolas\", 10),", | |
| "+ text_color=C[\"text_dim\"],", | |
| "+ ).pack(pady=(12, 0), padx=8)", | |
| "+ self._min_rep_var = make_slider(", | |
| "+ sidebar, \"Min Replicas\", 1, 5, 2", | |
| "+ )", | |
| "+ self._max_rep_var = make_slider(", | |
| "+ sidebar, \"Max Replicas\", 4, 20, 8", | |
| "+ )", | |
| "+", | |
| " ctk.CTkButton(", | |
| "- sidebar, text=\"Apply & Re-run\", fg_color=C[\"accent\"],", | |
| "- text_color=\"#fff\", hover_color=\"#c73550\",", | |
| "- command=lambda: (self._reset_simulation(), self._update_all())", | |
| "+ sidebar,", | |
| "+ text=\"Apply & Re-run\",", | |
| "+ fg_color=C[\"accent\"],", | |
| "+ text_color=\"#fff\",", | |
| "+ hover_color=\"#c73550\",", | |
| "+ command=self._do_reset,", | |
| " ).pack(pady=16, padx=12, fill=\"x\")", | |
| " ", | |
| " # Scenario selector", | |
| " ctk.CTkLabel(", | |
| "- sidebar, text=\"Scenario\", font=(\"Consolas\", 14, \"bold\"),", | |
| "- text_color=C[\"text\"]", | |
| "+ sidebar,", | |
| "+ text=\"Scenario\",", | |
| "+ font=(\"Consolas\", 14, \"bold\"),", | |
| "+ text_color=C[\"text\"],", | |
| " ).pack(pady=(16, 4))", | |
| " ", | |
| "+ scenario_info = [", | |
| "+ (\"Stable\", \"Low-variance workload (~45-55%)\"),", | |
| "+ (\"Ramp\", \"30% -> 85% gradual increase\"),", | |
| "+ (\"Spike\", \"3 sudden load spikes\"),", | |
| "+ (\"Diurnal\", \"48h day/night cycle\"),", | |
| "+ ]", | |
| " self._scenario_btns: list[ctk.CTkButton] = []", | |
| "- names = [\"Stable\", \"Ramp\", \"Spike\", \"Diurnal\"]", | |
| "- for i, name in enumerate(names):", | |
| "+ for i, (name, desc) in enumerate(scenario_info):", | |
| " btn = ctk.CTkButton(", | |
| "- sidebar, text=name, width=180, height=28,", | |
| "- fg_color=C[\"bg_card\"], hover_color=C[\"blue\"],", | |
| "- command=lambda idx=i: self._select_scenario(idx)", | |
| "+ sidebar,", | |
| "+ text=name,", | |
| "+ width=180,", | |
| "+ height=28,", | |
| "+ fg_color=C[\"bg_card\"],", | |
| "+ hover_color=C[\"blue\"],", | |
| "+ command=lambda idx=i: self._select_scenario(idx),", | |
| " )", | |
| " btn.pack(pady=2, padx=12)", | |
| "+ Tooltip(btn, desc)", | |
| " self._scenario_btns.append(btn)", | |
| " ", | |
| "+ # Scenario description", | |
| "+ self._scenario_desc = ctk.CTkLabel(", | |
| "+ sidebar,", | |
| "+ text=\"\",", | |
| "+ font=(\"Consolas\", 9),", | |
| "+ text_color=C[\"text_dim\"],", | |
| "+ wraplength=200,", | |
| "+ )", | |
| "+ self._scenario_desc.pack(pady=(8, 4), padx=8)", | |
| "+", | |
| "+ # Keyboard shortcuts help", | |
| "+ ctk.CTkLabel(", | |
| "+ sidebar,", | |
| "+ text=\"\\u2500\\u2500\\u2500 Shortcuts \\u2500\\u2500\\u2500\",", | |
| "+ font=(\"Consolas\", 10),", | |
| "+ text_color=C[\"text_dim\"],", | |
| "+ ).pack(pady=(16, 4), padx=8)", | |
| "+ shortcuts = [", | |
| "+ \"Space: Play/Pause\",", | |
| "+ \"Right Arrow: Step\",", | |
| "+ \"R: Reset\",", | |
| "+ \"1-4: Select Scenario\",", | |
| "+ \"Ctrl+E: Export Data\",", | |
| "+ \"Ctrl+S: Save Charts\",", | |
| "+ ]", | |
| "+ for sc in shortcuts:", | |
| "+ ctk.CTkLabel(", | |
| "+ sidebar,", | |
| "+ text=sc,", | |
| "+ font=(\"Consolas\", 8),", | |
| "+ text_color=C[\"text_dim\"],", | |
| "+ ).pack(padx=12, anchor=\"w\")", | |
| "+", | |
| " # -------------------------------------------------------------------", | |
| "- # Tab view (3 tabs)", | |
| "+ # Tab view (4 tabs)", | |
| " # -------------------------------------------------------------------", | |
| " ", | |
| "- def _build_tabview(self):", | |
| "+ def _build_tabview(self) -> None:", | |
| " self._tabview = ctk.CTkTabview(self, fg_color=C[\"bg\"])", | |
| "- self._tabview.pack(fill=\"both\", expand=True, padx=8, pady=(0, 8))", | |
| "+ self._tabview.pack(fill=\"both\", expand=True, padx=8, pady=(0, 4))", | |
| " ", | |
| " self._tab_nn = self._tabview.add(\"Neural Network\")", | |
| " self._tab_algo = self._tabview.add(\"Scaling Algorithm\")", | |
| " self._tab_exp = self._tabview.add(\"Experiment\")", | |
| "+ self._tab_log = self._tabview.add(\"Activity Log\")", | |
| " ", | |
| " self._build_nn_tab()", | |
| " self._build_algo_tab()", | |
| " self._build_exp_tab()", | |
| "+ self._build_log_tab()", | |
| " ", | |
| "+ # Timeline at the bottom", | |
| "+ self._build_timeline()", | |
| "+", | |
| " # --- Tab 1: Neural Network ---", | |
| " ", | |
| "- def _build_nn_tab(self):", | |
| "+ def _build_nn_tab(self) -> None:", | |
| " tab = self._tab_nn", | |
| " ", | |
| " # Top: pipeline diagram", | |
| "- pipe_frame = ctk.CTkFrame(tab, fg_color=C[\"bg_card\"], corner_radius=8, height=90)", | |
| "+ pipe_frame = ctk.CTkFrame(", | |
| "+ tab, fg_color=C[\"bg_card\"], corner_radius=8, height=90", | |
| "+ )", | |
| " pipe_frame.pack(fill=\"x\", padx=8, pady=4)", | |
| " pipe_frame.pack_propagate(False)", | |
| " ", | |
| " ctk.CTkLabel(", | |
| "- pipe_frame, text=\"ONNX Inference Pipeline\",", | |
| "- font=(\"Consolas\", 12, \"bold\"), text_color=C[\"text\"]", | |
| "+ pipe_frame,", | |
| "+ text=\"ONNX Inference Pipeline\",", | |
| "+ font=(\"Consolas\", 12, \"bold\"),", | |
| "+ text_color=C[\"text\"],", | |
| " ).pack(pady=(6, 4))", | |
| " ", | |
| " blocks_frame = ctk.CTkFrame(pipe_frame, fg_color=\"transparent\")", | |
| " blocks_frame.pack()", | |
| " stages = [", | |
| " (\"Raw Metrics\", \"5 values\", C[\"green\"]),", | |
| "- (\"→\", \"\", C[\"text_dim\"]),", | |
| "+ (\"\\u2192\", \"\", C[\"text_dim\"]),", | |
| " (\"Feature Eng.\", \"44 features\", C[\"purple\"]),", | |
| "- (\"→\", \"\", C[\"text_dim\"]),", | |
| "+ (\"\\u2192\", \"\", C[\"text_dim\"]),", | |
| " (\"StandardScaler\", \"normalize\", C[\"orange\"]),", | |
| "- (\"→\", \"\", C[\"text_dim\"]),", | |
| "- (\"ONNX LSTM\", \"(1,24,44)→(1,1)\", C[\"blue\"]),", | |
| "- (\"→\", \"\", C[\"text_dim\"]),", | |
| "- (\"Inverse Scale\", \"→ CPU %\", C[\"green\"]),", | |
| "+ (\"\\u2192\", \"\", C[\"text_dim\"]),", | |
| "+ (\"ONNX LSTM\", \"(1,24,44)\\u2192(1,1)\", C[\"blue\"]),", | |
| "+ (\"\\u2192\", \"\", C[\"text_dim\"]),", | |
| "+ (\"Inverse Scale\", \"\\u2192 CPU %\", C[\"green\"]),", | |
| " ]", | |
| "- for title, sub, color in stages:", | |
| "- if title == \"→\":", | |
| "+ stage_tooltips = [", | |
| "+ \"5 raw metrics: cpu_usage, mem_util, net_in, net_out, disk_io\",", | |
| "+ \"\",", | |
| "+ \"Generates 44 features: time features, lags, rolling windows\",", | |
| "+ \"\",", | |
| "+ \"Normalizes features using saved StandardScaler (mean/std)\",", | |
| "+ \"\",", | |
| "+ \"2-layer LSTM, 65K params, 0.13ms latency via ONNX Runtime\",", | |
| "+ \"\",", | |
| "+ \"Converts normalized prediction back to CPU percentage\",", | |
| "+ ]", | |
| "+ for i, (title, sub, color) in enumerate(stages):", | |
| "+ if title == \"\\u2192\":", | |
| " ctk.CTkLabel(", | |
| "- blocks_frame, text=\"→\", font=(\"Consolas\", 16, \"bold\"),", | |
| "- text_color=C[\"text_dim\"]", | |
| "+ blocks_frame,", | |
| "+ text=\"\\u2192\",", | |
| "+ font=(\"Consolas\", 16, \"bold\"),", | |
| "+ text_color=C[\"text_dim\"],", | |
| " ).pack(side=\"left\", padx=2)", | |
| " else:", | |
| "- f = ctk.CTkFrame(blocks_frame, fg_color=C[\"bg\"], corner_radius=6,", | |
| "- border_width=1, border_color=color)", | |
| "+ f = ctk.CTkFrame(", | |
| "+ blocks_frame,", | |
| "+ fg_color=C[\"bg\"],", | |
| "+ corner_radius=6,", | |
| "+ border_width=1,", | |
| "+ border_color=color,", | |
| "+ )", | |
| " f.pack(side=\"left\", padx=4)", | |
| "- ctk.CTkLabel(f, text=title, font=(\"Consolas\", 9, \"bold\"),", | |
| "- text_color=color).pack(padx=6, pady=(2, 0))", | |
| "- ctk.CTkLabel(f, text=sub, font=(\"Consolas\", 8),", | |
| "- text_color=C[\"text_dim\"]).pack(padx=6, pady=(0, 2))", | |
| "+ ctk.CTkLabel(", | |
| "+ f,", | |
| "+ text=title,", | |
| "+ font=(\"Consolas\", 9, \"bold\"),", | |
| "+ text_color=color,", | |
| "+ ).pack(padx=6, pady=(2, 0))", | |
| "+ ctk.CTkLabel(", | |
| "+ f,", | |
| "+ text=sub,", | |
| "+ font=(\"Consolas\", 8),", | |
| "+ text_color=C[\"text_dim\"],", | |
| "+ ).pack(padx=6, pady=(0, 2))", | |
| "+ if stage_tooltips[i]:", | |
| "+ Tooltip(f, stage_tooltips[i])", | |
| " ", | |
| "- # Middle: prediction output + buffer", | |
| "+ # Middle: prediction output + buffer + accuracy", | |
| " mid = ctk.CTkFrame(tab, fg_color=\"transparent\")", | |
| " mid.pack(fill=\"x\", padx=8, pady=4)", | |
| " ", | |
| " # Buffer status", | |
| "- buf_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8, width=250)", | |
| "+ buf_frame = ctk.CTkFrame(", | |
| "+ mid, fg_color=C[\"bg_card\"], corner_radius=8, width=220", | |
| "+ )", | |
| " buf_frame.pack(side=\"left\", padx=(0, 8), fill=\"y\")", | |
| " buf_frame.pack_propagate(False)", | |
| "- ctk.CTkLabel(buf_frame, text=\"Buffer Status\", font=(\"Consolas\", 11, \"bold\"),", | |
| "- text_color=C[\"text\"]).pack(pady=(8, 4))", | |
| "- self._buf_progress = ctk.CTkProgressBar(buf_frame, width=200)", | |
| "+ ctk.CTkLabel(", | |
| "+ buf_frame,", | |
| "+ text=\"Buffer Status\",", | |
| "+ font=(\"Consolas\", 11, \"bold\"),", | |
| "+ text_color=C[\"text\"],", | |
| "+ ).pack(pady=(8, 4))", | |
| "+ self._buf_progress = ctk.CTkProgressBar(buf_frame, width=180)", | |
| " self._buf_progress.pack(pady=4, padx=12)", | |
| " self._buf_progress.set(0)", | |
| " self._buf_lbl = ctk.CTkLabel(", | |
| "- buf_frame, text=\"0 / 24 NOT READY\", font=(\"Consolas\", 10),", | |
| "- text_color=C[\"red\"]", | |
| "+ buf_frame,", | |
| "+ text=\"0 / 24 NOT READY\",", | |
| "+ font=(\"Consolas\", 10),", | |
| "+ text_color=C[\"red\"],", | |
| " )", | |
| " self._buf_lbl.pack()", | |
| "+ Tooltip(", | |
| "+ buf_frame,", | |
| "+ \"LSTM requires 24 timesteps (2h of 5-min data) before predictions.\",", | |
| "+ )", | |
| " ", | |
| " # Prediction big number", | |
| "- pred_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8, width=200)", | |
| "+ pred_frame = ctk.CTkFrame(", | |
| "+ mid, fg_color=C[\"bg_card\"], corner_radius=8, width=180", | |
| "+ )", | |
