mean_memory_utilization_nodes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from plotnine import *\n",
    "from datetime import datetime\n",
    "import pytz\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "memory_free = pd.read_parquet(\"path to machine metric dataset/node_memory_MemFree/\")\n",
    "memory_free = memory_free / (1024 * 1024 * 1024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "memory_total = pd.read_parquet(\"path to machine metric dataset/node_memory_MemTotal/\")\n",
    "memory_total = memory_total / (1024 * 1024 * 1024)\n",
    "# print(memory_total.values.max())\n",
    "# print(len(memory_total.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r23n23\n",
      "r23n26\n"
     ]
    }
   ],
   "source": [
    "for c in memory_total.columns:\n",
    "    if memory_total[c].max() >= 257:\n",
    "        print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000.2234001159668\n"
     ]
    }
   ],
   "source": [
    "mem_used_df = pd.read_parquet(\"path to machine metric dataset/node_memory_MemTotal%20-%20node_memory_MemFree%20-%20node_memory_Buffers%20-%20node_memory_Cached%20-%20node_memory_Slab%20-%20node_memory_PageTables%20-%20node_memory_SwapCached/\")\n",
    "mem_used_df = mem_used_df / (1024 * 1024 * 1024)\n",
    "print(mem_used_df.max().max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# memory_used_fraction_df = 1 - (df / mem_total_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2880x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_memory_per_node = mem_used_df.mean() * 100\n",
    "\n",
    "# TODO: rewrite this. Devide based on index and the column names to get the true utilization levels.\n",
    "fig = plt.figure(figsize=(40,12))\n",
    "ax = mean_memory_per_node.plot.hist(bins=math.ceil(mean_memory_per_node.max()))\n",
    "ax.set_xlim(0,)\n",
    "ax.set_xlabel(\"Mean Memory Utilization (%)\", fontsize=40)\n",
    "ax.set_ylabel(\"Frequency\", fontsize=40)\n",
    "ax.tick_params(axis='both', which='major', labelsize=40)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=32)\n",
    "ax.set_title(\"Histogram of Mean Memory Utilization in LISA\", fontsize=50)\n",
    "plt.savefig(\"mean_memory_utilization_percentage.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(df):\n",
    "    df = df.value_counts(sort=False, normalize=True).rename_axis('target').reset_index(name='pdf')\n",
    "    df[\"cdf\"] = df[\"pdf\"].cumsum()\n",
    "    return df\n",
    "\n",
    "all_values_df = pd.DataFrame({\"target\": mem_used_df.values.ravel()})\n",
    "count_df = normalize(all_values_df)\n",
    "del all_values_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add a row at the start so that the CDF starts at 0 and ends at 1 (in case we only have one datapoint in the DF)\n",
    "plot_df = count_df.copy()\n",
    "plot_df.index = plot_df.index + 1  # shifting index\n",
    "plot_df.reset_index(inplace=True)\n",
    "plot_df['index'] = plot_df['index'] + 1\n",
    "plot_df.loc[0] = [0, -math.inf, 0, 0]  # add a row  at the start (index, count, pdf, cdf)\n",
    "plot_df.loc[len(plot_df)] = [len(plot_df), math.inf, 1, 1]\n",
    "plot_df.sort_index(inplace=True)\n",
    "\n",
    "ggplt = ggplot(plot_df) + \\\n",
    "            theme_light(base_size=18) + \\\n",
    "            theme(figure_size = (8,3)) + \\\n",
    "            geom_step(aes(x=\"target\", y=\"cdf\"), size=1) +\\\n",
    "            xlab(\"Node RAM Usage [GB]\") + \\\n",
    "            ylab(\"ECDF\")\n",
    "\n",
    "\n",
    "marker_x = [96, 192, 256, 1024, 2048]\n",
    "marker_y = []\n",
    "for i in marker_x:\n",
    "    marker_y.append(count_df[count_df['target'].le(i) | np.isclose(count_df['target'], i, rtol=1e-10, atol=1e-12)].tail(1)['cdf'].iloc[0])\n",
    "\n",
    "\n",
    "marker_labels = [\"96GB\", \"192GB\", \"256GB\", \"1TB\", \"2TB\"]\n",
    "markers = ['o', 'v', 'p', 's', 'd']\n",
    "\n",
    "fig = ggplt.draw(return_ggplot=False)\n",
    "ax = plt.gca()\n",
    "for x_pos, y_pos, marker_symbol, marker_label in zip(marker_x, marker_y, markers, marker_labels):\n",
    "    ax.scatter(x_pos, y_pos, marker=marker_symbol, label=marker_label, facecolors=\"None\", edgecolors=\"black\")\n",
    "ax.legend(ncol=len(marker_x), loc=4, prop={'size': 10})\n",
    "\n",
