367 lines
14 KiB
Text
367 lines
14 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Task slowdown"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import sys\n",
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"import gzip\n",
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"import pandas\n",
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"import seaborn as sns\n",
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"import matplotlib as mpl\n",
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"import matplotlib.pyplot as plt\n",
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"from IPython.display import display, HTML"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Legend for columns:\n",
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"- *n_fsh*: number of jobs that terminated with status 6 (FINISH)\n",
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"- *n_non*: number of jobs that did not terminate with status 6 or did not terminate at all\n",
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"- *finished%*: `n_fsh` / (`n_fsh` + `n_non`)\n",
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"- *c_zero_end*: count of number of **Finished** jobs that have a last execution time of `0` \n",
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"- *s_last*: sum of execution times for last events\n",
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"- *m_last*: mean execution time for last event\n",
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"- *s_all*: sum of all execution times for all events\n",
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"- *m_all*: mean execution time for all events\n",
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"- *s_slow*: sum of **slowdown** values computed for each job: `job_slowdown` = sum(`exec_time`) / last(`exec_time`)\n",
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"- *m_slow*: mean job-wise **slowdown** value, i.e. `s_slow` / `n_fsh`\n",
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"- *m_slow_2*: priority-wise mean **slowdown**, i.e. `s_all` / `s_last`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\\taskslowdown{Cluster A}{\n",
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"\\begin{tabular}{rrr}\n",
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"\\toprule\n",
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" priority & finished\\% & m\\_slow\\_2 \\\\\n",
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"\\midrule\n",
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" -1 & 10.620113 & 1.097556 \\\\\n",
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" 24 & 0.000000 & NaN \\\\\n",
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" 25 & 0.333054 & 82.973285 \\\\\n",
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" 100 & 0.000000 & NaN \\\\\n",
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" 101 & 81.917703 & 30.798089 \\\\\n",
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" 102 & 0.000000 & NaN \\\\\n",
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" 103 & 14.990678 & 1.130579 \\\\\n",
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" 105 & 57.678214 & 1.078733 \\\\\n",
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" 107 & 53.926543 & 1.016187 \\\\\n",
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" 114 & 0.000000 & NaN \\\\\n",
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" 115 & 4.108501 & 1.004324 \\\\\n",
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" 116 & 13.045304 & 1.032749 \\\\\n",
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" 117 & 0.000000 & NaN \\\\\n",
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" 118 & 11.907081 & 1.003494 \\\\\n",
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" 119 & 21.264583 & 1.504923 \\\\\n",
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" 170 & 0.000000 & NaN \\\\\n",
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" 200 & 27.211754 & 4.116760 \\\\\n",
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" 205 & 0.000000 & NaN \\\\\n",
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" 210 & 0.000000 & NaN \\\\\n",
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" 214 & 0.000000 & NaN \\\\\n",
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" 215 & 0.000000 & NaN \\\\\n",
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" 360 & 0.616372 & 2.924018 \\\\\n",
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" 400 & 0.000000 & NaN \\\\\n",
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" 450 & 2.203423 & 1.142450 \\\\\n",
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" 500 & 0.000000 & NaN \\\\\n",
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"\n",
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"}\n",
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"\\taskslowdown{Cluster B}{\n",
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"\\begin{tabular}{rrr}\n",
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"\\toprule\n",
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" priority & finished\\% & m\\_slow\\_2 \\\\\n",
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"\\midrule\n",
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" 0 & 45.193049 & 1.176397 \\\\\n",
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" 25 & 0.018094 & 133.481864 \\\\\n",
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" 80 & 0.000000 & NaN \\\\\n",
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" 100 & 0.000000 & NaN \\\\\n",
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" 101 & 66.479321 & 433.414195 \\\\\n",
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" 103 & 0.106377 & 1.645114 \\\\\n",
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" 105 & 0.463292 & 2.408090 \\\\\n",
