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hoernschen 2020-11-27 05:35:06 +01:00
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commit a336f74ce8
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MeasurementEvaluator.ipynb
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@ -2,21 +2,9 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"output_type": "error",
"ename": "ModuleNotFoundError",
"evalue": "No module named 'pandas'",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-c01ef916f33f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpathlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_objects\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mgo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpress\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas'"
]
}
],
"source": [ "source": [
"import os\n", "import os\n",
"import time\n", "import time\n",
@ -33,7 +21,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -59,7 +47,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -87,7 +75,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -123,22 +111,22 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"def cleanDataIQR(data):\n", "def cleanDataIQR(data, factor):\n",
" Q1=data.quantile(0.25)\n", " Q1=data.quantile(0.25)\n",
" Q3=data.quantile(0.75)\n", " Q3=data.quantile(0.75)\n",
" IQR=Q3-Q1\n", " IQR=Q3-Q1\n",
" lowqe_bound=Q1 - 10 * IQR\n", " lowqe_bound=Q1 - factor * IQR\n",
" upper_bound=Q3 + 10 * IQR\n", " upper_bound=Q3 + factor * IQR\n",
" return data[~((data < lowqe_bound) |(data > upper_bound))]" " return data[~((data < lowqe_bound) |(data > upper_bound))]"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -171,7 +159,7 @@
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}, },
"metadata": {}, "metadata": {},
"execution_count": 5 "execution_count": 6
} }
], ],
"source": [ "source": [
@ -194,7 +182,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 7,
"metadata": {}, "metadata": {},
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{ {
@ -253,7 +241,7 @@
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<th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>14.10.20 00:00:01</th>\n <td>0.076</td>\n <td>0.009</td>\n <td>0.148</td>\n <td>-0.117</td>\n <td>-0.196</td>\n <td>0.037</td>\n <td>0.052</td>\n <td>-0.075</td>\n <td>0.207</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>14.10.20 00:00:02</th>\n <td>0.100</td>\n <td>0.021</td>\n <td>0.178</td>\n <td>-0.068</td>\n <td>-0.209</td>\n <td>0.007</td>\n <td>0.030</td>\n <td>-0.132</td>\n <td>0.176</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>14.10.20 00:00:03</th>\n <td>0.090</td>\n <td>0.002</td>\n <td>0.186</td>\n <td>0.457</td>\n <td>-0.222</td>\n <td>2.782</td>\n <td>0.027</td>\n <td>-0.130</td>\n <td>0.182</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>14.10.20 00:00:04</th>\n <td>0.073</td>\n <td>-0.018</td>\n <td>0.131</td>\n <td>-0.085</td>\n <td>-0.169</td>\n <td>0.019</td>\n <td>0.055</td>\n <td>-0.066</td>\n <td>0.186</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>14.10.20 00:00:05</th>\n <td>0.106</td>\n <td>0.029</td>\n <td>0.169</td>\n <td>-0.100</td>\n <td>-0.223</td>\n <td>0.027</td>\n <td>0.005</td>\n <td>-0.139</td>\n <td>0.176</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 27 columns</p>\n</div>" "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>AVG[W] L1</th>\n <th>Min[W] L1</th>\n <th>Max[W] L1</th>\n <th>AVG[W] L2</th>\n <th>Min[W] L2</th>\n <th>Max[W] L2</th>\n <th>AVG[W] L3</th>\n <th>Min[W] L3</th>\n <th>Max[W] L3</th>\n <th>CPU% L1</th>\n <th>...</th>\n <th>KB Transmitted L2</th>\n <th>KB Received L2</th>\n <th>KB Read L2</th>\n <th>KB Write L2</th>\n <th>CPU% L3</th>\n <th>MEM Used L3</th>\n <th>KB Transmitted L3</th>\n <th>KB Received L3</th>\n <th>KB Read L3</th>\n <th>KB Write L3</th>\n </tr>\n <tr>\n <th>Time</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>14.10.20 00:00:01</th>\n <td>0.076</td>\n <td>0.009</td>\n <td>0.148</td>\n <td>-0.117</td>\n <td>-0.196</td>\n <td>0.037</td>\n <td>0.052</td>\n <td>-0.075</td>\n <td>0.207</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>14.10.20 00:00:02</th>\n <td>0.100</td>\n <td>0.021</td>\n <td>0.178</td>\n <td>-0.068</td>\n <td>-0.209</td>\n <td>0.007</td>\n <td>0.030</td>\n <td>-0.132</td>\n <td>0.176</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>14.10.20 00:00:03</th>\n <td>0.090</td>\n <td>0.002</td>\n <td>0.186</td>\n <td>0.457</td>\n <td>-0.222</td>\n <td>2.782</td>\n <td>0.027</td>\n <td>-0.130</td>\n <td>0.182</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>14.10.20 00:00:04</th>\n <td>0.073</td>\n <td>-0.018</td>\n <td>0.131</td>\n <td>-0.085</td>\n <td>-0.169</td>\n <td>0.019</td>\n <td>0.055</td>\n <td>-0.066</td>\n <td>0.186</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>14.10.20 00:00:05</th>\n <td>0.106</td>\n <td>0.029</td>\n <td>0.169</td>\n <td>-0.100</td>\n <td>-0.223</td>\n <td>0.027</td>\n <td>0.005</td>\n <td>-0.139</td>\n <td>0.176</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 27 columns</p>\n</div>"
}, },
"metadata": {}, "metadata": {},
