{ "cells": [ { "cell_type": "markdown", "id": "handy-organization", "metadata": {}, "source": [ "# Task slowdown" ] }, { "cell_type": "code", "execution_count": 2, "id": "guided-excellence", "metadata": {}, "outputs": [], "source": [ "import json\n", "import sys\n", "import gzip\n", "import pandas\n", "import seaborn as sns\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "from IPython.display import display, HTML" ] }, { "cell_type": "markdown", "id": "august-lobby", "metadata": {}, "source": [ "### Legend for columns:\n", "- *n_fsh*: number of jobs that terminated with status 6 (FINISH)\n", "- *n_non*: number of jobs that did not terminate with status 6 or did not terminate at all\n", "- *finished%*: `n_fsh` / (`n_fsh` + `n_non`)\n", "- *c_zero_end*: count of number of **Finished** jobs that have a last execution time of `0` \n", "- *s_last*: sum of execution times for last events\n", "- *m_last*: mean execution time for last event\n", "- *s_all*: sum of all execution times for all events\n", "- *m_all*: mean execution time for all events\n", "- *s_slow*: sum of **slowdown** values computed for each job: `job_slowdown` = sum(`exec_time`) / last(`exec_time`)\n", "- *m_slow*: mean job-wise **slowdown** value, i.e. `s_slow` / `n_fsh`\n", "- *m_slow_2*: priority-wise mean **slowdown**, i.e. `s_all` / `s_last`" ] }, { "cell_type": "code", "execution_count": 6, "id": "revised-mediterranean", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cluster a:\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " Unnamed: 0 priority n_fsh n_non finished% c_zero_end \\\n", "0 0 0 6626426 17257424 2.774438e-01 0.0 \n", "1 1 19 0 10 0.000000e+00 NaN \n", "2 2 25 79 7497 1.042767e-02 0.0 \n", "3 3 101 25000 0 1.000000e+00 0.0 \n", "4 4 103 24112 4986108 4.812563e-03 0.0 \n", "5 5 105 10000 690645 1.427256e-02 0.0 \n", "6 6 107 0 146547 0.000000e+00 NaN \n", "7 7 115 23 426 5.122494e-02 0.0 \n", "8 8 116 134 12809 1.035309e-02 0.0 \n", "9 9 117 1 2013415 4.966683e-07 0.0 \n", "10 10 118 506 49926 1.003331e-02 0.0 \n", "11 11 119 3970 2729917 1.452145e-03 0.0 \n", "12 12 200 57852 2082619 2.702770e-02 21.0 \n", "13 13 201 0 363 0.000000e+00 NaN \n", "14 14 220 0 360 0.000000e+00 NaN \n", "15 15 360 1862 40210 4.425746e-02 1.0 \n", "16 16 450 91 16906 5.353886e-03 0.0 \n", "\n", " s_last m_last s_all m_all s_slow \\\n", "0 3.750671e+16 5.663232e+09 4.209970e+16 6.211185e+09 9.556217e+11 \n", "1 NaN NaN NaN NaN NaN \n", "2 2.408477e+13 3.048705e+11 7.380026e+13 2.838472e+11 9.556237e+11 \n", "3 8.515792e+14 3.406317e+10 6.509309e+16 1.224965e+10 1.012711e+12 \n", "4 6.553604e+15 2.726352e+11 8.271087e+15 9.289390e+10 1.012712e+12 \n", "5 6.117631e+15 6.117631e+11 2.572798e+16 3.937615e+11 1.012750e+12 \n", "6 NaN NaN NaN NaN NaN \n", "7 6.648578e+11 2.890686e+10 6.648578e+11 2.890686e+10 1.012750e+12 \n", "8 8.484331e+10 6.331590e+08 6.231571e+12 4.210521e+10 1.012751e+12 \n", "9 6.557861e+08 6.557861e+08 6.557861e+08 6.557861e+08 1.012751e+12 \n", "10 1.000296e+14 1.976869e+11 1.947697e+14 1.391212e+11 1.012751e+12 \n", "11 5.554317e+14 1.399072e+11 4.055259e+15 1.555807e+10 1.246604e+12 \n", "12 1.728555e+16 2.987994e+11 1.002241e+17 4.707830e+11 1.713280e+13 \n", "13 NaN NaN NaN NaN NaN \n", "14 NaN NaN NaN NaN NaN \n", "15 1.561695e+15 8.387189e+11 3.152188e+15 3.973012e+11 1.713283e+13 \n", "16 1.338225e+14 1.470577e+12 1.411397e+14 1.411397e+12 1.713283e+13 \n", "\n", " m_slow m_slow_2 \n", "0 1.442917e+05 1.122458 \n", "1 NaN NaN \n", "2 1.209650e+10 3.064188 \n", "3 4.050842e+07 76.438090 \n", "4 4.212964e+07 1.262067 \n", "5 1.012750e+08 4.205547 \n", "6 NaN NaN \n", "7 4.403262e+10 1.000000 \n", "8 7.557840e+09 73.447995 \n", "9 1.012751e+12 1.000000 \n", "10 2.001483e+09 1.947121 \n", "11 3.140059e+08 7.301093 \n", "12 2.961590e+08 5.798142 \n", "13 NaN NaN \n", "14 NaN NaN \n", "15 9.201306e+09 2.018441 \n", "16 1.882729e+11 1.054678 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "CLUSTERS = \"abcdefgh\"\n", "DIR = \"/home/claudio/google_2019/thesis_queries/task_slowdown/\"\n", "\n", "df = {}\n", "\n", "for cluster in CLUSTERS:\n", " print(\"Cluster \" + cluster + \":\")\n", " df[cluster] = pandas.read_csv(DIR + \"/\" + cluster + \"_slowdown_table.csv\")\n", " df[cluster][\"m_slow_2\"] = (df[cluster][\"s_all\"] / df[cluster][\"s_last\"]) \n", " display(df[cluster])" ] }, { "cell_type": "code", "execution_count": null, "id": "physical-function", "metadata": {}, "outputs": [], "source": [ "wc" ] }, { "cell_type": "code", "execution_count": null, "id": "checked-playing", "metadata": {}, "outputs": [], "source": [ "tc" ] }, { "cell_type": "code", "execution_count": null, "id": "talented-realtor", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 5 }