diff --git a/expe_replay_feedback_metacentrum.ipynb b/expe_replay_feedback_metacentrum.ipynb
index 8d54652b536969909704939a48d1d84b82d74198..bdbe50b271b5f10a6726b92e9fa69744179c808c 100644
--- a/expe_replay_feedback_metacentrum.ipynb
+++ b/expe_replay_feedback_metacentrum.ipynb
@@ -12,7 +12,356 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Metacentrum\n",
+ "# Metacentrum\n",
+ "\n",
+ "## Initializing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# For easily changing the workload\n",
+ "EXPE_DIR = \"out/expe_replay_MC\"\n",
+ "PF_folder = \"platform/average_metacentrum\"\n",
+ "WL_folder = \"workload/MC\"\n",
+ "WL_full_swf_path = \"/home/mael/git/middleware_generators/workload/input/METACENTRUM-2013-3.swf\"\n",
+ "begin_data_in_swf = 109 # line number (counting from 1)\n",
+ "\n",
+ "WL_filtered2013_swf_path = \"/home/mael/git/middleware_generators/workload/output/METACENTRUM-2013_filtered.swf\"\n",
+ "WL_filtered2014_swf_path = \"/home/mael/git/middleware_generators/workload/output/METACENTRUM-2014_filtered.swf\"\n",
+ "\n",
+ "# Original log params\n",
+ "# WL_URL = \"http://www.cs.huji.ac.il/labs/parallel/workload/l_sdsc_sp2/SDSC-SP2-1998-4.swf.gz\"\n",
+ "# WL_start_time = '1998-04-24 18:11:04'\n",
+ "# timezone=\"US/Pacific\"\n",
+ "\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "header=[\n",
+ " \"JOB_ID\",\"SUBMIT_TIME\",\"WAIT_TIME\",\"RUN_TIME\",\"ALLOCATED_PROCESSOR_COUNT\",\"AVERAGE_CPU_TIME_USED\",\"USED_MEMORY\",\n",
+ " \"REQUESTED_NUMBER_OF_PROCESSORS\",\"REQUESTED_TIME\",\"REQUESTED_MEMORY\",\"STATUS\",\"USER_ID\",\"GROUP_ID\",\"APPLICATION_ID\", \n",
+ " \"QUEUD_ID\",\"PARTITION_ID\",\"PRECEDING_JOB_ID\",\"THINK_TIME_FROM_PRECEDING_JOB\"]\n",
+ "\n",
+ "# WL_full = pd.read_csv(WL_full_swf_path, header=begin_data_in_swf-2, delim_whitespace=True, names=header)\n",
+ "WL_filtered2013 = pd.read_csv(WL_filtered2013_swf_path, delim_whitespace=True, names=header)\n",
+ "WL_filtered2014 = pd.read_csv(WL_filtered2014_swf_path, delim_whitespace=True, names=header)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "-1 765986\n",
+ "Name: STATUS, dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "WL_filtered[\"finish_time\"] = WL_filtered.SUBMIT_TIME + WL_filtered.WAIT_TIME + WL_filtered.RUN_TIME\n",
+ "WL_filtered[\"turnaround_time\"] = WL_filtered.WAIT_TIME + WL_filtered.RUN_TIME\n",
+ "\n",
+ "print(WL_filtered.STATUS.value_counts())\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Simulation results:\n"
+ ]
+ },
+ {
+ "data": {
+ "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></th>\n",
+ " <th>makespan</th>\n",
+ " <th>length</th>\n",
+ " <th>nb_jobs</th>\n",
+ " <th>mean_waiting_time</th>\n",
+ " <th>median_waiting_time</th>\n",
+ " <th>max_waiting_time</th>\n",
+ " <th>mean_slowdown</th>\n",
+ " <th>max_slowdown</th>\n",
+ " <th>mean_turnaround_time</th>\n",
+ " <th>max_turnaround_time</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2013_filtered</th>\n",
+ " <th>0</th>\n",
+ " <td>39968099</td>\n",
+ " <td>31535248</td>\n",
+ " <td>765986</td>\n",
+ " <td>20205.083068</td>\n",
+ " <td>1259.0</td>\n",
+ " <td>18163266</td>\n",
+ " <td>801.890002</td>\n",
+ " <td>4879140.5</td>\n",
+ " <td>23984.826289</td>\n",
+ " <td>18163288</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2014_filtered</th>\n",
+ " <th>0</th>\n",
+ " <td>39127717</td>\n",
+ " <td>31449352</td>\n",