    "date_time = datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"memory_usage_cdf_nodes_{}.pdf\".format(date_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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>target</th>\n",
       "      <th>pdf</th>\n",
       "      <th>cdf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.277943</td>\n",
       "      <td>7.901544e-09</td>\n",
       "      <td>7.901544e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.278988</td>\n",
       "      <td>7.901544e-09</td>\n",
       "      <td>1.580309e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.279865</td>\n",
       "      <td>7.901544e-09</td>\n",
       "      <td>2.370463e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.279869</td>\n",
       "      <td>7.901544e-09</td>\n",
       "      <td>3.160618e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.280285</td>\n",
       "      <td>7.901544e-09</td>\n",
       "      <td>3.950772e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12111136</th>\n",
       "      <td>1999.837837</td>\n",
       "      <td>7.901544e-09</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12111137</th>\n",
       "      <td>1999.850060</td>\n",
       "      <td>7.901544e-09</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12111138</th>\n",
       "      <td>2000.201683</td>\n",
       "      <td>7.901544e-09</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12111139</th>\n",
       "      <td>2000.222832</td>\n",
       "      <td>7.901544e-09</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12111140</th>\n",
       "      <td>2000.223400</td>\n",
       "      <td>7.901544e-09</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12111141 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               target           pdf           cdf\n",
       "0            0.277943  7.901544e-09  7.901544e-09\n",
       "1            0.278988  7.901544e-09  1.580309e-08\n",
       "2            0.279865  7.901544e-09  2.370463e-08\n",
       "3            0.279869  7.901544e-09  3.160618e-08\n",
       "4            0.280285  7.901544e-09  3.950772e-08\n",
       "...               ...           ...           ...\n",
       "12111136  1999.837837  7.901544e-09  1.000000e+00\n",
       "12111137  1999.850060  7.901544e-09  1.000000e+00\n",
       "12111138  2000.201683  7.901544e-09  1.000000e+00\n",
       "12111139  2000.222832  7.901544e-09  1.000000e+00\n",
       "12111140  2000.223400  7.901544e-09  1.000000e+00\n",
       "\n",
       "[12111141 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{table}[]\n",
      "\\caption{RAM usage (GB) per percentage within Lisa.}\n",
      "\\label{surfing:tbl:ram-usage-percentiles}\n",
      "\\begin{tabular}{@{}lrrrrrrr@{}}\n",
      "\\toprule\n",
      "    & 1\\% & 25\\% & 50\\% & 75\\% & 90\\% & 99\\% & 100\\% \\\\ \\midrule\n",
      "RAM & 0.64    & 1.46     & 3.65     & 8.07     & 20.99     & 58.06    & 2000.22 \\\\ \\bottomrule\n",
      "\\end{tabular}\n",
      "\\end{table}\n"
     ]
    }
   ],
   "source": [
    "t = '''\\\\begin{{table}}[]\n",
    "\\\\caption{{RAM usage (GB) per percentage within Lisa.}}\n",
    "\\\\label{{surfing:tbl:ram-usage-percentiles}}\n",
    "\\\\begin{{tabular}}{{@{{}}lrrrrrrr@{{}}}}\n",
    "\\\\toprule\n",
    "    & 1\\\\% & 25\\\\% & 50\\\\% & 75\\\\% & 90\\\\% & 99\\\\% & 100\\\\% \\\\\\\\ \\\\midrule\n",
    "RAM & {0}    & {1}     & {2}     & {3}     & {4}     & {5}    & {6} \\\\\\\\ \\\\bottomrule\n",
    "\\\\end{{tabular}}\n",
    "\\\\end{{table}}'''\n",
    "\n",
    "# print(count_df[-1:]['cdf'] - 1.0)\n",
    "# print(count_df[-1:]['cdf'] >= 1.0)\n",
    "# print(count_df[-1:]['cdf'].ge(1.0))\n",
    "# print(np.isclose(count_df[-5:]['cdf'], 1.0, rtol=1e-10, atol=1e-12))\n",
    "# print(count_df[count_df['cdf'].ge(0.25) | np.isclose(count_df['cdf'], .25, rtol=1e-10, atol=1e-12)].iloc[0])\n",
    "\n",
    "percentages = [0.01, 0.25, 0.50, 0.75, 0.90, 0.99, 1]\n",
    "values = []\n",
    "\n",
    "for p in percentages:\n",
    "    values.append(count_df[count_df['cdf'].ge(p) | np.isclose(count_df['cdf'], p, rtol=1e-8, atol=1e-12)].iloc[0]['target'])\n",
    "\n",
    "print(t.format(*[\"{:.2f}\".format(v) for v in values]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{table}[]\n",
      "\\caption{Fraction of RAM usage below or at specified level.}\n",