| " pred_frame.pack(side=\"left\", padx=8, fill=\"y\")", | |
| " pred_frame.pack_propagate(False)", | |
| "- ctk.CTkLabel(pred_frame, text=\"ML Prediction\", font=(\"Consolas\", 11, \"bold\"),", | |
| "- text_color=C[\"text\"]).pack(pady=(8, 2))", | |
| "+ ctk.CTkLabel(", | |
| "+ pred_frame,", | |
| "+ text=\"ML Prediction\",", | |
| "+ font=(\"Consolas\", 11, \"bold\"),", | |
| "+ text_color=C[\"text\"],", | |
| "+ ).pack(pady=(8, 2))", | |
| " self._pred_big = ctk.CTkLabel(", | |
| "- pred_frame, text=\"—\", font=(\"Consolas\", 28, \"bold\"),", | |
| "- text_color=C[\"blue\"]", | |
| "+ pred_frame,", | |
| "+ text=\"\\u2014\",", | |
| "+ font=(\"Consolas\", 28, \"bold\"),", | |
| "+ text_color=C[\"blue\"],", | |
| " )", | |
| " self._pred_big.pack(pady=4)", | |
| " self._pred_actual = ctk.CTkLabel(", | |
| "- pred_frame, text=\"Actual: —\", font=(\"Consolas\", 10),", | |
| "- text_color=C[\"text_dim\"]", | |
| "+ pred_frame,", | |
| "+ text=\"Actual: \\u2014\",", | |
| "+ font=(\"Consolas\", 10),", | |
| "+ text_color=C[\"text_dim\"],", | |
| " )", | |
| " self._pred_actual.pack()", | |
| " ", | |
| "+ # Prediction accuracy panel", | |
| "+ acc_frame = ctk.CTkFrame(", | |
| "+ mid, fg_color=C[\"bg_card\"], corner_radius=8, width=200", | |
| "+ )", | |
| "+ acc_frame.pack(side=\"left\", padx=8, fill=\"y\")", | |
| "+ acc_frame.pack_propagate(False)", | |
| "+ ctk.CTkLabel(", | |
| "+ acc_frame,", | |
| "+ text=\"Prediction Accuracy\",", | |
| "+ font=(\"Consolas\", 11, \"bold\"),", | |
| "+ text_color=C[\"text\"],", | |
| "+ ).pack(pady=(8, 4))", | |
| "+ self._acc_mae_lbl = ctk.CTkLabel(", | |
| "+ acc_frame,", | |
| "+ text=\"MAE: \\u2014\",", | |
| "+ font=(\"Consolas\", 10),", | |
| "+ text_color=C[\"text_dim\"],", | |
| "+ )", | |
| "+ self._acc_mae_lbl.pack(anchor=\"w\", padx=12)", | |
| "+ self._acc_rmse_lbl = ctk.CTkLabel(", | |
| "+ acc_frame,", | |
| "+ text=\"RMSE: \\u2014\",", | |
| "+ font=(\"Consolas\", 10),", | |
| "+ text_color=C[\"text_dim\"],", | |
| "+ )", | |
| "+ self._acc_rmse_lbl.pack(anchor=\"w\", padx=12)", | |
| "+ self._acc_err_lbl = ctk.CTkLabel(", | |
| "+ acc_frame,", | |
| "+ text=\"Error: \\u2014\",", | |
| "+ font=(\"Consolas\", 10),", | |
| "+ text_color=C[\"text_dim\"],", | |
| "+ )", | |
| "+ self._acc_err_lbl.pack(anchor=\"w\", padx=12)", | |
| "+ Tooltip(", | |
| "+ acc_frame,", | |
| "+ \"MAE: Mean Absolute Error\\n\"", | |
| "+ \"RMSE: Root Mean Squared Error\\n\"", | |
| "+ \"Error: Current |predicted - actual|\",", | |
| "+ )", | |
| "+", | |
| " # Raw metrics panel", | |
| "- raw_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8)", | |
| "+ raw_frame = ctk.CTkFrame(", | |
| "+ mid, fg_color=C[\"bg_card\"], corner_radius=8", | |
| "+ )", | |
| " raw_frame.pack(side=\"left\", fill=\"both\", expand=True, padx=8)", | |
| "- ctk.CTkLabel(raw_frame, text=\"Current Metrics\", font=(\"Consolas\", 11, \"bold\"),", | |
| "- text_color=C[\"text\"]).pack(pady=(8, 4))", | |
| "+ ctk.CTkLabel(", | |
| "+ raw_frame,", | |
| "+ text=\"Current Metrics\",", | |
| "+ font=(\"Consolas\", 11, \"bold\"),", | |
| "+ text_color=C[\"text\"],", | |
| "+ ).pack(pady=(8, 4))", | |
| " self._raw_labels: dict[str, ctk.CTkLabel] = {}", | |
| "- for metric in [\"cpu_usage\", \"mem_util\", \"net_in\", \"net_out\", \"disk_io\"]:", | |
| "- lbl = ctk.CTkLabel(raw_frame, text=f\"{metric}: —\",", | |
| "- font=(\"Consolas\", 10), text_color=C[\"text_dim\"])", | |
| "- lbl.pack(anchor=\"w\", padx=12)", | |
| "+ metrics_info = {", | |
| "+ \"cpu_usage\": \"CPU utilization (%)\",", | |
| "+ \"mem_util\": \"Memory utilization (%)\",", | |
| "+ \"net_in\": \"Network inbound (KB/s)\",", | |
| "+ \"net_out\": \"Network outbound (KB/s)\",", | |
| "+ \"disk_io\": \"Disk I/O utilization (%)\",", | |
| "+ }", | |
| "+ for metric, desc in metrics_info.items():", | |
| "+ row = ctk.CTkFrame(raw_frame, fg_color=\"transparent\")", | |
| "+ row.pack(fill=\"x\", padx=12)", | |
| "+ lbl = ctk.CTkLabel(", | |
| "+ row,", | |
| "+ text=f\"{metric}: \\u2014\",", | |
| "+ font=(\"Consolas\", 10),", | |
| "+ text_color=C[\"text_dim\"],", | |
| "+ )", | |
| "+ lbl.pack(side=\"left\")", | |
| " self._raw_labels[metric] = lbl", | |
| "+ # Mini progress bar for each metric", | |
| "+ bar = ctk.CTkProgressBar(row, width=60, height=6)", | |
| "+ bar.pack(side=\"right\", padx=4)", | |
| "+ bar.set(0)", | |
| "+ self._raw_labels[f\"{metric}_bar\"] = bar", | |
| "+ Tooltip(raw_frame, \"Simulated from CPU data. In production, \"", | |
| "+ \"these come from Prometheus.\")", | |
| " ", | |
| " # Bottom: prediction history chart", | |
| "- self._nn_fig, self._nn_ax = plt.subplots(1, 1, figsize=(10, 3), dpi=80)", | |
| "+ self._nn_fig, self._nn_ax = plt.subplots(", | |
| "+ 1, 1, figsize=(10, 3), dpi=80", | |
| "+ )", | |
| " self._nn_canvas = embed_figure(tab, self._nn_fig)", | |
| " ", | |
| " # --- Tab 2: Scaling Algorithm ---", | |
| " ", | |
| "- def _build_algo_tab(self):", | |
| "+ def _build_algo_tab(self) -> None:", | |
| " tab = self._tab_algo", | |
| " ", | |
| " # 9-step pipeline" | |
| ] | |
| }, | |
| { | |
| "oldStart": 548, | |
| "oldLines": 25, | |
| "newStart": 1208, | |
| "newLines": 42, | |
| "lines": [ | |
| " pipe.pack(fill=\"x\", padx=4, pady=4)", | |
| " ", | |
| " step_names = [", | |
| "- \"EMA\", \"Hysteresis\", \"Desired\", \"Clamp\",", | |
| "- \"Rate Limit\", \"Cooldown\", \"Stabilize\", \"No Change?\", \"Commit\"", | |
| "+ \"EMA\",", | |
| "+ \"Hysteresis\",", | |
| "+ \"Desired\",", | |
| "+ \"Clamp\",", | |
| "+ \"Rate Limit\",", | |
| "+ \"Cooldown\",", | |
| "+ \"Stabilize\",", | |
| "+ \"No Change?\",", | |
| "+ \"Commit\",", | |
| " ]", | |
| " self._algo_blocks: list[PipelineBlock] = []", | |
| " for i, name in enumerate(step_names):", | |
| " if i > 0:", | |
| "- ctk.CTkLabel(pipe, text=\"→\", font=(\"Consolas\", 14, \"bold\"),", | |
| "- text_color=C[\"text_dim\"]).pack(side=\"left\", padx=1)", | |
| "+ ctk.CTkLabel(", | |
| "+ pipe,", | |
| "+ text=\"\\u2192\",", | |
| "+ font=(\"Consolas\", 14, \"bold\"),", | |
| "+ text_color=C[\"text_dim\"],", | |
| "+ ).pack(side=\"left\", padx=1)", | |
| " block = PipelineBlock(pipe, i + 1, name)", | |
| " block.pack(side=\"left\", padx=2)", | |
| " self._algo_blocks.append(block)", | |
| " ", | |
| " # State panel", | |
| "- state_frame = ctk.CTkFrame(tab, fg_color=C[\"bg_card\"], corner_radius=8, height=40)", | |
| "+ state_frame = ctk.CTkFrame(", | |
| "+ tab, fg_color=C[\"bg_card\"], corner_radius=8, height=40", | |
| "+ )", | |
| " state_frame.pack(fill=\"x\", padx=8, pady=4)", | |
| " self._state_lbl = ctk.CTkLabel(", | |
| " state_frame,", | |
| "- text=\"Cooldown UP: — | Cooldown DOWN: — | Consec. Below: — | Direction: —\",", | |
| "- font=(\"Consolas\", 10), text_color=C[\"text_dim\"]", | |
| "+ text=(", | |
| "+ \"Cooldown UP: \\u2014 | Cooldown DOWN: \\u2014 | \"", | |
| "+ \"Consec. Below: \\u2014 | Direction: \\u2014\"", | |
| "+ ),", | |
| "+ font=(\"Consolas\", 10),", | |
| "+ text_color=C[\"text_dim\"],", | |
| " )", | |
| " self._state_lbl.pack(pady=6)", | |
| " " | |
| ] | |
| }, | |
| { | |
| "oldStart": 576, | |
| "oldLines": 67, | |
| "newStart": 1253, | |
| "newLines": 156, | |
| "lines": [ | |
| " ", | |
| " left = ctk.CTkFrame(charts, fg_color=\"transparent\")", | |
| " left.pack(side=\"left\", fill=\"both\", expand=True)", | |
| "- self._algo_cpu_fig, self._algo_cpu_ax = plt.subplots(1, 1, figsize=(5.5, 3), dpi=80)", | |
| "+ self._algo_cpu_fig, self._algo_cpu_ax = plt.subplots(", | |
| "+ 1, 1, figsize=(5.5, 3), dpi=80", | |
| "+ )", | |
| " self._algo_cpu_canvas = embed_figure(left, self._algo_cpu_fig)", | |
| " ", | |
| " right = ctk.CTkFrame(charts, fg_color=\"transparent\")", | |
| " right.pack(side=\"left\", fill=\"both\", expand=True)", | |
| "- self._algo_rep_fig, self._algo_rep_ax = plt.subplots(1, 1, figsize=(5.5, 3), dpi=80)", | |
| "+ self._algo_rep_fig, self._algo_rep_ax = plt.subplots(", | |
| "+ 1, 1, figsize=(5.5, 3), dpi=80", | |
| "+ )", | |
| " self._algo_rep_canvas = embed_figure(right, self._algo_rep_fig)", | |
| " ", | |
| " # Decision log", | |
| " self._decision_log = ctk.CTkTextbox(", | |
| "- tab, height=100, font=(\"Consolas\", 9), fg_color=C[\"bg\"],", | |
| "- text_color=C[\"text_dim\"]", | |
| "+ tab,", | |
| "+ height=100,", | |
| "+ font=(\"Consolas\", 9),", | |
| "+ fg_color=C[\"bg\"],", | |
| "+ text_color=C[\"text_dim\"],", | |
| " )", | |
| " self._decision_log.pack(fill=\"x\", padx=8, pady=(0, 4))", | |
| " ", | |
| " # --- Tab 3: Experiment Comparison ---", | |
| " ", | |
| "- def _build_exp_tab(self):", | |
| "+ def _build_exp_tab(self) -> None:", | |
| " tab = self._tab_exp", | |
| " ", | |
| " # Main comparison chart", | |
| "- self._exp_fig, self._exp_axes = plt.subplots(2, 1, figsize=(10, 5), dpi=80,", | |
| "- gridspec_kw={\"height_ratios\": [2, 1]})", | |
| "+ self._exp_fig, self._exp_axes = plt.subplots(", | |
| "+ 3,", | |
| "+ 1,", | |
| "+ figsize=(10, 6),", | |
| "+ dpi=80,", | |
| "+ gridspec_kw={\"height_ratios\": [2, 1, 1]},", | |
| "+ )", | |
| " self._exp_canvas = embed_figure(tab, self._exp_fig)", | |
| " ", | |
| " # KPI cards grid", | |
| " cards_frame = ctk.CTkFrame(tab, fg_color=\"transparent\")", | |
| " cards_frame.pack(fill=\"x\", padx=8, pady=4)", | |
| " ", | |
| "- kpi_names = [", | |
| "- \"Avg Replicas\", \"Replica-Min\", \"SLA Violations\", \"Scaling Events\",", | |
| "- \"Over-Prov %\", \"Under-Prov %\", \"Reaction Time\", \"Cost Savings %\"", | |
| "+ kpi_info = [", | |
| "+ (\"Avg Replicas\", \"lower\", \"Average number of running replicas\"),", | |
| "+ (\"Replica-Min\", \"lower\", \"Total replica-minutes consumed\"),", | |
| "+ (", | |
| "+ \"SLA Violations\",", | |
| "+ \"lower\",", | |
| "+ \"Steps where CPU exceeded SLA threshold\",", | |
| "+ ),", | |
| "+ (", | |
| "+ \"Scaling Events\",", | |
| "+ \"lower\",", | |
| "+ \"Number of replica count changes\",", | |
| "+ ),", | |
| "+ (", | |