"execution_count": 6 "execution_count": 7
} }
], ],
"source": [ "source": [
@ -280,7 +268,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -306,7 +294,7 @@
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}, },
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"source": [ "source": [
@ -328,7 +316,7 @@
}, },
{ {
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{ {
@ -387,7 +375,7 @@
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"source": [ "source": [
@ -414,7 +402,7 @@
}, },
{ {
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{ {
@ -473,7 +461,7 @@
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<td>0.027</td>\n <td>-0.130</td>\n <td>0.182</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>14.10.20 00:00:04</th>\n <td>0.073</td>\n <td>-0.018</td>\n <td>0.131</td>\n <td>-0.085</td>\n <td>-0.169</td>\n <td>0.019</td>\n <td>0.055</td>\n <td>-0.066</td>\n <td>0.186</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>14.10.20 00:00:05</th>\n <td>0.106</td>\n <td>0.029</td>\n <td>0.169</td>\n <td>-0.100</td>\n <td>-0.223</td>\n <td>0.027</td>\n <td>0.005</td>\n <td>-0.139</td>\n <td>0.176</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 27 columns</p>\n</div>" "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>AVG[W] L1</th>\n <th>Min[W] L1</th>\n <th>Max[W] L1</th>\n <th>AVG[W] L2</th>\n <th>Min[W] L2</th>\n <th>Max[W] L2</th>\n <th>AVG[W] L3</th>\n <th>Min[W] L3</th>\n <th>Max[W] L3</th>\n <th>CPU% L1</th>\n <th>...</th>\n <th>KB Transmitted L2</th>\n <th>KB Received L2</th>\n <th>KB Read L2</th>\n <th>KB Write L2</th>\n <th>CPU% L3</th>\n <th>MEM Used L3</th>\n <th>KB Transmitted L3</th>\n <th>KB Received L3</th>\n <th>KB Read L3</th>\n <th>KB Write L3</th>\n </tr>\n <tr>\n <th>Time</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>14.10.20 00:00:01</th>\n <td>0.076</td>\n <td>0.009</td>\n <td>0.148</td>\n <td>-0.117</td>\n <td>-0.196</td>\n <td>0.037</td>\n <td>0.052</td>\n <td>-0.075</td>\n <td>0.207</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>14.10.20 00:00:02</th>\n <td>0.100</td>\n <td>0.021</td>\n <td>0.178</td>\n <td>-0.068</td>\n <td>-0.209</td>\n <td>0.007</td>\n <td>0.030</td>\n <td>-0.132</td>\n <td>0.176</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>14.10.20 00:00:03</th>\n <td>0.090</td>\n <td>0.002</td>\n <td>0.186</td>\n <td>0.457</td>\n <td>-0.222</td>\n <td>2.782</td>\n <td>0.027</td>\n <td>-0.130</td>\n <td>0.182</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>14.10.20 00:00:04</th>\n <td>0.073</td>\n <td>-0.018</td>\n <td>0.131</td>\n <td>-0.085</td>\n <td>-0.169</td>\n <td>0.019</td>\n <td>0.055</td>\n <td>-0.066</td>\n <td>0.186</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>14.10.20 00:00:05</th>\n <td>0.106</td>\n <td>0.029</td>\n <td>0.169</td>\n <td>-0.100</td>\n <td>-0.223</td>\n <td>0.027</td>\n <td>0.005</td>\n <td>-0.139</td>\n <td>0.176</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 27 columns</p>\n</div>"
}, },
"metadata": {}, "metadata": {},
"execution_count": 9 "execution_count": 10
} }
], ],
"source": [ "source": [
@ -487,7 +475,20 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"#plotBoxplotFigure(result, [\"KB Received L1\", \"KB Received L2\", \"KB Received L3\"], \"Network before cleaning\", \"Network Boxplot.png\")\n",
"#result[\"KB Received L1\"] = result.loc[stats.zscore(result[\"KB Received L1\"]) <= 3][\"KB Received L1\"]\n",
"#result[\"KB Received L2\"] = result.loc[stats.zscore(result[\"KB Received L2\"]) <= 3][\"KB Received L2\"]\n",
"#result[\"KB Received L3\"] = result.loc[stats.zscore(result[\"KB Received L3\"]) <= 3][\"KB Received L3\"]\n",
"#plotBoxplotFigure(result, [\"KB Received L1\", \"KB Received L2\", \"KB Received L3\"], \"Network before cleaning\", \"Network Boxplot cleaned.png\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": { "metadata": {
"tags": [] "tags": []
}, },
@ -499,15 +500,15 @@
"111111111 Windows\n", "111111111 Windows\n",
"Baseline Windows\n", "Baseline Windows\n",
"Baseline\n", "Baseline\n",
"121110101 ActivityPub\n", "110111111 ActivityPub\n",
"112110101 ActivityPub\n", "101111111 ActivityPub\n",
"211110101 ActivityPub\n", "211111111 ActivityPub\n",
"111110101 ActivityPub\n", "011111111 ActivityPub\n",
"111010101 ActivityPub\n", "111211111 ActivityPub\n",
"111210101 ActivityPub\n", "111111111 ActivityPub\n",
"110110101 ActivityPub\n", "121111111 ActivityPub\n",
"011110101 ActivityPub\n", "112111111 ActivityPub\n",
"101110101 ActivityPub\n", "111011111 ActivityPub\n",
"211111111 Matrix\n", "211111111 Matrix\n",
"121111111 Matrix\n", "121111111 Matrix\n",
"111111011 Matrix\n", "111111011 Matrix\n",
@ -556,41 +557,41 @@
"Min[W] L3 5.669529e+00\n", "Min[W] L3 5.669529e+00\n",
"dtype: float64\n", "dtype: float64\n",
"min\n", "min\n",
"AVG[W] L1 5.857771e+00\n", "AVG[W] L1 5.997093e+00\n",
"Min[W] L1 5.741483e+00\n", "Min[W] L1 5.811203e+00\n",
"Max[W] L1 5.994246e+00\n", "Max[W] L1 6.292356e+00\n",
"AVG[W] L2 3.889144e+00\n", "AVG[W] L2 4.037085e+00\n",
"Min[W] L2 3.698576e+00\n", "Min[W] L2 3.727415e+00\n",
"Max[W] L2 4.119449e+00\n", "Max[W] L2 4.554542e+00\n",
