2_1_allocs_usages_over_years.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "-  Through the comparison of yearly allocated cpu given to users, their usage and total available cpu, we witness an obvious declined usage portion with the increase of year. \n",
    "- Additionally, the allocations are much lower than available resources in recent 2 years, indicating there is only not under-usage but also insufficient utilization of available resources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%run include/required_libs.py\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cycle</th>\n",
       "      <th>allocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>9245000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>25829000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>43630671.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>51736375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>84270426.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>106894557.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cycle   allocation\n",
       "0   2013    9245000.0\n",
       "1   2014   25829000.0\n",
       "2   2015   43630671.0\n",
       "3   2016   51736375.0\n",
       "4   2017   84270426.0\n",
       "5   2018  106894557.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alloc = pd.read_csv('rawdata/allocs_by_cycle_group.csv')\n",
    "alloc = alloc.groupby('cycle')['allocation'].sum().to_frame()\n",
    "alloc = alloc.reset_index()\n",
    "alloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "avall = pd.read_csv('../rcc/rawdata/cluster_cores_by_cycles.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cycle</th>\n",
       "      <th>consumed</th>\n",
       "      <th>allocated</th>\n",
       "      <th>available</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>16.907530</td>\n",
       "      <td>9.245000</td>\n",
       "      <td>27.925112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>24.596786</td>\n",
       "      <td>25.829000</td>\n",
       "      <td>39.628809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>26.091431</td>\n",
       "      <td>43.630671</td>\n",
       "      <td>34.935164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>25.797905</td>\n",
       "      <td>51.736375</td>\n",
       "      <td>34.999115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>61.462770</td>\n",
       "      <td>84.270426</td>\n",
       "      <td>127.595111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>99.807392</td>\n",
       "      <td>106.894557</td>\n",
       "      <td>153.027033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cycle   consumed   allocated   available\n",
       "0   2013  16.907530    9.245000   27.925112\n",
       "1   2014  24.596786   25.829000   39.628809\n",
       "2   2015  26.091431   43.630671   34.935164\n",
       "3   2016  25.797905   51.736375   34.999115\n",
       "4   2017  61.462770   84.270426  127.595111\n",
       "5   2018  99.807392  106.894557  153.027033"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usage = pd.read_csv('rawdata/usages_by_cycle_group.csv')\n",
    "used = usage.groupby('cycle')['su'].sum().to_frame().reset_index()\n",
    "\n",
    "gap_day = list(usage.groupby(['cycle']).count()['su'])[-1]   #Projection\n",
    "used['su_projected'] = used['su']\n",
    "used.iloc[-1, -1] = used['su'].iloc[-1] * 365 / gap_day \n",
    "\n",
    "used.to_csv('result/2_1_usages_by_clcly_(projection).csv', index=False)\n",
    "\n",
    "\n",
    "used['total_su'] = avall['total_su']\n",
    "used['allocation'] = alloc['allocation']\n",
    "\n",
    "used['consumed'] = used['su_projected']/1000000\n",
    "used['allocated'] = used['allocation']/1000000\n",
    "used['available'] = used['total_su']/1000000\n",
    "used = used.drop(['total_su', 'allocation','su','su_projected'], axis=1, inplace=False)\n",
    "\n",
    "\n",
    "# correction of legacy\n",
    "leg = pd.read_csv('rawdata/legacy_summary.csv')\n",
    "used.loc[5,'consumed'] = sum(leg['usage'])/1000000 +used.loc[5,'consumed'] \n",
    "\n",
    "used.to_csv('result/2_1_allocs_usages_over_years.csv')\n",
    "used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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KEBAWVuE97/xcLlyhYT7Ns3LlSlJSUhg+fDgpKSmkpKQwYMAAgoKC8qZsW7Zs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\niIiIBND/B0g1eKUXVGE6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f459b1db0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "a = ax.bar(used.cycle, used['allocated'],color='#F4A460')\n",
    "b = ax.bar(used.cycle, used['consumed'],color='#556B2F')\n",
    "ax.plot(used.cycle, used['available'], color='black')\n",
    "c,= ax.plot(used.cycle, used['available'], 'ko', color='#a11b11',markersize=15);\n",
    "\n",
    "plt.legend((a,b,c),('Allocation','Consumed','Available'), fontsize=15);  ## lengend size  + rename legends\n",
    "plt.ylabel('Usage in millions', fontsize=18)                             ## label size\n",
    "plt.xlabel('Cycle', fontsize=16)\n",
    "\n",
    "ax.xaxis.set_tick_params(labelsize=15)                                             ## tick size\n",
    "ax.yaxis.set_tick_params(labelsize=15)\n",
    "fig.set_size_inches(10, 8)                                                         ## figure size\n",
    "plt.axhline(y=avall['broadwl'].iloc[-1]/1000000, color='r', linestyle='--');\n",
    "\n",
    "fig.savefig('image/2_1_allocs_usages_over_years.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}