| "+ \"Over-Prov %\",", | |
| "+ \"lower\",", | |
| "+ \"Percentage of over-provisioned capacity\",", | |
| "+ ),", | |
| "+ (", | |
| "+ \"Under-Prov %\",", | |
| "+ \"lower\",", | |
| "+ \"Percentage of under-provisioned capacity\",", | |
| "+ ),", | |
| "+ (", | |
| "+ \"Reaction Time\",", | |
| "+ \"lower\",", | |
| "+ \"Avg steps to respond to demand spike\",", | |
| "+ ),", | |
| "+ (", | |
| "+ \"Cost Savings %\",", | |
| "+ \"higher\",", | |
| "+ \"ML cost savings vs HPA (negative = higher cost)\",", | |
| "+ ),", | |
| " ]", | |
| " self._kpi_cards: list[KPICard] = []", | |
| "- for i, name in enumerate(kpi_names):", | |
| "- card = KPICard(cards_frame, name)", | |
| "- card.grid(row=i // 4, column=i % 4, padx=4, pady=4, sticky=\"nsew\")", | |
| "+ self._kpi_better: list[str] = []", | |
| "+ for i, (name, better, tooltip) in enumerate(kpi_info):", | |
| "+ card = KPICard(cards_frame, name, tooltip=tooltip)", | |
| "+ card.grid(", | |
| "+ row=i // 4, column=i % 4, padx=4, pady=4, sticky=\"nsew\"", | |
| "+ )", | |
| " self._kpi_cards.append(card)", | |
| "+ self._kpi_better.append(better)", | |
| " for c in range(4):", | |
| " cards_frame.columnconfigure(c, weight=1)", | |
| " ", | |
| "+ # --- Tab 4: Activity Log ---", | |
| "+", | |
| "+ def _build_log_tab(self) -> None:", | |
| "+ tab = self._tab_log", | |
| "+", | |
| "+ ctk.CTkLabel(", | |
| "+ tab,", | |
| "+ text=\"Activity & Notification Feed\",", | |
| "+ font=(\"Consolas\", 14, \"bold\"),", | |
| "+ text_color=C[\"text\"],", | |
| "+ ).pack(pady=(8, 4), padx=8, anchor=\"w\")", | |
| "+", | |
| "+ ctk.CTkLabel(", | |
| "+ tab,", | |
| "+ text=\"Scaling events, alerts, and system notifications\",", | |
| "+ font=(\"Consolas\", 10),", | |
| "+ text_color=C[\"text_dim\"],", | |
| "+ ).pack(padx=8, anchor=\"w\")", | |
| "+", | |
| "+ self._notif_frame = ctk.CTkScrollableFrame(", | |
| "+ tab, fg_color=C[\"bg\"], corner_radius=8", | |
| "+ )", | |
| "+ self._notif_frame.pack(", | |
| "+ fill=\"both\", expand=True, padx=8, pady=8", | |
| "+ )", | |
| "+", | |
| " # -------------------------------------------------------------------", | |
| " # Update all visuals", | |
| " # -------------------------------------------------------------------", | |
| " ", | |
| "- def _update_all(self):", | |
| "+ def _update_all(self) -> None:", | |
| " sc = self._scenarios[self._current_scenario_idx]", | |
| " total = len(sc.actual_cpu)", | |
| " self._step_lbl.configure(text=f\"Step: {self._step} / {total}\")", | |
| " ", | |
| "+ # Time display (5 min per step)", | |
| "+ minutes = self._step * 5", | |
| "+ hours = minutes // 60", | |
| "+ mins = minutes % 60", | |
| "+ self._time_lbl.configure(text=f\"{hours:02d}:{mins:02d}\")", | |
| "+", | |
| "+ # Timeline", | |
| "+ progress = self._step / total if total > 0 else 0", | |
| "+ self._timeline_bar.set(progress)", | |
| "+ self._timeline_pct.configure(text=f\"{progress * 100:.0f}%\")", | |
| "+", | |
| " # Highlight active scenario button", | |
| " for i, btn in enumerate(self._scenario_btns):", | |
| " btn.configure(", | |
| "- fg_color=C[\"blue\"] if i == self._current_scenario_idx else C[\"bg_card\"]", | |
| "+ fg_color=C[\"blue\"]", | |
| "+ if i == self._current_scenario_idx", | |
| "+ else C[\"bg_card\"]", | |
| " )", | |
| " ", | |
| "+ # Update scenario description", | |
| "+ self._scenario_desc.configure(text=sc.description)", | |
| "+", | |
| " self._update_nn_tab()", | |
| " self._update_algo_tab()", | |
| " self._update_exp_tab()", | |
| "+ self._update_log_tab()", | |
| " ", | |
| "- def _update_nn_tab(self):", | |
| "+ def _update_nn_tab(self) -> None:", | |
| " n = self._step", | |
| " ", | |
| " # Buffer status" | |
| ] | |
| }, | |
| { | |
| "oldStart": 644, | |
| "oldLines": 54, | |
| "newStart": 1410, | |
| "newLines": 129, | |
| "lines": [ | |
| " self._buf_progress.set(buf_fill / 24)", | |
| " ready = n >= 24", | |
| " self._buf_lbl.configure(", | |
| "- text=f\"{min(n, 54)} / 24 {'READY' if ready else 'FILLING...'}\",", | |
| "- text_color=C[\"green\"] if ready else C[\"red\"]", | |
| "+ text=f\"{min(n, 24)} / 24 {'READY' if ready else 'FILLING...'}\",", | |
| "+ text_color=C[\"green\"] if ready else C[\"red\"],", | |
| " )", | |
| " ", | |
| " # Current prediction", | |
| " if n > 0:", | |
| " pred = self._predicted_trace[-1]", | |
| " actual = self._actual_trace[-1]", | |
| "- color = C[\"green\"] if pred < 42 else C[\"yellow\"] if pred < 66 else C[\"orange\"] if pred < 85 else C[\"red\"]", | |
| "- self._pred_big.configure(text=f\"{pred:.1f}%\", text_color=color)", | |
| "+ color = (", | |
| "+ C[\"green\"]", | |
| "+ if pred < 42", | |
| "+ else C[\"yellow\"]", | |
| "+ if pred < 66", | |
| "+ else C[\"orange\"]", | |
| "+ if pred < 85", | |
| "+ else C[\"red\"]", | |
| "+ )", | |
| "+ self._pred_big.configure(", | |
| "+ text=f\"{pred:.1f}%\", text_color=color", | |
| "+ )", | |
| " self._pred_actual.configure(text=f\"Actual: {actual:.1f}%\")", | |
| " ", | |
| " # Raw metrics (simulated from CPU)", | |
| "- self._raw_labels[\"cpu_usage\"].configure(text=f\"cpu_usage: {actual:.1f}%\")", | |
| "- self._raw_labels[\"mem_util\"].configure(text=f\"mem_util: {actual * 0.6:.1f}%\")", | |
| "- self._raw_labels[\"net_in\"].configure(text=f\"net_in: {actual * 1.2:.0f} KB/s\")", | |
| "- self._raw_labels[\"net_out\"].configure(text=f\"net_out: {actual * 0.8:.0f} KB/s\")", | |
| "- self._raw_labels[\"disk_io\"].configure(text=f\"disk_io: {actual * 0.3:.1f}%\")", | |
| "+ metrics_vals = {", | |
| "+ \"cpu_usage\": (actual, 100),", | |
| "+ \"mem_util\": (actual * 0.6, 100),", | |
| "+ \"net_in\": (actual * 1.2, 120),", | |
| "+ \"net_out\": (actual * 0.8, 80),", | |
| "+ \"disk_io\": (actual * 0.3, 30),", | |
| "+ }", | |
| "+ for metric, (val, max_val) in metrics_vals.items():", | |
| "+ unit = \"%\" if \"usage\" in metric or \"util\" in metric or \"io\" in metric else \" KB/s\"", | |
| "+ self._raw_labels[metric].configure(", | |
| "+ text=f\"{metric}: {val:.1f}{unit}\"", | |
| "+ )", | |
| "+ bar_key = f\"{metric}_bar\"", | |
| "+ if bar_key in self._raw_labels:", | |
| "+ bar_val = min(val / max_val, 1.0) if max_val > 0 else 0", | |
| "+ self._raw_labels[bar_key].set(bar_val)", | |
| " ", | |
| "+ # Prediction accuracy", | |
| "+ errors = np.abs(", | |
| "+ np.array(self._actual_trace)", | |
| "+ - np.array(self._predicted_trace)", | |
| "+ )", | |
| "+ mae = float(np.mean(errors))", | |
| "+ rmse = float(np.sqrt(np.mean(errors**2)))", | |
| "+ cur_err = abs(actual - pred)", | |
| "+", | |
| "+ mae_color = (", | |
| "+ C[\"green\"]", | |
| "+ if mae < 5", | |
| "+ else C[\"yellow\"]", | |
| "+ if mae < 10", | |
| "+ else C[\"red\"]", | |
| "+ )", | |
| "+ self._acc_mae_lbl.configure(", | |
| "+ text=f\"MAE: {mae:.2f}%\", text_color=mae_color", | |
| "+ )", | |
| "+ self._acc_rmse_lbl.configure(", | |
| "+ text=f\"RMSE: {rmse:.2f}%\", text_color=mae_color", | |
| "+ )", | |
| "+ err_color = (", | |
| "+ C[\"green\"]", | |
| "+ if cur_err < 5", | |
| "+ else C[\"yellow\"]", | |
| "+ if cur_err < 10", | |
| "+ else C[\"red\"]", | |
| "+ )", | |
| "+ self._acc_err_lbl.configure(", | |
| "+ text=f\"Error: {cur_err:.2f}%\", text_color=err_color", | |
| "+ )", | |
| "+", | |
| " # Prediction history chart", | |
| " self._nn_ax.clear()", | |
| " if n > 1:", | |
| "- x = range(max(0, n - 80), n)", | |
| "- a_slice = self._actual_trace[max(0, n - 80):]", | |
| "- p_slice = self._predicted_trace[max(0, n - 80):]", | |
| "- self._nn_ax.plot(x, a_slice, color=C[\"green\"], lw=1.5, label=\"Actual CPU\")", | |
| "- self._nn_ax.plot(x, p_slice, color=C[\"purple\"], lw=1.5, ls=\"--\", label=\"Predicted\")", | |
| "- self._nn_ax.axhline(60, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.5)", | |
| "+ window = 80", | |
| "+ x = list(range(max(0, n - window), n))", | |
| "+ a_slice = self._actual_trace[max(0, n - window):]", | |
| "+ p_slice = self._predicted_trace[max(0, n - window):]", | |
| "+ self._nn_ax.plot(", | |
| "+ x, a_slice, color=C[\"green\"], lw=1.5, label=\"Actual CPU\"", | |
| "+ )", | |
| "+ self._nn_ax.plot(", | |
| "+ x,", | |
| "+ p_slice,", | |
| "+ color=C[\"purple\"],", | |
| "+ lw=1.5,", | |
| "+ ls=\"--\",", | |
| "+ label=\"Predicted\",", | |
| "+ )", | |
| "+ # Fill error band", | |
| "+ self._nn_ax.fill_between(", | |
| "+ x, a_slice, p_slice, alpha=0.12, color=C[\"purple\"]", | |
| "+ )", | |
| "+ self._nn_ax.axhline(", | |
| "+ 60, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.5", | |
| "+ )", | |
| " self._nn_ax.set_ylabel(\"CPU %\")", | |
| " self._nn_ax.set_ylim(0, 100)", | |
| " self._nn_ax.legend(loc=\"upper right\", fontsize=8)", | |
| " self._nn_ax.grid(True, alpha=0.2)", | |
| "- self._nn_ax.set_title(\"Prediction History (last 80 steps)\", fontsize=10)", | |
| "+ self._nn_ax.set_title(", | |
| "+ f\"Prediction History (last 80 steps) | Step {n}\",", | |
| "+ fontsize=10,", | |
| "+ )", | |
| " self._nn_fig.tight_layout()", | |
| " self._nn_canvas.draw_idle()", | |
| " ", | |
| "- def _update_algo_tab(self):", | |
| "+ def _update_algo_tab(self) -> None:", | |
| " n = self._step", | |
| " if n == 0:", | |
| " for block in self._algo_blocks:", | |
| "- block.update(\"—\", \"skipped\")", | |
| "+ block.update(\"\\u2014\", \"skipped\")", | |
| " return", | |
| " ", | |
| " # Get current step details", | |
| " pred = self._predicted_trace[-1]", | |
| " smoothed = self._smoothed_trace[-1]", | |
| " ml_replicas = self._ml_replicas_trace[-1]", | |
| "- prev_replicas = self._ml_replicas_trace[-2] if n > 1 else 2", | |
| "+ prev_replicas = (", | |
| "+ self._ml_replicas_trace[-2] if n > 1 else 2", | |
| "+ )", | |
| " ", | |
| " target = self._target_var.get()", | |
| " up_thresh = target * self._up_thresh_var.get()" | |
| ] | |
| }, | |
| { | |
| "oldStart": 706, | |
| "oldLines": 57, | |
| "newStart": 1547, | |
| "newLines": 124, | |
| "lines": [ | |
| " direction = \"HOLD\"", | |
| " ", | |
| " # Update blocks", | |