"AVG[W] L3 6.164653e+00\n", "AVG[W] L3 5.826669e+00\n",
"Min[W] L3 5.650754e+00\n", "Min[W] L3 5.622593e+00\n",
"Max[W] L3 7.187424e+00\n", "Max[W] L3 6.123331e+00\n",
"CPU% L1 5.932203e-02\n", "CPU% L1 4.322034e-01\n",
"MEM Used L1 4.503744e+06\n", "MEM Used L1 4.288508e+06\n",
"KB Transmitted L1 0.000000e+00\n", "KB Transmitted L1 5.677966e-01\n",
"KB Received L1 0.000000e+00\n", "KB Received L1 0.000000e+00\n",
"KB Read L1 0.000000e+00\n", "KB Read L1 0.000000e+00\n",
"KB Write L1 1.220339e+01\n", "KB Write L1 6.166102e+01\n",
"CPU% L2 5.932203e-02\n", "CPU% L2 3.135593e-01\n",
"MEM Used L2 4.335635e+06\n", "MEM Used L2 4.091692e+06\n",
"KB Transmitted L2 1.694915e-02\n", "KB Transmitted L2 6.694915e-01\n",
"KB Received L2 0.000000e+00\n", "KB Received L2 0.000000e+00\n",
"KB Read L2 0.000000e+00\n", "KB Read L2 0.000000e+00\n",
"KB Write L2 1.664407e+01\n", "KB Write L2 5.467797e+01\n",
"CPU% L3 3.127119e+00\n", "CPU% L3 3.983051e-01\n",
"MEM Used L3 4.310587e+06\n", "MEM Used L3 4.013788e+06\n",
"KB Transmitted L3 9.661017e-01\n", "KB Transmitted L3 1.864407e-01\n",
"KB Received L3 8.728814e-01\n", "KB Received L3 1.016949e-01\n",
"KB Read L3 0.000000e+00\n", "KB Read L3 0.000000e+00\n",
"KB Write L3 6.154237e+02\n", "KB Write L3 3.966949e+01\n",
"Energy L1 5.857771e+00\n", "Energy L1 5.997093e+00\n",
"Energy L2 3.889144e+00\n", "Energy L2 4.037085e+00\n",
"Energy L3 1.257979e+01\n", "Energy L3 6.159462e+00\n",
"Iteration 1.800000e+01\n", "Iteration 1.100000e+01\n",
"Energy total 6.425539e+00\n", "Energy total 2.924719e-01\n",
"Actions Send 1.150000e+02\n", "Actions Send 1.200000e+01\n",
"dtype: float64\n", "dtype: float64\n",
"6.704910347268386\n", "2.924719339609503\n",
"max\n", "max\n",
"AVG[W] L1 6.501847e+00\n", "AVG[W] L1 6.501847e+00\n",
"Min[W] L1 6.013449e+00\n", "Min[W] L1 6.013449e+00\n",
@ -610,7 +611,7 @@
"CPU% L2 1.957627e+00\n", "CPU% L2 1.957627e+00\n",
"MEM Used L2 4.403195e+06\n", "MEM Used L2 4.403195e+06\n",
"KB Transmitted L2 5.525424e+00\n", "KB Transmitted L2 5.525424e+00\n",
"KB Received L2 3.675214e+00\n", "KB Received L2 3.646552e+00\n",
"KB Read L2 0.000000e+00\n", "KB Read L2 0.000000e+00\n",
"KB Write L2 4.514915e+02\n", "KB Write L2 4.514915e+02\n",
"CPU% L3 3.084746e+00\n", "CPU% L3 3.084746e+00\n",
@ -619,14 +620,14 @@
"KB Received L3 3.686441e+00\n", "KB Received L3 3.686441e+00\n",
"KB Read L3 0.000000e+00\n", "KB Read L3 0.000000e+00\n",
"KB Write L3 4.467458e+02\n", "KB Write L3 4.467458e+02\n",
"Energy L1 8.728277e+01\n", "Energy L1 4.247116e+01\n",
"Energy L2 3.171558e+01\n", "Energy L2 1.663821e+01\n",
"Energy L3 3.329450e+01\n", "Energy L3 1.826516e+01\n",
"Iteration 2.600000e+01\n", "Iteration 2.600000e+01\n",
"Energy total 1.363917e+02\n", "Energy total 6.147336e+01\n",
"Actions Send 1.090000e+02\n", "Actions Send 1.090000e+02\n",
"dtype: float64\n", "dtype: float64\n",
"150.1559780437906\n" "67.67709118463543\n"
] ]
} }
], ],
@ -653,18 +654,18 @@
" end_measurement_string = time.strftime('%d.%m.%y %H:%M:%S', time.localtime(int(csv_data[\"End\"][len(csv_data[\"End\"]) - 1])))\n", " end_measurement_string = time.strftime('%d.%m.%y %H:%M:%S', time.localtime(int(csv_data[\"End\"][len(csv_data[\"End\"]) - 1])))\n",
" measurement_data = result.loc[start_measurement_string:end_measurement_string]\n", " measurement_data = result.loc[start_measurement_string:end_measurement_string]\n",
"\n", "\n",
" plotBoxplotFigure(measurement_data, [\"AVG[W] L1\", \"AVG[W] L2\", \"AVG[W] L3\"], measurement_name + \" Energy before cleaning\", \"plots/Energy/\" + measurement_name + \" Boxplot.png\")\n", " plotBoxplotFigure(measurement_data, [\"AVG[W] L1\", \"AVG[W] L2\", \"AVG[W] L3\"], measurement_name + \" Energy before cleaning\", \"plots/Energy/\" + measurement_name.replace(\" \", \"_\") + \"_Boxplot.png\")\n",
" measurement_data[\"AVG[W] L1\"] = cleanDataIQR(measurement_data[\"AVG[W] L1\"])\n", " measurement_data[\"AVG[W] L1\"] = cleanDataIQR(measurement_data[\"AVG[W] L1\"], 10)\n",
" measurement_data[\"AVG[W] L2\"] = cleanDataIQR(measurement_data[\"AVG[W] L2\"])\n", " measurement_data[\"AVG[W] L2\"] = cleanDataIQR(measurement_data[\"AVG[W] L2\"], 10)\n",
" measurement_data[\"AVG[W] L3\"] = cleanDataIQR(measurement_data[\"AVG[W] L3\"])\n", " measurement_data[\"AVG[W] L3\"] = cleanDataIQR(measurement_data[\"AVG[W] L3\"], 10)\n",
" plotBoxplotFigure(measurement_data, [\"AVG[W] L1\", \"AVG[W] L2\", \"AVG[W] L3\"], measurement_name + \" Energy after cleaning\", \"plots/Energy/\" + measurement_name + \" Boxplot cleaned.png\")\n", " plotBoxplotFigure(measurement_data, [\"AVG[W] L1\", \"AVG[W] L2\", \"AVG[W] L3\"], measurement_name + \" Energy after cleaning\", \"plots/Energy/\" + measurement_name.replace(\" \", \"_\") + \"_Boxplot_cleaned.png\")\n",
" \n", " \n",
" plotBoxplotFigure(measurement_data, [\"KB Received L1\", \"KB Received L2\", \"KB Received L3\"], measurement_name + \" Network before cleaning\", \"plots/Network/received/\" + measurement_name + \" Boxplot.png\")\n", " plotBoxplotFigure(measurement_data, [\"KB Received L1\", \"KB Received L2\", \"KB Received L3\"], measurement_name + \" Network before cleaning\", \"plots/Network/received/\" + measurement_name.replace(\" \", \"_\") + \"_Boxplot.png\")\n",