+ " <td>1212560</td>\n",
+ " <td>19429.814337</td>\n",
+ " <td>229.0</td>\n",
+ " <td>7829272</td>\n",
+ " <td>1521.444489</td>\n",
+ " <td>2636706.0</td>\n",
+ " <td>22160.752967</td>\n",
+ " <td>7856812</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " makespan length nb_jobs mean_waiting_time \\\n",
+ "2013_filtered 0 39968099 31535248 765986 20205.083068 \n",
+ "2014_filtered 0 39127717 31449352 1212560 19429.814337 \n",
+ "\n",
+ " median_waiting_time max_waiting_time mean_slowdown \\\n",
+ "2013_filtered 0 1259.0 18163266 801.890002 \n",
+ "2014_filtered 0 229.0 7829272 1521.444489 \n",
+ "\n",
+ " max_slowdown mean_turnaround_time max_turnaround_time \n",
+ "2013_filtered 0 4879140.5 23984.826289 18163288 \n",
+ "2014_filtered 0 2636706.0 22160.752967 7856812 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Simulation results (with readable durations):\n"
+ ]
+ },
+ {
+ "data": {
+ "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></th>\n",
+ " <th>makespan</th>\n",
+ " <th>length</th>\n",
+ " <th>nb_jobs</th>\n",
+ " <th>mean_waiting_time</th>\n",
+ " <th>median_waiting_time</th>\n",
+ " <th>max_waiting_time</th>\n",
+ " <th>mean_slowdown</th>\n",
+ " <th>max_slowdown</th>\n",
+ " <th>mean_turnaround_time</th>\n",
+ " <th>max_turnaround_time</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>2013_filtered</th>\n",
+ " <th>0</th>\n",
+ " <td>462 days 14:14:59</td>\n",
+ " <td>364 days 23:47:28</td>\n",
+ " <td>765986</td>\n",
+ " <td>0 days 05:36:45</td>\n",
+ " <td>0 days 00:20:59</td>\n",
+ " <td>210 days 05:21:06</td>\n",
+ " <td>801.890002</td>\n",
+ " <td>4879140.5</td>\n",
+ " <td>0 days 06:39:44</td>\n",
+ " <td>210 days 05:21:28</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2014_filtered</th>\n",
+ " <th>0</th>\n",
+ " <td>452 days 20:48:37</td>\n",
+ " <td>363 days 23:55:52</td>\n",
+ " <td>1212560</td>\n",
+ " <td>0 days 05:23:49</td>\n",
+ " <td>0 days 00:03:49</td>\n",
+ " <td>90 days 14:47:52</td>\n",
+ " <td>1521.444489</td>\n",
+ " <td>2636706.0</td>\n",
+ " <td>0 days 06:09:20</td>\n",
+ " <td>90 days 22:26:52</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " makespan length nb_jobs \\\n",
+ "2013_filtered 0 462 days 14:14:59 364 days 23:47:28 765986 \n",
+ "2014_filtered 0 452 days 20:48:37 363 days 23:55:52 1212560 \n",
+ "\n",
+ " mean_waiting_time median_waiting_time max_waiting_time \\\n",
+ "2013_filtered 0 0 days 05:36:45 0 days 00:20:59 210 days 05:21:06 \n",
+ "2014_filtered 0 0 days 05:23:49 0 days 00:03:49 90 days 14:47:52 \n",
+ "\n",
+ " mean_slowdown max_slowdown mean_turnaround_time \\\n",
+ "2013_filtered 0 801.890002 4879140.5 0 days 06:39:44 \n",
+ "2014_filtered 0 1521.444489 2636706.0 0 days 06:09:20 \n",
+ "\n",
+ " max_turnaround_time \n",
+ "2013_filtered 0 210 days 05:21:28 \n",
+ "2014_filtered 0 90 days 22:26:52 "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "header=[\n",
+ " \"JOB_ID\",\"SUBMIT_TIME\",\"WAIT_TIME\",\"RUN_TIME\",\"ALLOCATED_PROCESSOR_COUNT\",\"AVERAGE_CPU_TIME_USED\",\"USED_MEMORY\",\n",
+ " \"REQUESTED_NUMBER_OF_PROCESSORS\",\"REQUESTED_TIME\",\"REQUESTED_MEMORY\",\"STATUS\",\"USER_ID\",\"GROUP_ID\",\"APPLICATION_ID\", \n",
+ " \"QUEUD_ID\",\"PARTITION_ID\",\"PRECEDING_JOB_ID\",\"THINK_TIME_FROM_PRECEDING_JOB\"]\n",
+ "\n",
+ "# WL_full = pd.read_csv(WL_full_swf_path, header=begin_data_in_swf-2, delim_whitespace=True, names=header)\n",
