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Interactive Serverless Compute.md · GitHub

https://share.summari.com/interactive-serverless-computemd-github?utm_source=Chrome

Overview

• Serverless compute is emerging as an attractive cloud computing model that lets developers focus only on the core applications, building them as small, fine-grained workloads without having to worry about building and/or managing the infrastructure they run on.
• Cloud providers dynamically provision, deploy, patch, and monitor the infrastructure and its resources (e.g., compute, storage, memory, and network) for these workloads; with tenants only paying for the resources they consume at millisecond increments
• They generally put a strict limit on the compute time and resource that can be consumed by a single workload, in order to ensure that they can easily deploy and scale each workload without impacting the availability of other workloads.

Preliminary Results

• To compare the performance of λ−NIC versus existing serverless compute frameworks, we select OpenFaa

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Interactive Serverless Compute on Programmable SmartNICs

Interactive Serverless Compute on Programmable SmartNICs.md · GitHub https://share.summari.com/interactive-serverless-compute-on-programmable-smartnicsmd-github?utm_source=Chrome

ABSTRACT

• Limited con- currency and high latency of server CPUs prohibit many interactive workloads (e.g., web servers and database clients) from taking advantage of serverless compute to achieve high performance.
• Lambdas are an open-source framework that runs serverless workloads directly on a SmartNIC, an ASIC-based NIC that consists of a dense grid of NPU cores, enabling up to 880x and 736x improvements in response latency and throughput, respectively.

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Interactive Serverless Compute.md · GitHub

https://share.summari.com/interactive-serverless-computemd-github?utm_source=Chrome

Overview

• Serverless compute is emerging as an attractive cloud computing model that lets developers focus only on the core applications, building them as small, fine-grained workloads without having to worry about building and/or managing the infrastructure they run on.
• Cloud providers dynamically provision, deploy, patch, and monitor the infrastructure and its resources (e.g., compute, storage, memory, and network) for these workloads; with tenants only paying for the resources they consume at millisecond increments
• They generally put a strict limit on the compute time and resource that can be consumed by a single workload, in order to ensure that they can easily deploy and scale each workload without impacting the availability of other workloads.

Preliminary Results

• To compare the performance of λ−NIC versus existing serverless compute frameworks, we select OpenFaa

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Robot Operating System

Overview

Event-based software design is an integral part of hardware-software codesign. ICs,ASICs, microcontrollers, microprocessors—all of them rely on event-based processes. Even pure hardware is designed in ways where signal changes(events) trigger a different part of the circuitry.

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https://ieeexplore.ieee.org/abstract/document/5183468

Network virtualization: state of the art and research challenges

Clip source: Summary of - Network virtualization—2009.md · GitHub

Network virtualization—2009.md · GitHub

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https://ieeexplore.ieee.org/abstract/document/5183468

Network virtualization: state of the art and research challenges

Clip source: Summary of - Network virtualization—2009.md · GitHub

Network virtualization—2009.md · GitHub

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DefRec: Establishing Physical Function Virtualization to Disrupt Reconnaissance of Power Grids’ Cyber-Physical Infrastructures

Hui Lin Jianing Zhuang—University of Nevada
Yih-Chun Hu—University of Illinois
Huayu Zhou—University of Nevada, Reno hzhou@nevada.unr.edu

Clip source: Summary of - DefRec.md · GitHub

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Autonomic Network Node System

USOO832O388B
(12) United States Patent (10) Patent No.: US 8,320,388 B
LOuati et al. (45) Date of Patent: Nov. 27, 2012
(54) AUTONOMIC NETWORK NODE SYSTEM 7,649,851 B2 * 1/2010 Takashige et al.…………370, 7,656,872 B2* 2/2010 Yoshimoto et al.……….. 370/ O O 7,761,595 B1* 7/2010 Drennen et al.………….. 709/ (75) Inventors: SE E. SATR): 7,826,386 B2 * 1 1/2010 Beichter et al.……………370/ jamal Zeghlache, Etioles (FR) 7,843,851 B2 * 1 1/2010 Grammel……. 370, 8,014,411 B2 * 9/2011 Kazban et al.. 370/ (73) Assignees: Groupe des Ecoles des 2002/0059427 A1* 5/2002 Tamaki et al.……………. TO9, Telecommunications (GET), Evry (FR): 2005, 0180429 A1 8/2005 Ghahremani et al. Institut National des (Continued) Telecommunications (INT) FOREIGN PATENT DOCUMENTS (*) Notice: Subject to any disclaimer, the term of this EP 1653 688 A1 5. patent is extended or adjusted under 35 U.S.C. 154(b) by 363 days. OTHER PUBLICATIONS Doria et al., “ForCES Protocol Specificaiton.” Network Working (21) Appl. No.: 12/

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DefRec: Establishing Physical Function Virtualization to Disrupt Reconnaissance of Power Grids’ Cyber-Physical Infrastructures

Hui Lin Jianing Zhuang—University of Nevada
Yih-Chun Hu—University of Illinois
Huayu Zhou—University of Nevada, Reno hzhou@nevada.unr.edu

Clip source: Summary of - DefRec.md · GitHub

edecoux

DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation

Yizhe Zhang Siqi Sun Michel Galley Yen-Chun Chen
Chris Brockett Xiang Gao Jianfeng Gao Jingjing Liu Bill Dolan
Microsoft Corporation, Redmond, WA, USA∗

Clip source: Summary of - Large-Scale Generative Pre-training.md · GitHub