" if \"Baseline\" not in measurement_name and \"Windows\" not in measurement_name:\n", " if \"Baseline\" not in measurement_name and \"Windows\" not in measurement_name:\n",
" measurement_data[\"KB Received L1\"] = measurement_data.loc[stats.zscore(measurement_data[\"KB Received L1\"]) <= 3][\"KB Received L1\"]\n", " measurement_data[\"KB Received L1\"] = measurement_data.loc[stats.zscore(measurement_data[\"KB Received L1\"]) <= 1.5][\"KB Received L1\"]\n",
" measurement_data[\"KB Received L2\"] = measurement_data.loc[stats.zscore(measurement_data[\"KB Received L2\"]) <= 3][\"KB Received L2\"]\n", " measurement_data[\"KB Received L2\"] = measurement_data.loc[stats.zscore(measurement_data[\"KB Received L2\"]) <= 1.5][\"KB Received L2\"]\n",
" measurement_data[\"KB Received L3\"] = measurement_data.loc[stats.zscore(measurement_data[\"KB Received L3\"]) <= 3][\"KB Received L3\"]\n", " measurement_data[\"KB Received L3\"] = measurement_data.loc[stats.zscore(measurement_data[\"KB Received L3\"]) <= 1.5][\"KB Received L3\"]\n",
" plotBoxplotFigure(measurement_data, [\"KB Received L1\", \"KB Received L2\", \"KB Received L3\"], measurement_name + \" Network after cleaning\", \"plots/Network/received/\" + measurement_name + \" Boxplot cleaned.png\")\n", " plotBoxplotFigure(measurement_data, [\"KB Received L1\", \"KB Received L2\", \"KB Received L3\"], measurement_name + \" Network after cleaning\", \"plots/Network/received/\" + measurement_name.replace(\" \", \"_\") + \"_Boxplot_cleaned.png\")\n",
"\n", "\n",
" measurement_results = pd.DataFrame()\n", " measurement_results = pd.DataFrame()\n",
" iteration_count = 0\n", " iteration_count = 0\n",
@ -674,18 +675,18 @@
" end_string = time.strftime('%d.%m.%y %H:%M:%S', time.localtime(int(measurement_row[2]) - 1))\n", " end_string = time.strftime('%d.%m.%y %H:%M:%S', time.localtime(int(measurement_row[2]) - 1))\n",
" iteration_data = measurement_data.loc[start_string:end_string]\n", " iteration_data = measurement_data.loc[start_string:end_string]\n",
"\n", "\n",
" plotFigure(iteration_data, [\"AVG[W] L1\",\"AVG[W] L2\",\"AVG[W] L3\"], \"Energy (W)\", \"Energy \" + title, \"plots/Energy/\" + title + \".png\")\n", " plotFigure(iteration_data, [\"AVG[W] L1\",\"AVG[W] L2\",\"AVG[W] L3\"], \"Energy (W)\", \"Energy \" + title, \"plots/Energy/\" + title.replace(\" \", \"_\") + \".png\")\n",
" plotFigure(iteration_data, [\"CPU% L1\",\"CPU% L2\",\"CPU% L3\"], \"Workload (%)\", \"CPU \" + title, \"plots/CPU/\" + title + \".png\")\n", " plotFigure(iteration_data, [\"CPU% L1\",\"CPU% L2\",\"CPU% L3\"], \"Workload (%)\", \"CPU \" + title, \"plots/CPU/\" + title.replace(\" \", \"_\") + \".png\")\n",
" plotFigure(iteration_data, [\"MEM Used L1\",\"MEM Used L2\",\"MEM Used L3\"], \"Usage (MByte)\", \"Memory \" + title, \"plots/MEM/\" + title + \".png\")\n", " plotFigure(iteration_data, [\"MEM Used L1\",\"MEM Used L2\",\"MEM Used L3\"], \"Usage (MByte)\", \"Memory \" + title, \"plots/MEM/\" + title.replace(\" \", \"_\") + \".png\")\n",
" plotFigure(iteration_data, [\"KB Transmitted L1\",\"KB Transmitted L2\",\"KB Transmitted L3\"], \"Transmitted (KB)\", \"Network Send \" + title, \"plots/Network/transmitted/\" + title + \" transmitted.png\")\n", " plotFigure(iteration_data, [\"KB Transmitted L1\",\"KB Transmitted L2\",\"KB Transmitted L3\"], \"Transmitted (KB)\", \"Network Send \" + title, \"plots/Network/transmitted/\" + title.replace(\" \", \"_\") + \".png\")\n",
" plotFigure(iteration_data, [\"KB Received L1\",\"KB Received L2\",\"KB Received L3\"], \"Received (KB)\", \"Network Received \" + title, \"plots/Network/received/\" + title + \" received.png\")\n", " plotFigure(iteration_data, [\"KB Received L1\",\"KB Received L2\",\"KB Received L3\"], \"Received (KB)\", \"Network Received \" + title, \"plots/Network/received/\" + title.replace(\" \", \"_\") + \".png\")\n",
" plotFigure(iteration_data, [\"KB Read L1\",\"KB Read L2\",\"KB Read L3\"], \"Read (KB)\", \"Disc Read \" + title, \"plots/Disc/\" + title + \" read.png\")\n", " plotFigure(iteration_data, [\"KB Read L1\",\"KB Read L2\",\"KB Read L3\"], \"Read (KB)\", \"Disc Read \" + title, \"plots/Disc/\" + title.replace(\" \", \"_\") + \"_read.png\")\n",
" plotFigure(iteration_data, [\"KB Write L1\",\"KB Write L2\",\"KB Write L3\"], \"Write (KB)\", \"Disc Write \" + title, \"plots/Disc/\" + title + \" write.png\")\n", " plotFigure(iteration_data, [\"KB Write L1\",\"KB Write L2\",\"KB Write L3\"], \"Write (KB)\", \"Disc Write \" + title, \"plots/Disc/\" + title.replace(\" \", \"_\") + \"_write.png\")\n",
" iteration = iteration_data.mean()\n", " iteration = iteration_data.mean()\n",
"\n", "\n",
" iteration[\"Energy L1\"] = iteration[\"AVG[W] L1\"] + (((iteration[\"KB Received L1\"] / 1000000) / 0.025634766) * 52 * 3600) + ((iteration[\"KB Received L1\"] / 1000000) * 0.052 * 0.25 * 3600000)\n", " iteration[\"Energy L1\"] = iteration[\"AVG[W] L1\"] + (((iteration.fillna(0)[\"KB Received L1\"] / 1000000) / 0.058034665178572) * 52 * 3600) + ((iteration.fillna(0)[\"KB Received L1\"] / 1000000) * 0.052 * 0.25 * 3600000)\n",
