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@alendit
Created September 7, 2015 09:12
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{
"cells": [
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "autoreload 2",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "from run_benchmark import benchmark\nfrom plumbum import SshMachine\nimport pandas as pd\nimport numpy as np\nimport seaborn\n%pylab inline",
"execution_count": 2,
"outputs": [
{
"text": "Populating the interactive namespace from numpy and matplotlib\n",
"output_type": "stream",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "from get_ssh_config import get_ssh_config",
"execution_count": 3,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "get_ssh_config()",
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 4,
"metadata": {},
"data": {
"text/plain": "SSHConfig(host='192.168.121.118', user='vagrant', keyfile='/home/alendit/hyper/vagrant/.vagrant/machines/default/libvirt/private_key')"
}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "ssh_config = get_ssh_config()\nvagrant = SshMachine(ssh_config.host, user=ssh_config.user, keyfile=ssh_config.keyfile)",
"execution_count": 12,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "def execute_benchmark(machine=None):\n results = pd.DataFrame(columns=['factor', 'access_latency', 'fault_latency', 'missrate'])\n skipped = 0\n for factor in [.1, .2, .3, .5, .7, 1, 1.5, 2]:\n for iteration in range(100):\n try:\n if machine is None:\n run_results = benchmark(factor)\n else:\n run_results = benchmark(factor, machine)\n except AttributeError as exc:\n skipped += 1\n print(exc)\n print(\"\\r{}\".format(skipped))\n else:\n run_results['factor'] = factor\n\n results = results.append(run_results, ignore_index=True)\n return results\nresults_local = execute_benchmark()\nresults_kvm = execute_benchmark(vagrant)",
"execution_count": 77,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "g_local = results_local.groupby('factor').aggregate(np.average)\ng_kvm = results_kvm.groupby('factor').aggregate(np.average)",
"execution_count": 78,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "new = pd.Series(index=np.linspace(0, 2, 50 * 10), name='new')",
"execution_count": 79,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "new_l_l = new.append(g_local['access_latency'])\nnew_l_l.name = 'Local'\nnew_l_k = new.append(g_kvm['access_latency'])\nnew_l_k.name = 'KVM'\nfig = figure()\nnew_l_l.sort_index().interpolate(method='cubic').plot(figure=fig, legend=True)\nnew_l_k.sort_index().interpolate(method='cubic').plot(figure=fig, legend=True)",
"execution_count": 80,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 80,
"metadata": {},
"data": {
"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fd61cbf3668>"
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7fd61cb96e48>",
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tdbuxhUWyNHVhsMsRQgSJBH0I216/hwGvk2szV2AymIJdjhAiSCToQ5TL62Jr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\ngg==\n"
}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "new_l_l = new.append(g_local['fault_latency'])\nnew_l_l.name = 'Local'\nnew_l_k = new.append(g_kvm['fault_latency'])\nnew_l_k.name = 'KVM'\nfig = figure()\nnew_l_l.sort_index().interpolate(method='cubic').plot(figure=fig, legend=True)\nnew_l_k.sort_index().interpolate(method='cubic').plot(figure=fig, legend=True)",
"execution_count": 81,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 81,
"metadata": {},
"data": {
"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fd61cabfef0>"
}
},
{
"output_type": "display_data",
"metadata": {},
"data": {
"text/plain": "<matplotlib.figure.Figure at 0x7fd61ce7bdd8>",
"image/png": 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QHMd9WXeTEpbk7bDGtKXJ17G36iD7qw+xImUxluBPLyzlKaoGoCjKNdM0jX1Vh3gi/ylq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\nsDs0JiWFc8/qTPVg1zjlhwnAOR302fI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}
}
]
}
],
"metadata": {
"notify_time": "10",
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
"language_info": {
"pygments_lexer": "ipython3",
"version": "3.4.3",
"name": "python",
"nbconvert_exporter": "python",
"file_extension": ".py",
"codemirror_mode": {
"version": 3,
"name": "ipython"
},
"mimetype": "text/x-python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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