| "- self._algo_blocks[0].update(f\"{pred:.1f}→{smoothed:.1f}%\", \"active\")", | |
| "+ self._algo_blocks[0].update(", | |
| "+ f\"{pred:.1f}\\u2192{smoothed:.1f}%\", \"active\"", | |
| "+ )", | |
| "+ cmp_sym = (", | |
| "+ \">\"", | |
| "+ if direction == \"UP\"", | |
| "+ else \"<\"", | |
| "+ if direction == \"DOWN\"", | |
| "+ else \"in\"", | |
| "+ )", | |
| " self._algo_blocks[1].update(", | |
| "- f\"{smoothed:.0f}% {'>' if direction == 'UP' else '<' if direction == 'DOWN' else 'in'} \"", | |
| "- f\"[{down_thresh:.0f},{up_thresh:.0f}]\\n→{direction}\",", | |
| "- \"active\" if direction != \"HOLD\" else \"blocked\"", | |
| "+ f\"{smoothed:.0f}% {cmp_sym} \"", | |
| "+ f\"[{down_thresh:.0f},{up_thresh:.0f}]\"", | |
| "+ f\"\\n\\u2192{direction}\",", | |
| "+ \"active\" if direction != \"HOLD\" else \"blocked\",", | |
| " )", | |
| " ", | |
| "+ min_r = int(self._min_rep_var.get())", | |
| "+ max_r = int(self._max_rep_var.get())", | |
| "+", | |
| " if direction == \"HOLD\":", | |
| " for i in range(2, 9):", | |
| "- self._algo_blocks[i].update(\"—\", \"skipped\")", | |
| "+ self._algo_blocks[i].update(\"\\u2014\", \"skipped\")", | |
| " else:", | |
| " raw_desired = math.ceil(prev_replicas * smoothed / target)", | |
| " if direction == \"UP\":", | |
| "- raw_desired = math.ceil(raw_desired * (1 + self._safety_var.get()))", | |
| "- self._algo_blocks[2].update(f\"{prev_replicas}×{smoothed:.0f}/{target:.0f}\\n={raw_desired}\", \"active\")", | |
| "- clamped = max(2, min(8, raw_desired))", | |
| "- self._algo_blocks[3].update(f\"[2,8]→{clamped}\", \"active\")", | |
| "- rate_lim = min(clamped, prev_replicas + 2) if direction == \"UP\" else max(clamped, prev_replicas - 1)", | |
| "- self._algo_blocks[4].update(f\"±limit→{rate_lim}\", \"active\")", | |
| "- self._algo_blocks[5].update(\"OFF ✓\", \"active\")", | |
| "- self._algo_blocks[6].update(\"N/A\" if direction == \"UP\" else \"check\", \"active\")", | |
| "+ raw_desired = math.ceil(", | |
| "+ raw_desired * (1 + self._safety_var.get())", | |
| "+ )", | |
| "+ self._algo_blocks[2].update(", | |
| "+ f\"{prev_replicas}\\u00d7{smoothed:.0f}\"", | |
| "+ f\"/{target:.0f}\\n={raw_desired}\",", | |
| "+ \"active\",", | |
| "+ )", | |
| "+ clamped = max(min_r, min(max_r, raw_desired))", | |
| "+ self._algo_blocks[3].update(", | |
| "+ f\"[{min_r},{max_r}]\\u2192{clamped}\", \"active\"", | |
| "+ )", | |
| "+ rate_lim = (", | |
| "+ min(clamped, prev_replicas + 2)", | |
| "+ if direction == \"UP\"", | |
| "+ else max(clamped, prev_replicas - 1)", | |
| "+ )", | |
| "+ self._algo_blocks[4].update(", | |
| "+ f\"\\u00b1limit\\u2192{rate_lim}\", \"active\"", | |
| "+ )", | |
| "+ self._algo_blocks[5].update(\"OFF \\u2713\", \"active\")", | |
| "+ self._algo_blocks[6].update(", | |
| "+ \"N/A\" if direction == \"UP\" else \"check\", \"active\"", | |
| "+ )", | |
| " ", | |
| " executed = ml_replicas != prev_replicas", | |
| "+ eq_sym = \"\\u2260\" if executed else \"=\"", | |
| " self._algo_blocks[7].update(", | |
| "- f\"{ml_replicas}{'≠' if executed else '='}{prev_replicas}\",", | |
| "- \"active\" if executed else \"blocked\"", | |
| "+ f\"{ml_replicas}{eq_sym}{prev_replicas}\",", | |
| "+ \"active\" if executed else \"blocked\",", | |
| " )", | |
| " if executed:", | |
| "- self._algo_blocks[8].update(f\"{prev_replicas}→{ml_replicas}\", \"executed\")", | |
| "+ self._algo_blocks[8].update(", | |
| "+ f\"{prev_replicas}\\u2192{ml_replicas}\", \"executed\"", | |
| "+ )", | |
| " else:", | |
| " self._algo_blocks[8].update(\"hold\", \"blocked\")", | |
| " ", | |
| " # State panel", | |
| " sim = self._ml_sim", | |
| "+ dir_color = {", | |
| "+ \"UP\": C[\"green\"],", | |
| "+ \"DOWN\": C[\"orange\"],", | |
| "+ \"HOLD\": C[\"text_dim\"],", | |
| "+ }.get(direction, C[\"text_dim\"])", | |
| " self._state_lbl.configure(", | |
| " text=f\"Direction: {direction} | \"", | |
| "- f\"Consec. Below: {sim._consecutive_below} | \"", | |
| "- f\"Replicas: {ml_replicas} | \"", | |
| "- f\"Smoothed: {smoothed:.1f}%\"", | |
| "+ f\"Consec. Below: {sim._consecutive_below} | \"", | |
| "+ f\"Replicas: {ml_replicas} | \"", | |
| "+ f\"Smoothed: {smoothed:.1f}%\",", | |
| "+ text_color=dir_color,", | |
| " )", | |
| " ", | |
| " # CPU chart", | |
| " self._algo_cpu_ax.clear()", | |
| " if n > 1:", | |
| "- x = range(n)", | |
| "- self._algo_cpu_ax.plot(x, self._smoothed_trace, color=C[\"blue\"], lw=1.5, label=\"Smoothed\")", | |
| "- self._algo_cpu_ax.plot(x, self._actual_trace, color=C[\"green\"], lw=1, alpha=0.5, label=\"Actual\")", | |
| "- self._algo_cpu_ax.axhline(up_thresh, color=C[\"red\"], ls=\"--\", lw=0.8, label=f\"Up {up_thresh:.0f}%\")", | |
| "- self._algo_cpu_ax.axhline(down_thresh, color=C[\"orange\"], ls=\"--\", lw=0.8, label=f\"Down {down_thresh:.0f}%\")", | |
| "- self._algo_cpu_ax.axhline(target, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.6)", | |
| "- self._algo_cpu_ax.axhspan(down_thresh, up_thresh, alpha=0.08, color=C[\"yellow\"])", | |
| "+ x = list(range(n))", | |
| "+ self._algo_cpu_ax.plot(", | |
| "+ x,", | |
| "+ self._smoothed_trace,", | |
| "+ color=C[\"blue\"],", | |
| "+ lw=1.5,", | |
| "+ label=\"Smoothed\",", | |
| "+ )", | |
| "+ self._algo_cpu_ax.plot(", | |
| "+ x,", | |
| "+ self._actual_trace,", | |
| "+ color=C[\"green\"],", | |
| "+ lw=1,", | |
| "+ alpha=0.5,", | |
| "+ label=\"Actual\",", | |
| "+ )", | |
| "+ self._algo_cpu_ax.axhline(", | |
| "+ up_thresh,", | |
| "+ color=C[\"red\"],", | |
| "+ ls=\"--\",", | |
| "+ lw=0.8,", | |
| "+ label=f\"Up {up_thresh:.0f}%\",", | |
| "+ )", | |
| "+ self._algo_cpu_ax.axhline(", | |
| "+ down_thresh,", | |
| "+ color=C[\"orange\"],", | |
| "+ ls=\"--\",", | |
| "+ lw=0.8,", | |
| "+ label=f\"Down {down_thresh:.0f}%\",", | |
| "+ )", | |
| "+ self._algo_cpu_ax.axhline(", | |
| "+ target, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.6", | |
| "+ )", | |
| "+ self._algo_cpu_ax.axhspan(", | |
| "+ down_thresh, up_thresh, alpha=0.08, color=C[\"yellow\"]", | |
| "+ )", | |
| " self._algo_cpu_ax.set_ylim(0, 100)", | |
| " self._algo_cpu_ax.set_ylabel(\"CPU %\")", | |
| " self._algo_cpu_ax.legend(loc=\"upper right\", fontsize=7)" | |
| ] | |
| }, | |
| { | |
| "oldStart": 768, | |
| "oldLines": 12, | |
| "newStart": 1676, | |
| "newLines": 39, | |
| "lines": [ | |
| " # Replica chart", | |
| " self._algo_rep_ax.clear()", | |
| " if n > 1:", | |
| "- x = range(n)", | |
| "- self._algo_rep_ax.step(x, self._ml_replicas_trace, color=C[\"blue\"], lw=2, where=\"post\", label=\"ML Replicas\")", | |
| "- self._algo_rep_ax.step(x, self._hpa_replicas_trace, color=C[\"red\"], lw=1.5, where=\"post\", alpha=0.6, label=\"HPA Replicas\")", | |
| "- self._algo_rep_ax.axhline(2, color=C[\"text_dim\"], ls=\":\", lw=0.7, alpha=0.5)", | |
| "- self._algo_rep_ax.axhline(8, color=C[\"text_dim\"], ls=\":\", lw=0.7, alpha=0.5)", | |
| "- self._algo_rep_ax.set_ylim(0, 10)", | |
| "+ x = list(range(n))", | |
| "+ self._algo_rep_ax.step(", | |
| "+ x,", | |
| "+ self._ml_replicas_trace,", | |
| "+ color=C[\"blue\"],", | |
| "+ lw=2,", | |
| "+ where=\"post\",", | |
| "+ label=\"ML Replicas\",", | |
| "+ )", | |
| "+ self._algo_rep_ax.step(", | |
| "+ x,", | |
| "+ self._hpa_replicas_trace,", | |
| "+ color=C[\"red\"],", | |
| "+ lw=1.5,", | |
| "+ where=\"post\",", | |
| "+ alpha=0.6,", | |
| "+ label=\"HPA Replicas\",", | |
| "+ )", | |
| "+ self._algo_rep_ax.axhline(", | |
| "+ min_r,", | |
| "+ color=C[\"text_dim\"],", | |
| "+ ls=\":\",", | |
| "+ lw=0.7,", | |
| "+ alpha=0.5,", | |
| "+ )", | |
| "+ self._algo_rep_ax.axhline(", | |
| "+ max_r,", | |
| "+ color=C[\"text_dim\"],", | |
| "+ ls=\":\",", | |
| "+ lw=0.7,", | |
| "+ alpha=0.5,", | |
| "+ )", | |
| "+ self._algo_rep_ax.set_ylim(0, max_r + 2)", | |
| " self._algo_rep_ax.set_ylabel(\"Replicas\")", | |
| " self._algo_rep_ax.legend(loc=\"upper right\", fontsize=7)", | |
| " self._algo_rep_ax.grid(True, alpha=0.2)" | |
| ] | |
| }, | |
| { | |
| "oldStart": 784, | |
| "oldLines": 80, | |
| "newStart": 1719, | |
| "newLines": 178, | |
| "lines": [ | |
| " # Decision log", | |
| " self._decision_log.configure(state=\"normal\")", | |
| " self._decision_log.delete(\"1.0\", \"end\")", | |
| "- # Show last 20 entries", | |
| " for line in self._decisions_log[-20:]:", | |
| " self._decision_log.insert(\"end\", line + \"\\n\")", | |
| " self._decision_log.configure(state=\"disabled\")", | |
| " self._decision_log.see(\"end\")", | |
| " ", | |
| "- def _update_exp_tab(self):", | |
| "+ def _update_exp_tab(self) -> None:", | |
| " if not hasattr(self, \"_full_metrics\"):", | |
| " return", | |
| " metrics = self._full_metrics", | |
| " history = self._history", | |
| " ", | |
| "- # Top chart: Actual CPU + replicas", | |
| "- ax1, ax2 = self._exp_axes", | |
| "+ ax1, ax2, ax3 = self._exp_axes", | |
| " ax1.clear()", | |
| " ax2.clear()", | |
| "+ ax3.clear()", | |
| " ", | |
| " n = len(history)", | |
| " if n > 0:", | |
| "- x = range(n)", | |
| "+ x = list(range(n))", | |
| " actual = [r.actual_cpu for r in history]", | |
| " hpa_r = [r.hpa_replicas for r in history]", | |
| " ml_r = [r.ml_replicas for r in history]", | |
| " ", | |
| "- # CPU trace (background)", | |
| "- ax1.fill_between(x, actual, alpha=0.15, color=C[\"text_dim\"])", | |
| "- ax1.plot(x, actual, color=C[\"text_dim\"], lw=0.8, alpha=0.5, label=\"Actual CPU\")", | |
| "-", | |
| "- # Replicas", | |
| "- ax1.step(x, [r * 10 for r in hpa_r], color=C[\"red\"], lw=1.5,", | |
| "- where=\"post\", label=\"HPA Replicas (×10)\")", | |
| "- ax1.step(x, [r * 10 for r in ml_r], color=C[\"blue\"], lw=2,", | |
| "- where=\"post\", label=\"ML Replicas (×10)\")", | |
| "-", | |
| "- # SLA threshold", | |
| "+ # Chart 1: CPU + Replicas overlay", | |
| "+ ax1.fill_between(", | |
| "+ x, actual, alpha=0.15, color=C[\"text_dim\"]", | |
| "+ )", | |
| "+ ax1.plot(", | |
| "+ x,", | |
| "+ actual,", | |
| "+ color=C[\"text_dim\"],", | |