" iteration[\"Energy L2\"] = iteration[\"AVG[W] L2\"] + (((iteration[\"KB Received L2\"] / 1000000) / 0.025634766) * 52 * 3600) + ((iteration[\"KB Received L2\"] / 1000000) * 0.052 * 0.25 * 3600000)\n", " iteration[\"Energy L2\"] = iteration[\"AVG[W] L2\"] + (((iteration.fillna(0)[\"KB Received L2\"] / 1000000) / 0.058034665178572) * 52 * 3600) + ((iteration.fillna(0)[\"KB Received L2\"] / 1000000) * 0.052 * 0.25 * 3600000)\n",
" iteration[\"Energy L3\"] = iteration[\"AVG[W] L3\"] + (((iteration[\"KB Received L3\"] / 1000000) / 0.025634766) * 52 * 3600) + ((iteration[\"KB Received L3\"] / 1000000) * 0.052 * 0.25 * 3600000)\n", " iteration[\"Energy L3\"] = iteration[\"AVG[W] L3\"] + (((iteration.fillna(0)[\"KB Received L3\"] / 1000000) / 0.058034665178572) * 52 * 3600) + ((iteration.fillna(0)[\"KB Received L3\"] / 1000000) * 0.052 * 0.25 * 3600000)\n",
"\n", "\n",
" iteration['Iteration'] = int(measurement_row[0])\n", " iteration['Iteration'] = int(measurement_row[0])\n",
" measurement_results = measurement_results.append(iteration, ignore_index=True)\n", " measurement_results = measurement_results.append(iteration, ignore_index=True)\n",
@ -705,22 +706,22 @@
" iteration_data = iteration_data.reset_index()\n", " iteration_data = iteration_data.reset_index()\n",
" del iteration_data[\"Time\"]\n", " del iteration_data[\"Time\"]\n",
" if measurement_avg is None:\n", " if measurement_avg is None:\n",
" measurement_avg = iteration_data\n", " measurement_avg = iteration_data.fillna(0)\n",
" else:\n", " else:\n",
" measurement_avg = measurement_avg + iteration_data.fillna(0)\n", " measurement_avg = measurement_avg + iteration_data.fillna(0)\n",
" iteration_count = iteration_count + 1\n", " iteration_count = iteration_count + 1\n",
" measurement_avg = measurement_avg / iteration_count\n", " measurement_avg = measurement_avg / iteration_count\n",
" plotFigure(measurement_avg, [\"AVG[W] L1\",\"AVG[W] L2\",\"AVG[W] L3\"], \"Energy (W)\", \"Energy \" + measurement_name, \"plots/Energy/\" + measurement_name + \".png\")\n", " plotFigure(measurement_avg, [\"AVG[W] L1\",\"AVG[W] L2\",\"AVG[W] L3\"], \"Energy (W)\", \"Energy \" + measurement_name, \"plots/Energy/\" + measurement_name.replace(\" \", \"_\") + \".png\")\n",
" plotFigure(measurement_avg, [\"CPU% L1\",\"CPU% L2\",\"CPU% L3\"], \"Workload (%)\", \"CPU \" + measurement_name, \"plots/CPU/\" + measurement_name + \".png\")\n", " plotFigure(measurement_avg, [\"CPU% L1\",\"CPU% L2\",\"CPU% L3\"], \"Workload (%)\", \"CPU \" + measurement_name, \"plots/CPU/\" + measurement_name.replace(\" \", \"_\") + \".png\")\n",
" plotFigure(measurement_avg, [\"MEM Used L1\",\"MEM Used L2\",\"MEM Used L3\"], \"Usage (MByte)\", \"Memory \" + measurement_name, \"plots/MEM/\" + measurement_name + \".png\")\n", " plotFigure(measurement_avg, [\"MEM Used L1\",\"MEM Used L2\",\"MEM Used L3\"], \"Usage (MByte)\", \"Memory \" + measurement_name, \"plots/MEM/\" + measurement_name.replace(\" \", \"_\") + \".png\")\n",
" plotFigure(measurement_avg, [\"KB Transmitted L1\",\"KB Transmitted L2\",\"KB Transmitted L3\"], \"Transmitted (KB)\", \"Network Send \" + measurement_name, \"plots/Network/transmitted/\" + measurement_name + \" transmitted.png\")\n", " plotFigure(measurement_avg, [\"KB Transmitted L1\",\"KB Transmitted L2\",\"KB Transmitted L3\"], \"Transmitted (KB)\", \"Network Send \" + measurement_name, \"plots/Network/transmitted/\" + measurement_name.replace(\" \", \"_\") + \".png\")\n",
" plotFigure(measurement_avg, [\"KB Received L1\",\"KB Received L2\",\"KB Received L3\"], \"Received (KB)\", \"Network Received \" + measurement_name, \"plots/Network/received/\" + measurement_name + \" received.png\")\n", " plotFigure(measurement_avg, [\"KB Received L1\",\"KB Received L2\",\"KB Received L3\"], \"Received (KB)\", \"Network Received \" + measurement_name, \"plots/Network/received/\" + measurement_name.replace(\" \", \"_\") + \".png\")\n",
" plotFigure(measurement_avg, [\"KB Read L1\",\"KB Read L2\",\"KB Read L3\"], \"Read (KB)\", \"Disc Read \" + measurement_name, \"plots/Disc/\" + measurement_name + \" read.png\")\n", " plotFigure(measurement_avg, [\"KB Read L1\",\"KB Read L2\",\"KB Read L3\"], \"Read (KB)\", \"Disc Read \" + measurement_name, \"plots/Disc/\" + measurement_name.replace(\" \", \"_\") + \"_read.png\")\n",
" plotFigure(measurement_avg, [\"KB Write L1\",\"KB Write L2\",\"KB Write L3\"], \"Write (KB)\", \"Disc Write \" + measurement_name, \"plots/Disc/\" + measurement_name + \" write.png\")\n", " plotFigure(measurement_avg, [\"KB Write L1\",\"KB Write L2\",\"KB Write L3\"], \"Write (KB)\", \"Disc Write \" + measurement_name, \"plots/Disc/\" + measurement_name.replace(\" \", \"_\") + \"_write.png\")\n",
" csv_data = csv_data.merge(measurement_results)\n", " csv_data = csv_data.merge(measurement_results)\n",
" csv_data_mean = csv_data.mean()\n", " csv_data_mean = csv_data.mean()\n",
" csv_data = csv_data.append(csv_data_mean, ignore_index=True)\n", " csv_data = csv_data.append(csv_data_mean, ignore_index=True)\n",
" csv_data.to_csv(measurement_file.name)\n", " csv_data.to_csv(\"output/\" + measurement_file.name)\n",