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" 107 & 0.000000 & NaN \\\\\n",
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" 114 & 0.676897 & 1.003422 \\\\\n",
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" 115 & 4.117647 & 5.916852 \\\\\n",
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" 116 & 8.316438 & 1.109652 \\\\\n",
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" 117 & 0.000000 & NaN \\\\\n",
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" 118 & 0.311290 & 1.000000 \\\\\n",
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" 119 & 0.195997 & 2.555160 \\\\\n",
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" 170 & 0.000000 & NaN \\\\\n",
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" 199 & 0.000000 & NaN \\\\\n",
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" 200 & 30.916717 & 9.707524 \\\\\n",
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" 205 & 0.000000 & NaN \\\\\n",
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" 210 & 0.000000 & NaN \\\\\n",
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" 214 & 0.000000 & NaN \\\\\n",
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" 215 & 0.000000 & NaN \\\\\n",
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" 360 & 3.502999 & 1.612147 \\\\\n",
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" 450 & 0.612913 & 1.057515 \\\\\n",
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"\n",
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"}\n",
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"\\taskslowdown{Cluster C}{\n",
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"\\begin{tabular}{rrr}\n",
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"\\toprule\n",
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" priority & finished\\% & m\\_slow\\_2 \\\\\n",
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"\\midrule\n",
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" 0 & 50.887820 & 1.105787 \\\\\n",
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" 3 & 0.000000 & NaN \\\\\n",
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" 10 & 0.000000 & NaN \\\\\n",
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" 25 & 22.468276 & 8.191258 \\\\\n",
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" 100 & 0.000000 & NaN \\\\\n",
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" 101 & 52.628263 & 421.490544 \\\\\n",
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" 103 & 0.005336 & 2.794339 \\\\\n",
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" 105 & 0.023521 & 1.372291 \\\\\n",
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" 107 & 0.000245 & 14.708268 \\\\\n",
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" 114 & 0.022221 & 1.011266 \\\\\n",
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" 115 & 0.281832 & 1.980743 \\\\\n",
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" 116 & 0.013836 & 1.022119 \\\\\n",
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" 117 & 93.165468 & 1.000000 \\\\\n",
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" 118 & 0.004137 & 1.100009 \\\\\n",
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" 119 & 2.215917 & 2.044049 \\\\\n",
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" 170 & 0.000000 & NaN \\\\\n",
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" 200 & 3.606796 & 4.139724 \\\\\n",
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" 205 & 0.000000 & NaN \\\\\n",
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" 210 & 0.000000 & NaN \\\\\n",
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" 214 & 0.000000 & NaN \\\\\n",
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" 215 & 0.000000 & NaN \\\\\n",
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" 360 & 4.367418 & 2.061085 \\\\\n",
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" 450 & 1.512578 & 1.066014 \\\\\n",
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"\n",
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"}\n",
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"\\taskslowdown{Cluster D}{\n",
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"\\begin{tabular}{rrr}\n",
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"\\toprule\n",
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" priority & finished\\% & m\\_slow\\_2 \\\\\n",
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"\\midrule\n",
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" 0 & 26.522899 & 1.116002 \\\\\n",
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" 5 & 0.000000 & NaN \\\\\n",
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" 25 & 16.293068 & 65.676400 \\\\\n",
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" 100 & 0.000000 & NaN \\\\\n",
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" 101 & 45.314870 & 315.954065 \\\\\n",
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" 103 & 0.004540 & 1.065721 \\\\\n",
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" 105 & 0.051712 & 2.897040 \\\\\n",
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" 107 & 0.000350 & 1.551354 \\\\\n",
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" 114 & 0.000000 & NaN \\\\\n",
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" 115 & 5.189033 & 2.186562 \\\\\n",
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" 116 & 0.126154 & 1.278510 \\\\\n",
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" 117 & 85.714286 & 1.000000 \\\\\n",
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" 118 & 0.054055 & 2.048749 \\\\\n",
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" 119 & 0.441844 & 3.020486 \\\\\n",
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" 197 & 0.000000 & NaN \\\\\n",
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" 199 & 0.000000 & NaN \\\\\n",
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" 200 & 6.528759 & 5.514350 \\\\\n",