| "+ lw=0.8,", | |
| "+ alpha=0.5,", | |
| "+ label=\"Actual CPU\",", | |
| "+ )", | |
| "+ ax1.step(", | |
| "+ x,", | |
| "+ [r * 10 for r in hpa_r],", | |
| "+ color=C[\"red\"],", | |
| "+ lw=1.5,", | |
| "+ where=\"post\",", | |
| "+ label=\"HPA Replicas (\\u00d710)\",", | |
| "+ )", | |
| "+ ax1.step(", | |
| "+ x,", | |
| "+ [r * 10 for r in ml_r],", | |
| "+ color=C[\"blue\"],", | |
| "+ lw=2,", | |
| "+ where=\"post\",", | |
| "+ label=\"ML Replicas (\\u00d710)\",", | |
| "+ )", | |
| " target = self._target_var.get()", | |
| "- ax1.axhline(target * 1.2, color=C[\"red\"], ls=\":\", lw=0.8, alpha=0.5, label=\"SLA Limit\")", | |
| "-", | |
| "- ax1.set_ylabel(\"CPU % / Replicas×10\")", | |
| "+ ax1.axhline(", | |
| "+ target * 1.2,", | |
| "+ color=C[\"red\"],", | |
| "+ ls=\":\",", | |
| "+ lw=0.8,", | |
| "+ alpha=0.5,", | |
| "+ label=\"SLA Limit\",", | |
| "+ )", | |
| "+ ax1.set_ylabel(\"CPU % / Replicas\\u00d710\")", | |
| " ax1.set_ylim(0, 100)", | |
| " ax1.legend(loc=\"upper right\", fontsize=7, ncol=2)", | |
| "- ax1.set_title(f\"Experiment: {self._scenarios[self._current_scenario_idx].name}\", fontsize=11)", | |
| "+ sc = self._scenarios[self._current_scenario_idx]", | |
| "+ ax1.set_title(", | |
| "+ f\"Experiment: {sc.name}\", fontsize=11", | |
| "+ )", | |
| " ax1.grid(True, alpha=0.2)", | |
| " ", | |
| "- # Bottom chart: replica diff", | |
| "- diff = [m - h for m, h in zip(ml_r, hpa_r)]", | |
| "- colors_bar = [C[\"green\"] if d < 0 else C[\"red\"] if d > 0 else C[\"text_dim\"] for d in diff]", | |
| "- ax2.bar(x, diff, color=colors_bar, alpha=0.7, width=1.0)", | |
| "- ax2.axhline(0, color=C[\"text_dim\"], lw=0.5)", | |
| "- ax2.set_ylabel(\"ML - HPA\")", | |
| "- ax2.set_xlabel(\"Step\")", | |
| "+ # Chart 2: Replica comparison (no multiplier)", | |
| "+ ax2.step(", | |
| "+ x,", | |
| "+ hpa_r,", | |
| "+ color=C[\"red\"],", | |
| "+ lw=1.5,", | |
| "+ where=\"post\",", | |
| "+ label=\"HPA\",", | |
| "+ )", | |
| "+ ax2.step(", | |
| "+ x,", | |
| "+ ml_r,", | |
| "+ color=C[\"blue\"],", | |
| "+ lw=2,", | |
| "+ where=\"post\",", | |
| "+ label=\"ML\",", | |
| "+ )", | |
| "+ ax2.set_ylabel(\"Replicas\")", | |
| "+ ax2.legend(loc=\"upper right\", fontsize=7)", | |
| " ax2.grid(True, alpha=0.2)", | |
| "- ax2.set_title(\"Replica Difference (negative = ML uses fewer)\", fontsize=9)", | |
| "+ ax2.set_title(\"Replica Count Comparison\", fontsize=9)", | |
| " ", | |
| "+ # Chart 3: Replica diff", | |
| "+ diff = [m - h for m, h in zip(ml_r, hpa_r)]", | |
| "+ colors_bar = [", | |
| "+ C[\"green\"]", | |
| "+ if d < 0", | |
| "+ else C[\"red\"]", | |
| "+ if d > 0", | |
| "+ else C[\"text_dim\"]", | |
| "+ for d in diff", | |
| "+ ]", | |
| "+ ax3.bar(x, diff, color=colors_bar, alpha=0.7, width=1.0)", | |
| "+ ax3.axhline(0, color=C[\"text_dim\"], lw=0.5)", | |
| "+ ax3.set_ylabel(\"ML - HPA\")", | |
| "+ ax3.set_xlabel(\"Step\")", | |
| "+ ax3.grid(True, alpha=0.2)", | |
| "+ ax3.set_title(", | |
| "+ \"Replica Difference (negative = ML uses fewer)\",", | |
| "+ fontsize=9,", | |
| "+ )", | |
| "+", | |
| " self._exp_fig.tight_layout()", | |
| " self._exp_canvas.draw_idle()", | |
| " ", | |
| " # KPI cards", | |
| "- self._kpi_cards[0].set_values(f\"{metrics.hpa_avg_replicas:.1f}\", f\"{metrics.ml_avg_replicas:.1f}\")", | |
| "- self._kpi_cards[1].set_values(f\"{metrics.hpa_replica_minutes:.0f}\", f\"{metrics.ml_replica_minutes:.0f}\")", | |
| "- self._kpi_cards[2].set_values(f\"{metrics.hpa_sla_violations}\", f\"{metrics.ml_sla_violations}\")", | |
| "- self._kpi_cards[3].set_values(f\"{metrics.hpa_scaling_events}\", f\"{metrics.ml_scaling_events}\")", | |
| "- self._kpi_cards[4].set_values(f\"{metrics.hpa_over_provisioning_pct:.1f}%\", f\"{metrics.ml_over_provisioning_pct:.1f}%\")", | |
| "- self._kpi_cards[5].set_values(f\"{metrics.hpa_under_provisioning_pct:.1f}%\", f\"{metrics.ml_under_provisioning_pct:.1f}%\")", | |
| "- self._kpi_cards[6].set_values(f\"{metrics.hpa_avg_reaction_time:.1f}\", f\"{metrics.ml_avg_reaction_time:.1f}\")", | |
| "- self._kpi_cards[7].set_values(f\"—\", f\"{metrics.cost_savings_pct:+.1f}%\")", | |
| "+ kpi_data = [", | |
| "+ (", | |
| "+ f\"{metrics.hpa_avg_replicas:.1f}\",", | |
| "+ f\"{metrics.ml_avg_replicas:.1f}\",", | |
| "+ ),", | |
| "+ (", | |
| "+ f\"{metrics.hpa_replica_minutes:.0f}\",", | |
| "+ f\"{metrics.ml_replica_minutes:.0f}\",", | |
| "+ ),", | |
| "+ (", | |
| "+ f\"{metrics.hpa_sla_violations}\",", | |
| "+ f\"{metrics.ml_sla_violations}\",", | |
| "+ ),", | |
| "+ (", | |
| "+ f\"{metrics.hpa_scaling_events}\",", | |
| "+ f\"{metrics.ml_scaling_events}\",", | |
| "+ ),", | |
| "+ (", | |
| "+ f\"{metrics.hpa_over_provisioning_pct:.1f}%\",", | |
| "+ f\"{metrics.ml_over_provisioning_pct:.1f}%\",", | |
| "+ ),", | |
| "+ (", | |
| "+ f\"{metrics.hpa_under_provisioning_pct:.1f}%\",", | |
| "+ f\"{metrics.ml_under_provisioning_pct:.1f}%\",", | |
| "+ ),", | |
| "+ (", | |
| "+ f\"{metrics.hpa_avg_reaction_time:.1f}\",", | |
| "+ f\"{metrics.ml_avg_reaction_time:.1f}\",", | |
| "+ ),", | |
| "+ (", | |
| "+ \"\\u2014\",", | |
| "+ f\"{metrics.cost_savings_pct:+.1f}%\",", | |
| "+ ),", | |
| "+ ]", | |
| "+ for i, (hpa_val, ml_val) in enumerate(kpi_data):", | |
| "+ self._kpi_cards[i].set_values(", | |
| "+ hpa_val, ml_val, better=self._kpi_better[i]", | |
| "+ )", | |
| " ", | |
| "+ def _update_log_tab(self) -> None:", | |
| "+ \"\"\"Update the activity log / notification feed tab.\"\"\"", | |
| "+ # Clear and rebuild (only last 30 for performance)", | |
| "+ for widget in self._notif_frame.winfo_children():", | |
| "+ widget.destroy()", | |
| "+ for text, level in self._notifications[-30:]:", | |
| "+ NotificationItem(self._notif_frame, text, level).pack(", | |
| "+ fill=\"x\", padx=4, pady=1", | |
| "+ )", | |
| " ", | |
| "+", | |
| " # ---------------------------------------------------------------------------", | |
| " # Entry point", | |
| " # ---------------------------------------------------------------------------", | |
| " ", | |
| " ", | |
| "-def main():", | |
| "+def main() -> None:", | |
| "+ \"\"\"Launch the experiment dashboard application.\"\"\"", | |
| " app = ExperimentApp()", | |
| " app.mainloop()", | |
| " " | |
| ] | |
| } | |
| ], | |
| "originalFile": "\"\"\"ML Autoscaler — Experiment Dashboard (CustomTkinter).\n\nInteractive visualization of:\n - Neural network internals (buffer, features, ONNX inference)\n - Scaling algorithm 9-step pipeline\n - HPA vs ML autoscaler comparison\n\nUsage:\n python scripts/experiment_gui.py\n\"\"\"\n\nfrom __future__ import annotations\n\nimport math\nimport sys\nfrom pathlib import Path\n\nimport customtkinter as ctk\nimport matplotlib\nimport numpy as np\n\nmatplotlib.use(\"TkAgg\")\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\nfrom matplotlib.figure import Figure\n\n# Add project root to path for imports\nROOT = Path(__file__).resolve().parent.parent\nsys.path.insert(0, str(ROOT / \"src\"))\nsys.path.insert(0, str(ROOT))\n\nfrom scripts.experiment_scenarios import get_all_scenarios, Scenario\nfrom scripts.experiment_simulator import (\n ExperimentRunner,\n HPAConfig,\n MLAutoscalerSimulator,\n MLConfig,\n StepRecord,\n compute_metrics,\n)\n\n# ---------------------------------------------------------------------------\n# Color palette (unified with Grafana dashboards)\n# ---------------------------------------------------------------------------\n\nC = {\n \"bg\": \"#1a1a2e\",\n \"bg_card\": \"#16213e\",\n \"sidebar\": \"#0f3460\",\n \"accent\": \"#e94560\",\n \"green\": \"#73BF69\",\n \"yellow\": \"#FADE2A\",\n \"orange\": \"#FF9830\",\n \"red\": \"#F2495C\",\n \"blue\": \"#5794F2\",\n \"purple\": \"#B877D9\",\n \"text\": \"#e0e0e0\",\n \"text_dim\": \"#8892b0\",\n \"grid\": \"#2a2a4a\",\n}\n\n# Matplotlib dark style\nplt.rcParams.update({\n \"figure.facecolor\": C[\"bg_card\"],\n \"axes.facecolor\": C[\"bg\"],\n \"axes.edgecolor\": C[\"grid\"],\n \"axes.labelcolor\": C[\"text\"],\n \"text.color\": C[\"text\"],\n \"xtick.color\": C[\"text_dim\"],\n \"ytick.color\": C[\"text_dim\"],\n \"grid.color\": C[\"grid\"],\n \"grid.alpha\": 0.3,\n \"font.size\": 9,\n \"legend.facecolor\": C[\"bg_card\"],\n \"legend.edgecolor\": C[\"grid\"],\n})\n\n\n# ---------------------------------------------------------------------------\n# Utility: embed a matplotlib figure in a CTkFrame\n# ---------------------------------------------------------------------------\n\n\ndef embed_figure(parent: ctk.CTkFrame, fig: Figure) -> FigureCanvasTkAgg:\n canvas = FigureCanvasTkAgg(fig, master=parent)\n canvas.draw()\n canvas.get_tk_widget().pack(fill=\"both\", expand=True)\n return canvas\n\n\n# ---------------------------------------------------------------------------\n# KPI Card widget\n# ---------------------------------------------------------------------------\n\n\nclass KPICard(ctk.CTkFrame):\n \"\"\"Compact KPI card: title + HPA value + ML value.\"\"\"\n\n def __init__(self, master, title: str, **kw):\n super().__init__(master, fg_color=C[\"bg_card\"], corner_radius=8, **kw)\n self._title_lbl = ctk.CTkLabel(\n self, text=title, font=(\"Consolas\", 11), text_color=C[\"text_dim\"]\n )\n self._title_lbl.pack(pady=(6, 2))\n\n row = ctk.CTkFrame(self, fg_color=\"transparent\")\n row.pack(pady=(0, 6))\n self._hpa_lbl = ctk.CTkLabel(\n row, text=\"HPA: --\", font=(\"Consolas\", 13, \"bold\"), text_color=C[\"red\"]\n )\n self._hpa_lbl.pack(side=\"left\", padx=6)\n self._ml_lbl = ctk.CTkLabel(\n row, text=\"ML: --\", font=(\"Consolas\", 13, \"bold\"), text_color=C[\"blue\"]\n )\n self._ml_lbl.pack(side=\"left\", padx=6)\n\n def set_values(self, hpa_val: str, ml_val: str):\n self._hpa_lbl.configure(text=f\"HPA: {hpa_val}\")\n self._ml_lbl.configure(text=f\"ML: {ml_val}\")\n\n\n# ---------------------------------------------------------------------------\n# Pipeline Step block\n# ---------------------------------------------------------------------------\n\n\nclass PipelineBlock(ctk.CTkFrame):\n \"\"\"One step in the 9-step scaling algorithm visualization.