" if \"Windows\" not in measurement_name:\n", " if \"Windows\" not in measurement_name:\n",
" if \"Baseline\" in measurement_name:\n", " if \"Baseline\" in measurement_name:\n",
" baseline = csv_data_mean\n", " baseline = csv_data_mean\n",
@ -752,12 +753,12 @@
" summary[\"MEM Used total\"] = summary[\"MEM Used L1 without Baseline\"] + summary[\"MEM Used L2 without Baseline\"] + summary[\"MEM Used L3 without Baseline\"]\n", " summary[\"MEM Used total\"] = summary[\"MEM Used L1 without Baseline\"] + summary[\"MEM Used L2 without Baseline\"] + summary[\"MEM Used L3 without Baseline\"]\n",
" summary[\"KB Received total\"] = summary[\"KB Received L1 without Baseline\"] + summary[\"KB Received L2 without Baseline\"] + summary[\"KB Received L3 without Baseline\"]\n", " summary[\"KB Received total\"] = summary[\"KB Received L1 without Baseline\"] + summary[\"KB Received L2 without Baseline\"] + summary[\"KB Received L3 without Baseline\"]\n",
" summary[\"KB Write total\"] = summary[\"KB Write L1 without Baseline\"] + summary[\"KB Write L2 without Baseline\"] + summary[\"KB Write L3 without Baseline\"]\n", " summary[\"KB Write total\"] = summary[\"KB Write L1 without Baseline\"] + summary[\"KB Write L2 without Baseline\"] + summary[\"KB Write L3 without Baseline\"]\n",
"summary.to_csv(\"summary.csv\")" "summary.to_csv(\"output/summary.csv\")"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 21,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -766,19 +767,19 @@
"fig = go.Figure()\n", "fig = go.Figure()\n",
"\n", "\n",
"fig.add_trace(go.Scatterpolar(\n", "fig.add_trace(go.Scatterpolar(\n",
" r=[0.3313,0.3313,0.3313,0.3313,0.3313,0.3313,0.3313,0.3313,0.3313],\n", " r=[0.3192,0.3192,0.3192,0.3192,0.3192,0.3192,0.3192,0.3192,0.3192],\n",
" theta=categories,\n", " theta=categories,\n",
" fill='toself',\n", " fill='toself',\n",
" name='Average'\n", " name='Average'\n",
"))\n", "))\n",
"fig.add_trace(go.Scatterpolar(\n", "fig.add_trace(go.Scatterpolar(\n",
" r=[0.3478,0.3651,0.3333,0.3738,0.3313,0.3313,0.3313,0.3313,0.3313],\n", " r=[0.3301,0.2590,0.3331,0.3718,0.3192,0.3192,0.3192,0.3192,0.3192],\n",
" theta=categories,\n", " theta=categories,\n",
" fill='toself',\n", " fill='toself',\n",
" name='Worst'\n", " name='Worst'\n",
"))\n", "))\n",
"fig.add_trace(go.Scatterpolar(\n", "fig.add_trace(go.Scatterpolar(\n",
" r=[0.2806,0.2578,0.0110,0.3291,0.3313,0.3310,0.2790,0.2782,0.3313],\n", " r=[0.2815,0.3753,0.0225,0.3264,0.3192,0.3194,0.2819,0.2720,0.3192],\n",
" theta=categories,\n", " theta=categories,\n",
" fill='toself',\n", " fill='toself',\n",
" name='Best'\n", " name='Best'\n",
@ -800,12 +801,12 @@
" showlegend=True\n", " showlegend=True\n",
")\n", ")\n",
"\n", "\n",
"fig.write_image(\"Radar Matrix.png\")" "fig.write_image(\"plots/Radar Matrix.png\")"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 22,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -814,19 +815,19 @@
"fig = go.Figure()\n", "fig = go.Figure()\n",
"\n", "\n",
"fig.add_trace(go.Scatterpolar(\n", "fig.add_trace(go.Scatterpolar(\n",
" r=[0.2926,0.2926,0.2926,0.2926,0.2926,0.2926,0.2926,0.2926,0.2926],\n", " r=[0.2479,0.2479,0.2479,0.2479,0.2479,0.2479,0.2479,0.2479,0.2479],\n",
" theta=categories,\n", " theta=categories,\n",
" fill='toself',\n", " fill='toself',\n",
" name='Average'\n", " name='Average'\n",
"))\n", "))\n",
"fig.add_trace(go.Scatterpolar(\n", "fig.add_trace(go.Scatterpolar(\n",
" r=[0.2708,0.3015,0.9479,0.3451,0.2926,0.2926,0.2926,0.2926,0.2926],\n", " r=[0.2770,0.2556,0.9052,0.1882,0.2479,0.2479,0.2479,0.2479,0.2479],\n",
" theta=categories,\n", " theta=categories,\n",
" fill='toself',\n", " fill='toself',\n",
" name='Worst'\n", " name='Worst'\n",
"))\n", "))\n",
"fig.add_trace(go.Scatterpolar(\n", "fig.add_trace(go.Scatterpolar(\n",
" r=[0.2930,0.2199,0.0013,0.2269,0.2926,0.2926,0.2926,0.2926,0.2926],\n", " r=[0.2187,0.0513,0.0030,0.3072,0.2479,0.2479,0.2479,0.2479,0.2479],\n",
" theta=categories,\n", " theta=categories,\n",
" fill='toself',\n", " fill='toself',\n",
" name='Best'\n", " name='Best'\n",
@ -848,12 +849,12 @@
" showlegend=True\n", " showlegend=True\n",
")\n", ")\n",
"\n", "\n",
"fig.write_image(\"Radar ActivityPub.png\")" "fig.write_image(\"plots/Radar ActivityPub.png\")"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -885,7 +886,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.8.6-final" "version": "3.7.5-final"
} }
}, },
"nbformat": 4, "nbformat": 4,

0
MeasurementFiles/ActivityPub/1602788743 111110101 Measurement ActivityPub.csv → MeasurementFiles/ActivityPub/1602788743 111111111 Measurement ActivityPub.csv
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MeasurementFiles/ActivityPub/1602792388 011110101 Measurement ActivityPub.csv → MeasurementFiles/ActivityPub/1602792388 011111111 Measurement ActivityPub.csv
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MeasurementFiles/ActivityPub/1602796034 211110101 Measurement ActivityPub.csv → MeasurementFiles/ActivityPub/1602796034 211111111 Measurement ActivityPub.csv