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" 205 & 0.000000 & NaN \\\\\n",
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" 210 & 0.000000 & NaN \\\\\n",
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" 214 & 0.000000 & NaN \\\\\n",
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" 215 & 0.000000 & NaN \\\\\n",
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" 360 & 1.594977 & 2.476706 \\\\\n",
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" 450 & 0.611145 & 1.330248 \\\\\n",
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"\n",
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"}\n",
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"\\taskslowdown{Cluster E}{\n",
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"\\begin{tabular}{rrr}\n",
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"\\toprule\n",
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" priority & finished\\% & m\\_slow\\_2 \\\\\n",
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"\\midrule\n",
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" 0 & 42.805214 & 1.439544 \\\\\n",
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" 25 & 5.344531 & 2.676136 \\\\\n",
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" 100 & 0.000000 & NaN \\\\\n",
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" 101 & 0.015918 & 1.122507 \\\\\n",
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" 103 & 0.021660 & 3.163046 \\\\\n",
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" 105 & 0.404803 & 14.750313 \\\\\n",
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" 107 & 0.000000 & NaN \\\\\n",
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" 114 & 0.000000 & NaN \\\\\n",
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" 115 & 0.027326 & 1.000000 \\\\\n",
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" 116 & 0.000000 & NaN \\\\\n",
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" 117 & 0.000000 & NaN \\\\\n",
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" 118 & 0.000000 & NaN \\\\\n",
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" 119 & 0.458256 & 10.310893 \\\\\n",
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" 170 & 0.000000 & NaN \\\\\n",
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" 200 & 1.959258 & 8.535722 \\\\\n",
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" 201 & 0.000000 & NaN \\\\\n",
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" 205 & 0.000000 & NaN \\\\\n",
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" 210 & 0.000000 & NaN \\\\\n",
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" 215 & 0.000000 & NaN \\\\\n",
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" 220 & 0.000000 & NaN \\\\\n",
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" 360 & 37.157031 & 2.873243 \\\\\n",
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" 450 & 0.548458 & 1.113283 \\\\\n",
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"\n",
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"}\n",
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"\\taskslowdown{Cluster F}{\n",
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"\\begin{tabular}{rrr}\n",
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"\\toprule\n",
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" priority & finished\\% & m\\_slow\\_2 \\\\\n",
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"\\midrule\n",
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" 0 & 45.208221 & 1.088162 \\\\\n",
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" 25 & 0.647505 & 2.230960 \\\\\n",
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" 100 & 0.000000 & NaN \\\\\n",
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" 101 & 40.296631 & 323.858714 \\\\\n",
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" 103 & 0.058418 & 1.167347 \\\\\n",
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" 105 & 0.222372 & 1.550453 \\\\\n",
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" 107 & 0.060860 & 1.012727 \\\\\n",
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" 114 & 0.006958 & 1.000000 \\\\\n",
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" 115 & 3.647104 & 5.094215 \\\\\n",
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" 116 & 0.000000 & NaN \\\\\n",
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" 117 & 0.000086 & 1.000000 \\\\\n",
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" 118 & 0.002082 & 1.000000 \\\\\n",
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" 119 & 31.354662 & 7.608799 \\\\\n",
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" 200 & 3.653528 & 5.943247 \\\\\n",
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" 201 & 0.000000 & NaN \\\\\n",
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" 360 & 7.424790 & 2.171524 \\\\\n",
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" 450 & 0.992623 & 1.021053 \\\\\n",
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"\n",
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"}\n",
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"\\taskslowdown{Cluster G}{\n",
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"\\begin{tabular}{rrr}\n",
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"\\toprule\n",
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" priority & finished\\% & m\\_slow\\_2 \\\\\n",
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"\\midrule\n",
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" 0 & 33.612201 & 1.138988 \\\\\n",
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" 25 & 0.233338 & 8.692558 \\\\\n",
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" 50 & 0.000000 & NaN \\\\\n",
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" 100 & 0.000000 & NaN \\\\\n",