\"\"\"\n\n def __init__(self, master, step_num: int, title: str, **kw):\n super().__init__(master, fg_color=C[\"bg_card\"], corner_radius=6,\n border_width=1, border_color=C[\"grid\"], **kw)\n self.configure(width=115, height=80)\n self.pack_propagate(False)\n ctk.CTkLabel(\n self, text=f\"Step {step_num}\", font=(\"Consolas\", 8),\n text_color=C[\"text_dim\"]\n ).pack(pady=(4, 0))\n ctk.CTkLabel(\n self, text=title, font=(\"Consolas\", 9, \"bold\"),\n text_color=C[\"text\"]\n ).pack()\n self._value_lbl = ctk.CTkLabel(\n self, text=\"—\", font=(\"Consolas\", 10), text_color=C[\"text_dim\"],\n wraplength=100\n )\n self._value_lbl.pack(pady=(2, 4))\n\n def update(self, text: str, status: str = \"active\"):\n colors = {\n \"active\": C[\"green\"],\n \"blocked\": C[\"yellow\"],\n \"skipped\": C[\"text_dim\"],\n \"executed\": C[\"accent\"],\n }\n self._value_lbl.configure(text=text, text_color=colors.get(status, C[\"text\"]))\n border = {\n \"active\": C[\"green\"],\n \"blocked\": C[\"yellow\"],\n \"executed\": C[\"accent\"],\n }\n self.configure(border_color=border.get(status, C[\"grid\"]))\n\n\n# ---------------------------------------------------------------------------\n# Main Application\n# ---------------------------------------------------------------------------\n\n\nclass ExperimentApp(ctk.CTk):\n def __init__(self):\n super().__init__()\n self.title(\"ML Autoscaler — Experiment Dashboard\")\n self.geometry(\"1440x920\")\n ctk.set_appearance_mode(\"dark\")\n\n # State\n self._scenarios: list[Scenario] = []\n self._current_scenario_idx = 0\n self._step = 0\n self._playing = False\n self._speed_ms = 200\n self._history: list[StepRecord] = []\n\n # ML simulator state for step-by-step\n self._ml_sim: MLAutoscalerSimulator | None = None\n self._hpa_replicas_trace: list[int] = []\n self._ml_replicas_trace: list[int] = []\n self._actual_trace: list[float] = []\n self._predicted_trace: list[float] = []\n self._smoothed_trace: list[float] = []\n self._decisions_log: list[str] = []\n\n # Build UI\n self._build_controls()\n self._build_sidebar()\n self._build_tabview()\n\n # Load data\n self._load_scenarios()\n\n # -------------------------------------------------------------------\n # Data loading\n # -------------------------------------------------------------------\n\n def _load_scenarios(self):\n try:\n self._scenarios = get_all_scenarios(\n ROOT / \"data\" / \"processed\" / \"test.csv\",\n ROOT / \"data\" / \"processed\" / \"scaler.pkl\",\n ROOT / \"data\" / \"processed\" / \"metadata.json\",\n )\n except Exception as e:\n self._scenarios = [self._synthetic_fallback()]\n self._select_scenario(0)\n\n def _synthetic_fallback(self) -> Scenario:\n \"\"\"Create a simple synthetic scenario if data files are missing.\"\"\"\n n = 200\n rng = np.random.RandomState(42)\n t = np.linspace(0, 4 * np.pi, n)\n actual = 50 + 25 * np.sin(t) + rng.normal(0, 3, n)\n predicted = np.roll(actual, -3)\n predicted[-3:] = actual[-3:]\n predicted += rng.normal(0, 2, n)\n return Scenario(\n name=\"synthetic_demo\",\n description=\"Synthetic sine wave demo\",\n actual_cpu=np.clip(actual, 10, 100),\n predicted_cpu=np.clip(predicted, 10, 100),\n duration_hours=n * 5 / 60,\n )\n\n def _select_scenario(self, idx: int):\n self._current_scenario_idx = idx\n self._reset_simulation()\n self._update_all()\n\n def _reset_simulation(self):\n self._step = 0\n self._playing = False\n sc = self._scenarios[self._current_scenario_idx]\n\n # Run full experiment for Tab 3\n runner = ExperimentRunner(\n sc.actual_cpu, sc.predicted_cpu,\n ml_config=self._build_ml_config(),\n )\n self._history, self._full_metrics = runner.run()\n\n # Reset step-by-step state\n self._ml_sim = MLAutoscalerSimulator(self._build_ml_config())\n self._hpa_replicas_trace = []\n self._ml_replicas_trace = []\n self._actual_trace = []\n self._predicted_trace = []\n self._smoothed_trace = []\n self._decisions_log = []\n\n def _build_ml_config(self) -> MLConfig:\n return MLConfig(\n target_utilization=self._target_var.get(),\n smoothing_alpha=self._alpha_var.get(),\n safety_margin=self._safety_var.get(),\n scale_up_threshold_factor=self._up_thresh_var.get(),\n scale_down_threshold_factor=self._down_thresh_var.get(),\n )\n\n # -------------------------------------------------------------------\n # Control bar (top)\n # -------------------------------------------------------------------\n\n def _build_controls(self):\n bar = ctk.CTkFrame(self, fg_color=C[\"bg_card\"], height=50)\n bar.pack(fill=\"x\", padx=8, pady=(8, 4))\n\n ctk.CTkLabel(\n bar, text=\"ML Autoscaler — Experiment Dashboard\",\n font=(\"Consolas\", 16, \"bold\"), text_color=C[\"text\"]\n ).pack(side=\"left\", padx=12)\n\n # Transport controls\n self._play_btn = ctk.CTkButton(\n bar, text=\"▶ Play\", width=90, fg_color=C[\"green\"],\n text_color=\"#000\", hover_color=\"#5ea554\",\n command=self._toggle_play\n )\n self._play_btn.pack(side=\"left\", padx=4)\n\n ctk.CTkButton(\n bar, text=\"⏭ Step\", width=90, fg_color=C[\"blue\"],\n text_color=\"#000\", hover_color=\"#4678c9\",\n command=self._do_step\n ).pack(side=\"left\", padx=4)\n\n ctk.CTkButton(\n bar, text=\"↺ Reset\", width=90, fg_color=C[\"orange\"],\n text_color=\"#000\", hover_color=\"#d97e24\",\n command=lambda: (self._reset_simulation(), self._update_all())\n ).pack(side=\"left\", padx=4)\n\n # Step counter\n self._step_lbl = ctk.CTkLabel(\n bar, text=\"Step: 0 / 0\", font=(\"Consolas\", 12),\n text_color=C[\"text_dim\"]\n )\n self._step_lbl.pack(side=\"right\", padx=12)\n\n # Speed slider\n ctk.CTkLabel(bar, text=\"Speed:\", text_color=C[\"text_dim\"]).pack(side=\"right\")\n self._speed_slider = ctk.CTkSlider(\n bar, from_=50, to=1000, width=120,\n command=lambda v: setattr(self, \"_speed_ms\", int(v))\n )\n self._speed_slider.set(200)\n self._speed_slider.pack(side=\"right\", padx=4)\n\n def _toggle_play(self):\n self._playing = not self._playing\n self._play_btn.configure(\n text=\"⏸ Pause\" if self._playing else \"▶ Play\",\n fg_color=C[\"yellow\"] if self._playing else C[\"green\"],\n )\n if self._playing:\n self._auto_step()\n\n def _auto_step(self):\n if not self._playing:\n return\n sc = self._scenarios[self._current_scenario_idx]\n if self._step < len(sc.actual_cpu):\n self._do_step()\n self.after(self._speed_ms, self._auto_step)\n else:\n self._playing = False\n self._play_btn.configure(text=\"▶ Play\", fg_color=C[\"green\"])\n\n def _do_step(self):\n sc = self._scenarios[self._current_scenario_idx]\n if self._step >= len(sc.actual_cpu):\n return\n\n t = self._step\n actual = float(sc.actual_cpu[t])\n predicted = float(sc.predicted_cpu[t])\n\n # ML autoscaler step\n ml_replicas, ml_dir = self._ml_sim.step(predicted, t)\n smoothed = self._ml_sim._smoothed or predicted\n\n # HPA (from pre-computed history)\n hpa_replicas = self._history[t].hpa_replicas if t < len(self._history) else 2\n\n self._actual_trace.append(actual)\n self._predicted_trace.append(predicted)\n self._smoothed_trace.append(smoothed)\n self._ml_replicas_trace.append(ml_replicas)\n self._hpa_replicas_trace.append(hpa_replicas)\n self._decisions_log.append(\n f\"[t={t:>3}] {ml_dir:>4} | ML:{ml_replicas} HPA:{hpa_replicas} | \"\n f\"actual={actual:.1f}% pred={predicted:.1f}% smooth={smoothed:.1f}%\"\n )\n\n self._step += 1\n self._update_all()\n\n # -------------------------------------------------------------------\n # Sidebar (parameters)\n # -------------------------------------------------------------------\n\n def _build_sidebar(self):\n sidebar = ctk.CTkFrame(self, fg_color=C[\"sidebar\"], width=220, corner_radius=0)\n sidebar.pack(side=\"left\", fill=\"y\", padx=0, pady=0)\n sidebar.pack_propagate(False)\n\n ctk.CTkLabel(\n sidebar, text=\"Parameters\", font=(\"Consolas\", 14, \"bold\"),\n text_color=C[\"text\"]\n ).pack(pady=(12, 8))\n\n def make_slider(parent, label, from_, to, default, resolution=None):\n ctk.CTkLabel(\n parent, text=label, font=(\"Consolas\", 10), text_color=C[\"text_dim\"]\n ).pack(pady=(8, 0), padx=8, anchor=\"w\")\n var = ctk.DoubleVar(value=default)\n val_lbl = ctk.CTkLabel(\n parent, text=f\"{default}\", font=(\"Consolas\", 10), text_color=C[\"text\"]\n )\n val_lbl.pack(padx=8, anchor=\"e\")\n\n def on_change(v):\n val_lbl.configure(text=f\"{float(v):.2f}\")\n\n slider = ctk.CTkSlider(\n parent, from_=from_, to=to, variable=var,\n width=180, command=on_change\n )\n slider.pack(padx=8)\n return var\n\n self._target_var = make_slider(sidebar, \"Target Util %\", 20, 90, 60)\n self._alpha_var = make_slider(sidebar, \"EMA Alpha\", 0.1, 1.0, 0.7)\n self._safety_var = make_slider(sidebar, \"Safety Margin\", 0.0, 0.5, 0.15)\n self._up_thresh_var = make_slider(sidebar, \"Up Threshold ×\", 1.0, 1.5, 1.1)\n self._down_thresh_var = make_slider(sidebar, \"Down Threshold ×\", 0.3, 0.9, 0.7)\n\n ctk.CTkButton(\n sidebar, text=\"Apply & Re-run\", fg_color=C[\"accent\"],\n text_color=\"#fff\", hover_color=\"#c73550\",\n command=lambda: (self._reset_simulation(), self._update_all())\n ).pack(pady=16, padx=12, fill=\"x\")\n\n # Scenario selector\n ctk.CTkLabel(\n sidebar, text=\"Scenario\", font=(\"Consolas\", 14, \"bold\"),\n text_color=C[\"text\"]\n ).pack(pady=(16, 4))\n\n self._scenario_btns: list[ctk.CTkButton] = []\n names = [\"Stable\", \"Ramp\", \"Spike\", \"Diurnal\"]\n for i, name in enumerate(names):\n btn = ctk.CTkButton(\n sidebar, text=name, width=180, height=28,\n fg_color=C[\"bg_card\"], hover_color=C[\"blue\"],\n command=lambda idx=i: self._select_scenario(idx)\n )\n btn.pack(pady=2, padx=12)\n self._scenario_btns.append(btn)\n\n # -------------------------------------------------------------------\n # Tab view (3 tabs)\n # -------------------------------------------------------------------\n\n def _build_tabview(self):\n self._tabview = ctk.CTkTabview(self, fg_color=C[\"bg\"])\n self._tabview.pack(fill=\"both\", expand=True, padx=8, pady=(0, 8))\n\n self._tab_nn = self._tabview.add(\"Neural Network\")\n self._tab_algo = self._tabview.add(\"Scaling Algorithm\")\n self._tab_exp = self._tabview.add(\"Experiment\")\n\n self._build_nn_tab()\n self._build_algo_tab()\n self._build_exp_tab()\n\n # --- Tab 1: Neural Network ---\n\n def _build_nn_tab(self):\n tab = self._tab_nn\n\n # Top: pipeline diagram\n pipe_frame = ctk.CTkFrame(tab, fg_color=C[\"bg_card\"], corner_radius=8, height=90)\n pipe_frame.pack(fill=\"x\", padx=8, pady=4)\n pipe_frame.pack_propagate(False)\n\n ctk.CTkLabel(\n pipe_frame, text=\"ONNX Inference Pipeline\",\n font=(\"Consolas\", 12, \"bold\"), text_color=C[\"text\"]\n ).pack(pady=(6, 4))\n\n blocks_frame = ctk.CTkFrame(pipe_frame, fg_color=\"transparent\")\n blocks_frame.pack()\n stages = [\n (\"Raw Metrics\", \"5 values\", C[\"green\"]),\n (\"→\", \"\", C[\"text_dim\"]),\n (\"Feature Eng.