+ "WL_filtered2013 = pd.read_csv(WL_filtered2013_swf_path, delim_whitespace=True, names=header)\n",
+ "WL_filtered2014 = pd.read_csv(WL_filtered2014_swf_path, delim_whitespace=True, names=header)\n",
+ "\n",
+ "dfs = []\n",
+ "\n",
+ "for WL_swf in [WL_filtered2013, WL_filtered2014]:\n",
+ " WL_swf[\"finish_time\"] = WL_swf.SUBMIT_TIME + WL_swf.WAIT_TIME + WL_swf.RUN_TIME\n",
+ " WL_swf[\"turnaround_time\"] = WL_swf.WAIT_TIME + WL_swf.RUN_TIME\n",
+ " WL_swf[\"slowdown\"] = WL_swf.turnaround_time / WL_swf.RUN_TIME.replace(0, 1)\n",
+ "\n",
+ " original_metrics = pd.DataFrame.from_dict({\n",
+ " \"nb_jobs\": [WL_swf.shape[0]],\n",
+ " # \"nb_jobs_success\": WL_swf.STATUS.value_counts()[1],\n",
+ " \"mean_waiting_time\": WL_swf.WAIT_TIME.mean(),\n",
+ " \"median_waiting_time\": WL_swf.WAIT_TIME.median(),\n",
+ " \"max_waiting_time\": WL_swf.WAIT_TIME.max(),\n",
+ " \"makespan\": WL_swf.finish_time.max() - WL_swf.SUBMIT_TIME.min(),\n",
+ " \"length\": WL_swf.SUBMIT_TIME.max() - WL_swf.SUBMIT_TIME.min(),\n",
+ " \"mean_slowdown\": WL_swf.slowdown.mean(),\n",
+ " \"max_slowdown\": WL_swf.slowdown.max(),\n",
+ " \"mean_turnaround_time\": WL_swf.turnaround_time.mean(),\n",
+ " \"max_turnaround_time\": WL_swf.turnaround_time.max()\n",
+ " })\n",
+ " dfs.append(original_metrics)\n",
+ "\n",
+ "all = pd.concat(dfs, keys=[\"2013_filtered\", \"2014_filtered\"])\n",
+ "\n",
+ "print(\"Simulation results:\")\n",
+ "display(all[[\"makespan\",\"length\",\"nb_jobs\",\"mean_waiting_time\",\"median_waiting_time\",\"max_waiting_time\",\"mean_slowdown\",\"max_slowdown\",\"mean_turnaround_time\",\"max_turnaround_time\"]])\n",
+ "\n",
+ "print(\"Simulation results (with readable durations):\")\n",
+ "all = all.astype({\"makespan\":'timedelta64[s]',\"length\":'timedelta64[s]',\n",
+ " \"mean_waiting_time\":'timedelta64[s]',\"max_waiting_time\":'timedelta64[s]',\"median_waiting_time\":'timedelta64[s]',\n",
+ " \"mean_turnaround_time\":'timedelta64[s]',\"max_turnaround_time\":'timedelta64[s]'})\n",
+ "display(all[[\"makespan\",\"length\",\"nb_jobs\",\"mean_waiting_time\",\"median_waiting_time\",\"max_waiting_time\",\"mean_slowdown\",\"max_slowdown\",\"mean_turnaround_time\",\"max_turnaround_time\"]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[<matplotlib.axis.YTick at 0x7fb62f453700>,\n",
+ " <matplotlib.axis.YTick at 0x7fb62f452f80>,\n",
+ " <matplotlib.axis.YTick at 0x7fb62f51a620>,\n",
+ " <matplotlib.axis.YTick at 0x7fb62f2a8100>,\n",
+ " <matplotlib.axis.YTick at 0x7fb62f2a8580>,\n",
+ " <matplotlib.axis.YTick at 0x7fb62f2a8cd0>,\n",
+ " <matplotlib.axis.YTick at 0x7fb62f2a9420>,\n",
+ " <matplotlib.axis.YTick at 0x7fb62f2a9b70>,\n",
+ " <matplotlib.axis.YTick at 0x7fb62f2aa2c0>]"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig,ax=plt.subplots()\n",
+ "WL_filtered2013.WAIT_TIME.plot(kind=\"box\", showmeans=True, showfliers=False, ax=ax)\n",
+ "y_l, y_r = ax.get_ylim(); y_l, y_r = int(y_l / (3600)), int(y_r / (3600))\n",
+ "ax.set_yticks(np.arange(y_l * 3600, (y_r+1) * 3600, step=3600), labels=np.arange(y_l, y_r+1, step=1)) "
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
"Simulation launched in grid5000."
]
},
@@ -314,8 +663,22 @@
}
],
"metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
"language_info": {
- "name": "python"
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.8"
},
"orig_nbformat": 4
},