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MeasurementFiles/ActivityPub/1602799678 101110101 Measurement ActivityPub.csv → MeasurementFiles/ActivityPub/1602799678 101111111 Measurement ActivityPub.csv
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MeasurementFiles/ActivityPub/1602810616 112110101 Measurement ActivityPub.csv → MeasurementFiles/ActivityPub/1602810616 112111111 Measurement ActivityPub.csv
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MeasurementFiles/ActivityPub/1602814266 111010101 Measurement ActivityPub.csv → MeasurementFiles/ActivityPub/1602814266 111011111 Measurement ActivityPub.csv
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MeasurementFiles/ActivityPub/1602817908 111210101 Measurement ActivityPub.csv → MeasurementFiles/ActivityPub/1602817908 111211111 Measurement ActivityPub.csv
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39
Spikes
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@ -1,39 +0,0 @@
Spikes:
011110101 ActivityPub 4
011111111 Matrix 1
011111111 Matrix 7
110110101 ActivityPub 13
110110101 ActivityPub 29
110110101 ActivityPub 30
111010101 ActivityPub 28
111110101 ActivityPub 1
111110111 Matrix 4
111110111 Matrix 22
111110111 Matrix 23
111110111 Matrix 28
111111011 Matrix 13
111111011 Matrix 16
111111011 Matrix 23
111111101 Matrix 5
111111101 Matrix 8
111111101 Matrix 26
111210101 ActivityPub 5
112110101 ActivityPub 7
112110101 ActivityPub 30
121110101 ActivityPub 16
121111111 Matrix 12
211110101 ActivityPub 27
Baseline 6
Raus:
011111111 Matrix 2
011111111 Matrix 3
111111011 Matrix 30
111111101 Matrix 1
111210101 ActivityPub 25
111210101 ActivityPub 26
111210101 ActivityPub 27
121110101 ActivityPub 10
121110101 ActivityPub 11
121110101 ActivityPub 12
121110101 ActivityPub 13

22
summary.csv Normal file
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@ -0,0 +1,22 @@
,AVG[W] L1,AVG[W] L2,AVG[W] L3,Actions Send,CPU% L1,CPU% L2,CPU% L3,Energy L1,Energy L2,Energy L3,KB Received L1,KB Received L2,KB Received L3,KB Write L1,KB Write L2,KB Write L3,MEM Used L1,MEM Used L2,MEM Used L3,Measurement,AVG[W] L1 without Baseline,AVG[W] L2 without Baseline,AVG[W] L3 without Baseline,CPU% L1 without Baseline,CPU% L2 without Baseline,CPU% L3 without Baseline,Energy L1 without Baseline,Energy L2 without Baseline,Energy L3 without Baseline,KB Received L1 without Baseline,KB Received L2 without Baseline,KB Received L3 without Baseline,KB Write L1 without Baseline,KB Write L2 without Baseline,KB Write L3 without Baseline,MEM Used L1 without Baseline,MEM Used L2 without Baseline,MEM Used L3 without Baseline,Energy total,Energy Efficiency,Energy Efficiency normalized,CPU% total,MEM Used total,KB Received total,KB Write total
0,7.808914276401562,6.286390808344198,7.261747763341277,757.6923076923077,23.409387222946545,11.657757496740551,19.58833116036505,76.76200014617676,26.53339329856112,56.070228795363,21.070730117340286,6.1870925684485005,14.914928292046937,2817.023142112125,1281.5837679269885,1315.1082138200784,4440015.048239895,4252831.544980444,4160338.2451108214,121110101 ActivityPub,1.8579636737759415,2.1542189447992177,1.4437023353395677,23.358582494275417,11.63659242054058,19.540902019217068,70.81104954355114,22.40122143501614,50.25218336736129,21.070730117340286,6.1870925684485005,14.914928292046937,2813.2292950817873,1276.1843661323724,1309.5672367512848,364660.91204846883,361689.4704041728,338097.97053455,143.46445434592857,22.72127398778158,0.30452405913835734,54.53607693403306,1064448.3529871916,42.172750977835726,5398.9808979654445
1,6.489535099506286,4.68131444574323,6.1959403954802275,110.0,3.6415254237288144,1.9183615819209037,3.081073446327683,38.34146264995015,16.35839386029627,20.253281331152895,9.733333333333333,3.5682897407590577,4.29565164904148,431.261581920904,438.4511299435028,438.2454802259888,4554120.656497177,4384645.333333332,4359633.8271186445,112110101 ActivityPub,0.5385844968806657,0.5491425821982503,0.37789496747851814,3.590720695057685,1.8971965057209321,3.0336443051796986,32.390512047324535,12.226221996751292,14.435235903151185,9.733333333333333,3.5682897407590577,4.29565164904148,427.4677348905665,433.05172814888664,432.7045031571952,478766.5203057504,493503.25875706086,537393.5525423731,59.05196994722701,64.42033085152038,0.9481388916402161,8.521561505958315,1509663.3316051844,17.59727472313387,1293.2239661966482
2,6.499537517504467,4.648689082041624,6.135075411656768,113.73333333333333,3.108474576271186,1.6107344632768361,2.4175141242937843,18.557714408843857,8.066865839318057,9.266790281607319,3.6847457627118643,1.0445287406275132,0.9569915254237288,448.028813559322,206.21271186440674,216.13559322033902,4264589.376271186,4077322.110734463,3988751.0564971743,211110101 ActivityPub,0.5485869148788467,0.5165172184966442,0.3170299836550585,3.0576698476000566,1.5895693870768646,2.3700849831457997,12.606763806218236,3.934693975773077,3.44874485360561,3.6847457627118643,1.0445287406275132,0.9569915254237288,444.23496652898444,200.8133100697906,210.5946161515454,189235.2400797601,186180.03615819197,166510.7819209029,19.990202635596923,21.09165577026639,0.2793712970618585,7.017324217822721,541926.058158855,5.686266028763106,855.6428927503205