      "\\label{surfing:tbl:ram-usage-fraction}\n",
      "\\begin{tabular}{@{}lrrrrr@{}}\n",
      "\\toprule\n",
      "    & 96 & 192 & 256 & & 1024 & 2048 \\\\ \\midrule\n",
      "Percentage & 1.00    & 1.00     & 1.00     & 1.00     & 1.00    \\\\ \\bottomrule\n",
      "\\end{tabular}\n",
      "\\end{table}\n"
     ]
    }
   ],
   "source": [
    "t = '''\\\\begin{{table}}[]\n",
    "\\\\caption{{Fraction of RAM usage below or at specified level.}}\n",
    "\\\\label{{surfing:tbl:ram-usage-fraction}}\n",
    "\\\\begin{{tabular}}{{@{{}}lrrrrrr@{{}}}}\n",
    "\\\\toprule\n",
    "    & 96GB & 192GB & 256GB & 1TB & 2TB \\\\\\\\ \\\\midrule\n",
    "Percentage & {0}    & {1}     & {2}     & {3}     & {4}    \\\\\\\\ \\\\bottomrule\n",
    "\\\\end{{tabular}}\n",
    "\\\\end{{table}}'''\n",
    "\n",
    "RAMs = [96, 192, 256, 1024, 2048]\n",
    "values = []\n",
    "\n",
    "for r in RAMs:\n",
    "    values.append(count_df[count_df['target'].le(r) | np.isclose(count_df['target'], r, rtol=1e-10, atol=1e-12)].tail(1)['cdf'].iloc[0])\n",
    "\n",
    "print(t.format(*[\"{:.2f}\".format(v) for v in values]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This cell outputs the special node 1128 in rack 1128, the 2TB RAM node\n",
    "# Unobfuscated it is r23n23\n",
    "memory_free_1128 = memory_free['r23n23']\n",
    "memory_total_1128 = memory_total['r23n23']\n",
    "\n",
    "df_1128 =  memory_free_1128.to_frame(name = 'free').join(memory_total_1128.to_frame(name='total')).replace(-1, np.NaN)\n",
    "\n",
    "df_1128['util'] = 1 - (df_1128['free'] / df_1128['total'])\n",
    "df_1128[\"dt\"] = pd.to_datetime(df_1128.index, utc=True, unit=\"s\")\n",
    "df_1128[\"dt\"] = df_1128[\"dt\"].dt.tz_convert(pytz.timezone('Europe/Amsterdam')).dt.tz_localize(None)\n",
    "df_1128 = df_1128.set_index(\"dt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dt\n",
       "2019-12-30 07:00:15    0.052183\n",
       "2019-12-30 07:00:30    0.052183\n",
       "2019-12-30 07:00:45    0.052180\n",
       "2019-12-30 07:01:00    0.052180\n",
       "2019-12-30 07:01:15    0.052179\n",
       "                         ...   \n",
       "2020-05-19 23:58:45    0.000968\n",
       "2020-05-19 23:59:00    0.000968\n",
       "2020-05-19 23:59:15    0.000968\n",
       "2020-05-19 23:59:30    0.000968\n",
       "2020-05-19 23:59:45    0.000968\n",
       "Name: util, Length: 805199, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1128['util']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2740170263408781 15.536085501122136\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_converted_xticks(ax):\n",
    "    \"\"\"\n",
    "    :param ax:\n",
    "    :return list of day and month strings\n",
    "    \"\"\"\n",
    "    return [pd.to_datetime(tick.get_text()).date().strftime(\"%d\\n%b\") for tick in ax.get_xticklabels()]\n",
    "\n",
    "median = df_1128['util'].median() * 100\n",
    "median_zeroes_filtered = df_1128['util'][df_1128['util'] > 0].median() * 100\n",
    "avg = df_1128['util'].mean() * 100\n",
    "\n",
    "print(median, avg)\n",
    "# print(df_1128['util'])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11, 5))\n",
    "ax.plot(df_1128['util'] * 100, color=\"lightcoral\")\n",
    "ax.set_ylabel(\"RAM Utilization [%]\", fontsize=18)\n",
    "ax.set_xlabel(\"\", fontsize=14)\n",
    "ax.tick_params(axis='both', which='major', labelsize=16)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=16)\n",
    "\n",
    "ax.axhline(avg, label=\"Average ({:.3f})\".format(avg), color=\"steelblue\", linestyle=\"solid\")\n",
    "ax.axhline(median, label=\"Median ({:.3f})\".format(median), color=\"yellowgreen\", linestyle=\"dashed\")\n",
    "ax.axhline(median_zeroes_filtered, label=\"Median zeros filtered ({:.3f})\".format(median_zeroes_filtered), color=\"black\", linestyle=\"dotted\")\n",
    "\n",
    "ax.legend(ncol=3, prop={\"size\": 14}, bbox_to_anchor=(0.5, 1.15), loc=9)\n",
    "\n",
    "fig.tight_layout()\n",
    "# This call needs to be after the tight_layout call so that the labels have been set!\n",
    "ax.set_xticklabels(get_converted_xticks(ax))\n",
    "\n",
    "date_time = datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\")\n",
    "fig.savefig(\"ram_utilization_node1128_{}.pdf\".format(date_time), bbox_inches='tight')"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}