\", \"44 features\", C[\"purple\"]),\n (\"→\", \"\", C[\"text_dim\"]),\n (\"StandardScaler\", \"normalize\", C[\"orange\"]),\n (\"→\", \"\", C[\"text_dim\"]),\n (\"ONNX LSTM\", \"(1,24,44)→(1,1)\", C[\"blue\"]),\n (\"→\", \"\", C[\"text_dim\"]),\n (\"Inverse Scale\", \"→ CPU %\", C[\"green\"]),\n ]\n for title, sub, color in stages:\n if title == \"→\":\n ctk.CTkLabel(\n blocks_frame, text=\"→\", font=(\"Consolas\", 16, \"bold\"),\n text_color=C[\"text_dim\"]\n ).pack(side=\"left\", padx=2)\n else:\n f = ctk.CTkFrame(blocks_frame, fg_color=C[\"bg\"], corner_radius=6,\n border_width=1, border_color=color)\n f.pack(side=\"left\", padx=4)\n ctk.CTkLabel(f, text=title, font=(\"Consolas\", 9, \"bold\"),\n text_color=color).pack(padx=6, pady=(2, 0))\n ctk.CTkLabel(f, text=sub, font=(\"Consolas\", 8),\n text_color=C[\"text_dim\"]).pack(padx=6, pady=(0, 2))\n\n # Middle: prediction output + buffer\n mid = ctk.CTkFrame(tab, fg_color=\"transparent\")\n mid.pack(fill=\"x\", padx=8, pady=4)\n\n # Buffer status\n buf_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8, width=250)\n buf_frame.pack(side=\"left\", padx=(0, 8), fill=\"y\")\n buf_frame.pack_propagate(False)\n ctk.CTkLabel(buf_frame, text=\"Buffer Status\", font=(\"Consolas\", 11, \"bold\"),\n text_color=C[\"text\"]).pack(pady=(8, 4))\n self._buf_progress = ctk.CTkProgressBar(buf_frame, width=200)\n self._buf_progress.pack(pady=4, padx=12)\n self._buf_progress.set(0)\n self._buf_lbl = ctk.CTkLabel(\n buf_frame, text=\"0 / 24 NOT READY\", font=(\"Consolas\", 10),\n text_color=C[\"red\"]\n )\n self._buf_lbl.pack()\n\n # Prediction big number\n pred_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8, width=200)\n pred_frame.pack(side=\"left\", padx=8, fill=\"y\")\n pred_frame.pack_propagate(False)\n ctk.CTkLabel(pred_frame, text=\"ML Prediction\", font=(\"Consolas\", 11, \"bold\"),\n text_color=C[\"text\"]).pack(pady=(8, 2))\n self._pred_big = ctk.CTkLabel(\n pred_frame, text=\"—\", font=(\"Consolas\", 28, \"bold\"),\n text_color=C[\"blue\"]\n )\n self._pred_big.pack(pady=4)\n self._pred_actual = ctk.CTkLabel(\n pred_frame, text=\"Actual: —\", font=(\"Consolas\", 10),\n text_color=C[\"text_dim\"]\n )\n self._pred_actual.pack()\n\n # Raw metrics panel\n raw_frame = ctk.CTkFrame(mid, fg_color=C[\"bg_card\"], corner_radius=8)\n raw_frame.pack(side=\"left\", fill=\"both\", expand=True, padx=8)\n ctk.CTkLabel(raw_frame, text=\"Current Metrics\", font=(\"Consolas\", 11, \"bold\"),\n text_color=C[\"text\"]).pack(pady=(8, 4))\n self._raw_labels: dict[str, ctk.CTkLabel] = {}\n for metric in [\"cpu_usage\", \"mem_util\", \"net_in\", \"net_out\", \"disk_io\"]:\n lbl = ctk.CTkLabel(raw_frame, text=f\"{metric}: —\",\n font=(\"Consolas\", 10), text_color=C[\"text_dim\"])\n lbl.pack(anchor=\"w\", padx=12)\n self._raw_labels[metric] = lbl\n\n # Bottom: prediction history chart\n self._nn_fig, self._nn_ax = plt.subplots(1, 1, figsize=(10, 3), dpi=80)\n self._nn_canvas = embed_figure(tab, self._nn_fig)\n\n # --- Tab 2: Scaling Algorithm ---\n\n def _build_algo_tab(self):\n tab = self._tab_algo\n\n # 9-step pipeline\n pipe = ctk.CTkFrame(tab, fg_color=\"transparent\")\n pipe.pack(fill=\"x\", padx=4, pady=4)\n\n step_names = [\n \"EMA\", \"Hysteresis\", \"Desired\", \"Clamp\",\n \"Rate Limit\", \"Cooldown\", \"Stabilize\", \"No Change?\", \"Commit\"\n ]\n self._algo_blocks: list[PipelineBlock] = []\n for i, name in enumerate(step_names):\n if i > 0:\n ctk.CTkLabel(pipe, text=\"→\", font=(\"Consolas\", 14, \"bold\"),\n text_color=C[\"text_dim\"]).pack(side=\"left\", padx=1)\n block = PipelineBlock(pipe, i + 1, name)\n block.pack(side=\"left\", padx=2)\n self._algo_blocks.append(block)\n\n # State panel\n state_frame = ctk.CTkFrame(tab, fg_color=C[\"bg_card\"], corner_radius=8, height=40)\n state_frame.pack(fill=\"x\", padx=8, pady=4)\n self._state_lbl = ctk.CTkLabel(\n state_frame,\n text=\"Cooldown UP: — | Cooldown DOWN: — | Consec. Below: — | Direction: —\",\n font=(\"Consolas\", 10), text_color=C[\"text_dim\"]\n )\n self._state_lbl.pack(pady=6)\n\n # Charts: CPU + Replicas side by side\n charts = ctk.CTkFrame(tab, fg_color=\"transparent\")\n charts.pack(fill=\"both\", expand=True, padx=4, pady=4)\n\n left = ctk.CTkFrame(charts, fg_color=\"transparent\")\n left.pack(side=\"left\", fill=\"both\", expand=True)\n self._algo_cpu_fig, self._algo_cpu_ax = plt.subplots(1, 1, figsize=(5.5, 3), dpi=80)\n self._algo_cpu_canvas = embed_figure(left, self._algo_cpu_fig)\n\n right = ctk.CTkFrame(charts, fg_color=\"transparent\")\n right.pack(side=\"left\", fill=\"both\", expand=True)\n self._algo_rep_fig, self._algo_rep_ax = plt.subplots(1, 1, figsize=(5.5, 3), dpi=80)\n self._algo_rep_canvas = embed_figure(right, self._algo_rep_fig)\n\n # Decision log\n self._decision_log = ctk.CTkTextbox(\n tab, height=100, font=(\"Consolas\", 9), fg_color=C[\"bg\"],\n text_color=C[\"text_dim\"]\n )\n self._decision_log.pack(fill=\"x\", padx=8, pady=(0, 4))\n\n # --- Tab 3: Experiment Comparison ---\n\n def _build_exp_tab(self):\n tab = self._tab_exp\n\n # Main comparison chart\n self._exp_fig, self._exp_axes = plt.subplots(2, 1, figsize=(10, 5), dpi=80,\n gridspec_kw={\"height_ratios\": [2, 1]})\n self._exp_canvas = embed_figure(tab, self._exp_fig)\n\n # KPI cards grid\n cards_frame = ctk.CTkFrame(tab, fg_color=\"transparent\")\n cards_frame.pack(fill=\"x\", padx=8, pady=4)\n\n kpi_names = [\n \"Avg Replicas\", \"Replica-Min\", \"SLA Violations\", \"Scaling Events\",\n \"Over-Prov %\", \"Under-Prov %\", \"Reaction Time\", \"Cost Savings %\"\n ]\n self._kpi_cards: list[KPICard] = []\n for i, name in enumerate(kpi_names):\n card = KPICard(cards_frame, name)\n card.grid(row=i // 4, column=i % 4, padx=4, pady=4, sticky=\"nsew\")\n self._kpi_cards.append(card)\n for c in range(4):\n cards_frame.columnconfigure(c, weight=1)\n\n # -------------------------------------------------------------------\n # Update all visuals\n # -------------------------------------------------------------------\n\n def _update_all(self):\n sc = self._scenarios[self._current_scenario_idx]\n total = len(sc.actual_cpu)\n self._step_lbl.configure(text=f\"Step: {self._step} / {total}\")\n\n # Highlight active scenario button\n for i, btn in enumerate(self._scenario_btns):\n btn.configure(\n fg_color=C[\"blue\"] if i == self._current_scenario_idx else C[\"bg_card\"]\n )\n\n self._update_nn_tab()\n self._update_algo_tab()\n self._update_exp_tab()\n\n def _update_nn_tab(self):\n n = self._step\n\n # Buffer status\n buf_fill = min(n, 24)\n self._buf_progress.set(buf_fill / 24)\n ready = n >= 24\n self._buf_lbl.configure(\n text=f\"{min(n, 54)} / 24 {'READY' if ready else 'FILLING...'}\",\n text_color=C[\"green\"] if ready else C[\"red\"]\n )\n\n # Current prediction\n if n > 0:\n pred = self._predicted_trace[-1]\n actual = self._actual_trace[-1]\n color = C[\"green\"] if pred < 42 else C[\"yellow\"] if pred < 66 else C[\"orange\"] if pred < 85 else C[\"red\"]\n self._pred_big.configure(text=f\"{pred:.1f}%\", text_color=color)\n self._pred_actual.configure(text=f\"Actual: {actual:.1f}%\")\n\n # Raw metrics (simulated from CPU)\n self._raw_labels[\"cpu_usage\"].configure(text=f\"cpu_usage: {actual:.1f}%\")\n self._raw_labels[\"mem_util\"].configure(text=f\"mem_util: {actual * 0.6:.1f}%\")\n self._raw_labels[\"net_in\"].configure(text=f\"net_in: {actual * 1.2:.0f} KB/s\")\n self._raw_labels[\"net_out\"].configure(text=f\"net_out: {actual * 0.8:.0f} KB/s\")\n self._raw_labels[\"disk_io\"].configure(text=f\"disk_io: {actual * 0.3:.1f}%\")\n\n # Prediction history chart\n self._nn_ax.clear()\n if n > 1:\n x = range(max(0, n - 80), n)\n a_slice = self._actual_trace[max(0, n - 80):]\n p_slice = self._predicted_trace[max(0, n - 80):]\n self._nn_ax.plot(x, a_slice, color=C[\"green\"], lw=1.5, label=\"Actual CPU\")\n self._nn_ax.plot(x, p_slice, color=C[\"purple\"], lw=1.5, ls=\"--\", label=\"Predicted\")\n self._nn_ax.axhline(60, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.5)\n self._nn_ax.set_ylabel(\"CPU %\")\n self._nn_ax.set_ylim(0, 100)\n self._nn_ax.legend(loc=\"upper right\", fontsize=8)\n self._nn_ax.grid(True, alpha=0.2)\n self._nn_ax.set_title(\"Prediction History (last 80 steps)\", fontsize=10)\n self._nn_fig.tight_layout()\n self._nn_canvas.draw_idle()\n\n def _update_algo_tab(self):\n n = self._step\n if n == 0:\n for block in self._algo_blocks:\n block.update(\"—\", \"skipped\")\n return\n\n # Get current step details\n pred = self._predicted_trace[-1]\n smoothed = self._smoothed_trace[-1]\n ml_replicas = self._ml_replicas_trace[-1]\n prev_replicas = self._ml_replicas_trace[-2] if n > 1 else 2\n\n target = self._target_var.get()\n up_thresh = target * self._up_thresh_var.get()\n down_thresh = target * self._down_thresh_var.get()\n\n # Determine direction\n if smoothed > up_thresh:\n direction = \"UP\"\n elif smoothed < down_thresh:\n direction = \"DOWN\"\n else:\n direction = \"HOLD\"\n\n # Update blocks\n