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" 101 & 96.470338 & 19.378523 \\\\\n",
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" 103 & 0.032539 & 1.271282 \\\\\n",
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" 105 & 0.196286 & 1.000738 \\\\\n",
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" 107 & 0.000000 & NaN \\\\\n",
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" 114 & 0.000000 & NaN \\\\\n",
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" 115 & 7.633588 & 1.802068 \\\\\n",
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" 117 & 0.000000 & NaN \\\\\n",
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" 118 & 48.969072 & 3.877102 \\\\\n",
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" 119 & 0.085944 & 3.166077 \\\\\n",
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" 170 & 0.000000 & NaN \\\\\n",
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" 200 & 26.747126 & 14.573912 \\\\\n",
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" 360 & 1.618878 & 2.119524 \\\\\n",
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" 450 & 2.737219 & 1.036927 \\\\\n",
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"\n",
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"}\n",
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"\\taskslowdown{Cluster H}{\n",
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"\\begin{tabular}{rrr}\n",
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"\\toprule\n",
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" priority & finished\\% & m\\_slow\\_2 \\\\\n",
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"\\midrule\n",
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" 0 & 27.744380 & 1.122458 \\\\\n",
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" 19 & 0.000000 & NaN \\\\\n",
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" 25 & 1.042767 & 3.064188 \\\\\n",
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" 101 & 100.000000 & 76.438090 \\\\\n",
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" 103 & 0.481256 & 1.262067 \\\\\n",
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" 105 & 1.427256 & 4.205547 \\\\\n",
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" 107 & 0.000000 & NaN \\\\\n",
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" 115 & 5.122494 & 1.000000 \\\\\n",
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" 116 & 1.035309 & 73.447995 \\\\\n",
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" 117 & 0.000050 & 1.000000 \\\\\n",
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" 118 & 1.003331 & 1.947121 \\\\\n",
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" 119 & 0.145214 & 7.301093 \\\\\n",
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" 200 & 2.702770 & 5.798142 \\\\\n",
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" 201 & 0.000000 & NaN \\\\\n",
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" 220 & 0.000000 & NaN \\\\\n",
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" 360 & 4.425746 & 2.018441 \\\\\n",
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" 450 & 0.535389 & 1.054678 \\\\\n",
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"\\bottomrule\n",
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"\\end{tabular}\n",
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"\n",
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"}\n"
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]
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}
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],
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"source": [
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"CLUSTERS = \"abcdefgh\"\n",
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"DIR = \"/Users/maggicl/Git/bachelorThesis/task_slowdown/\"\n",
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"\n",
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"df = {}\n",
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"\n",
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"for cluster in CLUSTERS:\n",
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" print(\"\\\\taskslowdown{Cluster \" + cluster.upper() + \"}{\")\n",
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" df[cluster] = pandas.read_csv(DIR + \"/\" + cluster + \"_slowdown_table.csv\")\n",
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" df[cluster][\"m_slow_2\"] = (df[cluster][\"s_all\"] / df[cluster][\"s_last\"]) \n",
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" del df[cluster][\"s_slow\"]\n",
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" del df[cluster][\"m_slow\"]\n",
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" del df[cluster][\"m_all\"]\n",
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" del df[cluster][\"m_last\"]\n",
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" del df[cluster][\"s_all\"]\n",
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" del df[cluster][\"s_last\"]\n",
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" del df[cluster][\"c_zero_end\"]\n",
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" del df[cluster][\"n_fsh\"]\n",
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" del df[cluster][\"n_non\"]\n",
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" del df[cluster][\"Unnamed: 0\"]\n",
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" df[cluster][\"finished%\"] *= 100\n",
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" print(df[cluster].to_latex(index=False))\n",
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" print(\"}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"wc"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"tc"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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