3,6.467503954802259,4.5982720628712155,6.1015745762711875,113.76666666666667,3.0036723163841805,1.5610169491525425,2.3872881355932205,16.741259533004627,8.729510582550562,11.681630353360507,3.1394611038678826,1.2624266310999215,1.7051571780385335,440.9898305084746,198.85762711864407,204.78192090395478,4154723.262146892,3968123.528813559,3887950.482485875,111110101 ActivityPub,0.516553352176639,0.4661001993262355,0.28352914826947817,2.952867587713051,1.539851872952571,2.339858994445236,10.790308930379005,4.597338719005582,5.863584925358798,3.1394611038678826,1.2624266310999215,1.7051571780385335,437.19598347813707,193.45822532402792,199.24094383516118,79369.12595546572,76981.45423728786,65710.2079096036,21.251232574743383,22.415598379453908,0.2998060302367301,6.832578455110858,222060.78810235718,6.107044913006337,829.8951526373261
4,6.461813559322033,4.571790677966102,6.1071092733294385,113.46666666666667,2.9985875706214697,1.6214689265536721,2.4610169491525427,17.301158146316233,13.248427315662473,9.706898745681315,3.312294171616206,2.651412429378531,1.1000260756192959,447.1581920903954,207.01214689265535,232.98305084745763,4606779.806779662,4445579.781920904,4409869.790960452,111010101 ActivityPub,0.5108629566964131,0.43961881442112194,0.2890638453277292,2.9477828419503402,1.6003038503537006,2.413587808004558,11.350207543690612,9.116255452117493,3.8888533176796054,3.312294171616206,2.651412429378531,1.1000260756192959,443.3643450600579,201.6127450980392,227.44207377866402,531425.6705882358,554437.7073446331,587629.5163841802,24.35531631348771,25.75767882742531,0.3513902316434376,6.961674500308599,1673492.894317049,7.063732676614032,872.4191639367612
5,6.39544979101947,4.481787507846829,6.055677137154539,114.81481481481481,2.65819209039548,1.3069679849340867,2.0740740740740744,15.103516819263643,9.581453272711915,8.514372396053787,2.6610169491525424,1.558359276668907,0.7513297423539815,371.44632768361583,168.38794726930323,173.29692404268678,4656199.339610796,4500007.605775267,4458191.7526679225,111210101 ActivityPub,0.4444991883938494,0.3496156443018492,0.23763170915282927,2.6073873617243506,1.2858029087341152,2.02664493292609,9.152566216638022,5.449281409166935,2.6963269680520776,2.6610169491525424,1.558359276668907,0.7513297423539815,367.6524806532783,162.98854547468707,167.75594697389317,580845.2034193701,608865.5311989957,635951.4780916511,17.298174593857034,18.0793824787409,0.2328775891511427,5.919835203384556,1825662.2127100169,4.97070596817543,698.3969731018585
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1 AVG[W] L1 AVG[W] L2 AVG[W] L3 Actions Send CPU% L1 CPU% L2 CPU% L3 Energy L1 Energy L2 Energy L3 KB Received L1 KB Received L2 KB Received L3 KB Write L1 KB Write L2 KB Write L3 MEM Used L1 MEM Used L2 MEM Used L3 Measurement AVG[W] L1 without Baseline AVG[W] L2 without Baseline AVG[W] L3 without Baseline CPU% L1 without Baseline CPU% L2 without Baseline CPU% L3 without Baseline Energy L1 without Baseline Energy L2 without Baseline Energy L3 without Baseline KB Received L1 without Baseline KB Received L2 without Baseline KB Received L3 without Baseline KB Write L1 without Baseline KB Write L2 without Baseline KB Write L3 without Baseline MEM Used L1 without Baseline MEM Used L2 without Baseline MEM Used L3 without Baseline Energy total Energy Efficiency Energy Efficiency normalized CPU% total MEM Used total KB Received total KB Write total
2 0 7.808914276401562 6.286390808344198 7.261747763341277 757.6923076923077 23.409387222946545 11.657757496740551 19.58833116036505 76.76200014617676 26.53339329856112 56.070228795363 21.070730117340286 6.1870925684485005 14.914928292046937 2817.023142112125 1281.5837679269885 1315.1082138200784 4440015.048239895 4252831.544980444 4160338.2451108214 121110101 ActivityPub 1.8579636737759415 2.1542189447992177 1.4437023353395677 23.358582494275417 11.63659242054058 19.540902019217068 70.81104954355114 22.40122143501614 50.25218336736129 21.070730117340286 6.1870925684485005 14.914928292046937 2813.2292950817873 1276.1843661323724 1309.5672367512848 364660.91204846883 361689.4704041728 338097.97053455 143.46445434592857 22.72127398778158 0.30452405913835734 54.53607693403306 1064448.3529871916 42.172750977835726 5398.9808979654445
3 1 6.489535099506286 4.68131444574323 6.1959403954802275 110.0 3.6415254237288144 1.9183615819209037 3.081073446327683 38.34146264995015 16.35839386029627 20.253281331152895 9.733333333333333 3.5682897407590577 4.29565164904148 431.261581920904 438.4511299435028 438.2454802259888 4554120.656497177 4384645.333333332 4359633.8271186445 112110101 ActivityPub 0.5385844968806657 0.5491425821982503 0.37789496747851814 3.590720695057685 1.8971965057209321 3.0336443051796986 32.390512047324535 12.226221996751292 14.435235903151185 9.733333333333333 3.5682897407590577 4.29565164904148 427.4677348905665 433.05172814888664 432.7045031571952 478766.5203057504 493503.25875706086 537393.5525423731 59.05196994722701 64.42033085152038 0.9481388916402161 8.521561505958315 1509663.3316051844 17.59727472313387 1293.2239661966482
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