self._algo_blocks[0].update(f\"{pred:.1f}→{smoothed:.1f}%\", \"active\")\n self._algo_blocks[1].update(\n f\"{smoothed:.0f}% {'>' if direction == 'UP' else '<' if direction == 'DOWN' else 'in'} \"\n f\"[{down_thresh:.0f},{up_thresh:.0f}]\\n→{direction}\",\n \"active\" if direction != \"HOLD\" else \"blocked\"\n )\n\n if direction == \"HOLD\":\n for i in range(2, 9):\n self._algo_blocks[i].update(\"—\", \"skipped\")\n else:\n raw_desired = math.ceil(prev_replicas * smoothed / target)\n if direction == \"UP\":\n raw_desired = math.ceil(raw_desired * (1 + self._safety_var.get()))\n self._algo_blocks[2].update(f\"{prev_replicas}×{smoothed:.0f}/{target:.0f}\\n={raw_desired}\", \"active\")\n clamped = max(2, min(8, raw_desired))\n self._algo_blocks[3].update(f\"[2,8]→{clamped}\", \"active\")\n rate_lim = min(clamped, prev_replicas + 2) if direction == \"UP\" else max(clamped, prev_replicas - 1)\n self._algo_blocks[4].update(f\"±limit→{rate_lim}\", \"active\")\n self._algo_blocks[5].update(\"OFF ✓\", \"active\")\n self._algo_blocks[6].update(\"N/A\" if direction == \"UP\" else \"check\", \"active\")\n\n executed = ml_replicas != prev_replicas\n self._algo_blocks[7].update(\n f\"{ml_replicas}{'≠' if executed else '='}{prev_replicas}\",\n \"active\" if executed else \"blocked\"\n )\n if executed:\n self._algo_blocks[8].update(f\"{prev_replicas}→{ml_replicas}\", \"executed\")\n else:\n self._algo_blocks[8].update(\"hold\", \"blocked\")\n\n # State panel\n sim = self._ml_sim\n self._state_lbl.configure(\n text=f\"Direction: {direction} | \"\n f\"Consec. Below: {sim._consecutive_below} | \"\n f\"Replicas: {ml_replicas} | \"\n f\"Smoothed: {smoothed:.1f}%\"\n )\n\n # CPU chart\n self._algo_cpu_ax.clear()\n if n > 1:\n x = range(n)\n self._algo_cpu_ax.plot(x, self._smoothed_trace, color=C[\"blue\"], lw=1.5, label=\"Smoothed\")\n self._algo_cpu_ax.plot(x, self._actual_trace, color=C[\"green\"], lw=1, alpha=0.5, label=\"Actual\")\n self._algo_cpu_ax.axhline(up_thresh, color=C[\"red\"], ls=\"--\", lw=0.8, label=f\"Up {up_thresh:.0f}%\")\n self._algo_cpu_ax.axhline(down_thresh, color=C[\"orange\"], ls=\"--\", lw=0.8, label=f\"Down {down_thresh:.0f}%\")\n self._algo_cpu_ax.axhline(target, color=C[\"yellow\"], ls=\":\", lw=0.8, alpha=0.6)\n self._algo_cpu_ax.axhspan(down_thresh, up_thresh, alpha=0.08, color=C[\"yellow\"])\n self._algo_cpu_ax.set_ylim(0, 100)\n self._algo_cpu_ax.set_ylabel(\"CPU %\")\n self._algo_cpu_ax.legend(loc=\"upper right\", fontsize=7)\n self._algo_cpu_ax.grid(True, alpha=0.2)\n self._algo_cpu_ax.set_title(\"CPU + Thresholds\", fontsize=10)\n self._algo_cpu_fig.tight_layout()\n self._algo_cpu_canvas.draw_idle()\n\n # Replica chart\n self._algo_rep_ax.clear()\n if n > 1:\n x = range(n)\n self._algo_rep_ax.step(x, self._ml_replicas_trace, color=C[\"blue\"], lw=2, where=\"post\", label=\"ML Replicas\")\n self._algo_rep_ax.step(x, self._hpa_replicas_trace, color=C[\"red\"], lw=1.5, where=\"post\", alpha=0.6, label=\"HPA Replicas\")\n self._algo_rep_ax.axhline(2, color=C[\"text_dim\"], ls=\":\", lw=0.7, alpha=0.5)\n self._algo_rep_ax.axhline(8, color=C[\"text_dim\"], ls=\":\", lw=0.7, alpha=0.5)\n self._algo_rep_ax.set_ylim(0, 10)\n self._algo_rep_ax.set_ylabel(\"Replicas\")\n self._algo_rep_ax.legend(loc=\"upper right\", fontsize=7)\n self._algo_rep_ax.grid(True, alpha=0.2)\n self._algo_rep_ax.set_title(\"Replica Count\", fontsize=10)\n self._algo_rep_fig.tight_layout()\n self._algo_rep_canvas.draw_idle()\n\n # Decision log\n self._decision_log.configure(state=\"normal\")\n self._decision_log.delete(\"1.0\", \"end\")\n # Show last 20 entries\n for line in self._decisions_log[-20:]:\n self._decision_log.insert(\"end\", line + \"\\n\")\n self._decision_log.configure(state=\"disabled\")\n self._decision_log.see(\"end\")\n\n def _update_exp_tab(self):\n if not hasattr(self, \"_full_metrics\"):\n return\n metrics = self._full_metrics\n history = self._history\n\n # Top chart: Actual CPU + replicas\n ax1, ax2 = self._exp_axes\n ax1.clear()\n ax2.clear()\n\n n = len(history)\n if n > 0:\n x = range(n)\n actual = [r.actual_cpu for r in history]\n hpa_r = [r.hpa_replicas for r in history]\n ml_r = [r.ml_replicas for r in history]\n\n # CPU trace (background)\n ax1.fill_between(x, actual, alpha=0.15, color=C[\"text_dim\"])\n ax1.plot(x, actual, color=C[\"text_dim\"], lw=0.8, alpha=0.5, label=\"Actual CPU\")\n\n # Replicas\n ax1.step(x, [r * 10 for r in hpa_r], color=C[\"red\"], lw=1.5,\n where=\"post\", label=\"HPA Replicas (×10)\")\n ax1.step(x, [r * 10 for r in ml_r], color=C[\"blue\"], lw=2,\n where=\"post\", label=\"ML Replicas (×10)\")\n\n # SLA threshold\n target = self._target_var.get()\n ax1.axhline(target * 1.2, color=C[\"red\"], ls=\":\", lw=0.8, alpha=0.5, label=\"SLA Limit\")\n\n ax1.set_ylabel(\"CPU % / Replicas×10\")\n ax1.set_ylim(0, 100)\n ax1.legend(loc=\"upper right\", fontsize=7, ncol=2)\n ax1.set_title(f\"Experiment: {self._scenarios[self._current_scenario_idx].name}\", fontsize=11)\n ax1.grid(True, alpha=0.2)\n\n # Bottom chart: replica diff\n diff = [m - h for m, h in zip(ml_r, hpa_r)]\n colors_bar = [C[\"green\"] if d < 0 else C[\"red\"] if d > 0 else C[\"text_dim\"] for d in diff]\n ax2.bar(x, diff, color=colors_bar, alpha=0.7, width=1.0)\n ax2.axhline(0, color=C[\"text_dim\"], lw=0.5)\n ax2.set_ylabel(\"ML - HPA\")\n ax2.set_xlabel(\"Step\")\n ax2.grid(True, alpha=0.2)\n ax2.set_title(\"Replica Difference (negative = ML uses fewer)\", fontsize=9)\n\n self._exp_fig.tight_layout()\n self._exp_canvas.draw_idle()\n\n # KPI cards\n self._kpi_cards[0].set_values(f\"{metrics.hpa_avg_replicas:.1f}\", f\"{metrics.ml_avg_replicas:.1f}\")\n self._kpi_cards[1].set_values(f\"{metrics.hpa_replica_minutes:.0f}\", f\"{metrics.ml_replica_minutes:.0f}\")\n self._kpi_cards[2].set_values(f\"{metrics.hpa_sla_violations}\", f\"{metrics.ml_sla_violations}\")\n self._kpi_cards[3].set_values(f\"{metrics.hpa_scaling_events}\", f\"{metrics.ml_scaling_events}\")\n self._kpi_cards[4].set_values(f\"{metrics.hpa_over_provisioning_pct:.1f}%\", f\"{metrics.ml_over_provisioning_pct:.1f}%\")\n self._kpi_cards[5].set_values(f\"{metrics.hpa_under_provisioning_pct:.1f}%\", f\"{metrics.ml_under_provisioning_pct:.1f}%\")\n self._kpi_cards[6].set_values(f\"{metrics.hpa_avg_reaction_time:.1f}\", f\"{metrics.ml_avg_reaction_time:.1f}\")\n self._kpi_cards[7].set_values(f\"—\", f\"{metrics.cost_savings_pct:+.1f}%\")\n\n\n# ---------------------------------------------------------------------------\n# Entry point\n# ---------------------------------------------------------------------------\n\n\ndef main():\n app = ExperimentApp()\n app.mainloop()\n\n\nif __name__ == \"__main__\":\n main()\n" | |
| } | |
| } | |
| [2026-02-07T18:26:11.109Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-6", | |
| "id": "msg_01L9egB37115A744mJoTXYUd", | |
| "type": "message", | |
| "role": "assistant", | |
| "content": [ | |
| { | |
| "type": "text", | |
| "text": "Good. Now I need to remove the unused `time` import and check for any linting issues. Let me also check if there are any issues with the `import` of `HPAConfig` (I removed it from imports but it might not be used anymore)." | |
| } | |
| ], | |
| "stop_reason": null, | |
| "stop_sequence": null, | |
| "usage": { | |
| "input_tokens": 1, | |
| "cache_creation_input_tokens": 45066, | |
| "cache_read_input_tokens": 95072, | |
| "cache_creation": { | |
| "ephemeral_5m_input_tokens": 0, | |
| "ephemeral_1h_input_tokens": 45066 | |
| }, | |
| "output_tokens": 1, | |
| "service_tier": "standard", | |
| "inference_geo": "not_available" | |
| }, | |
| "context_management": null | |
| }, | |
| "parent_tool_use_id": null, | |
| "session_id": "ce1ab353-0721-47e7-a373-84e062534c7f", | |
| "uuid": "4c4ca876-b0f5-402c-89a0-cc4a3db0521a" | |
| } | |
| [2026-02-07T18:26:12.824Z] [INFO] { | |
| "type": "assistant", | |
| "message": { | |
| "model": "claude-opus-4-6", | |
| "id": "msg_01L9egB37115A744mJoTXYUd", | |
| "type": "message", | |
| "role": "assistant", | |
| "content": [ | |
| { | |
| "type": "tool_use", | |
| "id": "toolu_01NFf1L3aZ15y7mWkX4BSwgJ", | |
| "name": "Edit", | |
| "input": { | |
| "replace_all": false, | |
| "file_path": "/tmp/gh-issue-solver-1770487990892/scripts/experiment_gui.py", | |
| "old_string": "import csv\nimport math\nimport sys\nimport time\nfrom pathlib import Path", | |
| "new_string": "import csv\nimport math\nimport sys\nfrom pathlib import Path" | |
| } | |
| } | |
| ], | |
| "stop_reason": null, | |
| "stop_sequence": null, | |
| "usage": { | |
| "input_tokens": 1, | |
| "cache_creation_input_tokens": 45066, | |
| "cache_read_input_tokens": 95072, | |
| "cache_creation": { | |
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| "ephemeral_1h_input_tokens": 45066 | |
| }, | |
| "output_tokens": 1, | |
| "service_tier": "standard", | |
| "inference_geo": "not_available" | |
| }, | |
| "context_management": null | |
| }, | |
| "parent_tool_use_id": null, | |
| "session_id": "ce1ab353-0721-47e7-a373-84e062534c7f", | |
| "uuid": "12acc548-722e-4da6-9a8d-e83193aeca29" | |
| } | |
| [2026-02-07T18:26:12.856Z] [INFO] { | |
| "type": "user", | |
| "message": { | |
| "role": "user", | |
| "content": [ | |
| { | |
| "tool_use_id": "toolu_01NFf1L3aZ15y7mWkX4BSwgJ", | |
| "type": "tool_result", | |
| "content": "The file /tmp/gh-issue-solver-1770487990892/scripts/experiment_gui.py has been updated successfully." | |
| } | |
| ] | |
| }, | |
| "parent_tool_use_id": null, | |
| "session_id": "ce1ab353-0721-47e7-a373-84e062534c7f", | |
| "uuid": "784695a9-3711-44c8-a7eb-7c6da8d6f6be", | |
| "tool_use_result": { | |
| "filePath": "/tmp/gh-issue-solver-1770487990892/scripts/experiment_gui.py", | |
| "oldString": "import csv\nimport math\nimport sys\nimport time\nfrom pathlib import Path", | |
| "newString": "import csv\nimport math\nimport sys\nfrom pathlib import Path", |
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