experiments/parse_multiple.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.2.0\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "# imports\n",
    "\n",
    "import pacsltk.pacs_util as pacs_util\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "pacs_util.prepare_matplotlib_cycler()\n",
    "\n",
    "# To avoid type 3 fonts: http://phyletica.org/matplotlib-fonts/\n",
    "from matplotlib import rcParams\n",
    "rcParams['pdf.fonttype'] = 42\n",
    "rcParams['ps.fonttype'] = 42\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "\n",
    "import parse_util\n",
    "\n",
    "import os\n",
    "import glob\n",
    "from datetime import timedelta\n",
    "\n",
    "import pacsltk\n",
    "print(pacsltk.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "config name: bench1_cpu_io_rps\noverview file: results/bench1_cpu_io_rps/overview_res-2021-03-20_09-46-14.csv\nexperiment file: configs/bench1_cpu_io_rps.json\n"
     ]
    }
   ],
   "source": [
    "# Config paths\n",
    "# EXP_CONFIG_NAME_DEFAULT = 'bench1_sleep_rand2_1000_200'\n",
    "# EXP_CONFIG_NAME_DEFAULT = 'autoscale_go_500_10k_5'\n",
    "# EXP_CONFIG_NAME_DEFAULT = 'autoscale_go_500_10k_5_rps'\n",
    "EXP_CONFIG_NAME_DEFAULT = 'bench1_cpu_io_rps'\n",
    "exp_config_name = os.getenv(\"EXP_CONFIG_NAME\", EXP_CONFIG_NAME_DEFAULT)\n",
    "exp_file = f\"configs/{exp_config_name}.json\"\n",
    "overview_file_glob = f\"results/{exp_config_name}/overview*.csv\"\n",
    "overview_file = glob.glob(overview_file_glob)[0]\n",
    "\n",
    "print('config name:', exp_config_name)\n",
    "print('overview file:', overview_file)\n",
    "print('experiment file:', exp_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   target  rps                 res_name       request_func workload_name  \\\n",
       "0     1.0    1  res-2021-03-18_18-26-11  bench1_cpu_io_rps        bench1   \n",
       "1     1.0    2  res-2021-03-18_19-26-56  bench1_cpu_io_rps        bench1   \n",
       "2     1.0    3  res-2021-03-18_20-27-25  bench1_cpu_io_rps        bench1   \n",
       "3     1.0    5  res-2021-03-18_21-27-48  bench1_cpu_io_rps        bench1   \n",
       "4     1.0    7  res-2021-03-18_22-28-10  bench1_cpu_io_rps        bench1   \n",
       "5     1.0   10  res-2021-03-18_23-28-30  bench1_cpu_io_rps        bench1   \n",
       "6     1.0   15  res-2021-03-19_00-28-47  bench1_cpu_io_rps        bench1   \n",
       "7     1.0   20  res-2021-03-19_01-29-03  bench1_cpu_io_rps        bench1   \n",
       "\n",
       "   is_rps  exp_time_mins  \n",
       "0    True             60  \n",
       "1    True             60  \n",
       "2    True             60  \n",
       "3    True             60  \n",
       "4    True             60  \n",
       "5    True             60  \n",
       "6    True             60  \n",
       "7    True             60  "
      ],
      "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>rps</th>\n      <th>res_name</th>\n      <th>request_func</th>\n      <th>workload_name</th>\n      <th>is_rps</th>\n      <th>exp_time_mins</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>1</td>\n      <td>res-2021-03-18_18-26-11</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>2</td>\n      <td>res-2021-03-18_19-26-56</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.0</td>\n      <td>3</td>\n      <td>res-2021-03-18_20-27-25</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.0</td>\n      <td>5</td>\n      <td>res-2021-03-18_21-27-48</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.0</td>\n      <td>7</td>\n      <td>res-2021-03-18_22-28-10</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1.0</td>\n      <td>10</td>\n      <td>res-2021-03-18_23-28-30</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1.0</td>\n      <td>15</td>\n      <td>res-2021-03-19_00-28-47</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1.0</td>\n      <td>20</td>\n      <td>res-2021-03-19_01-29-03</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "# get overview\n",
    "overview_df = pd.read_csv(overview_file, index_col=0, parse_dates=True)\n",
    "overview_df.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "  0%|          | 0/40 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "ea09dd77c8354605b508c1cabe2757f0"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   conc_window_average_mean  conc_window_average_var  conc_window_average_se  \\\n",
       "0                  0.092804                 0.000047                0.003058   \n",
       "1                  0.135685                 0.000073                0.003808   \n",
       "2                  0.140022                 0.000049                0.003132   \n",
       "3                  0.135473                 0.000026                0.002299   \n",
       "4                  0.135197                 0.000021                0.002031   \n",
       "\n",
       "   conc_window_average_ci  average_ready_count_mean  average_ready_count_var  \\\n",
       "0                0.005993                  1.993349                 0.000904   \n",
       "1                0.007464                  3.122339                 0.001907   \n",
       "2                0.006138                  4.795579                 0.008884   \n",
       "3                0.004507                  7.522682                 0.016150   \n",
       "4                0.003981                 10.401064                 0.011073   \n",
       "\n",
       "   average_ready_count_se  average_ready_count_ci  client_elapsed_time_mean  \\\n",
       "0                0.013450                0.026361                  0.174884   \n",
       "1                0.019529                0.038276                  0.211781   \n",
       "2                0.042153                0.082620                  0.212974   \n",
       "3                0.056833                0.111393                  0.205661   \n",
       "4                0.047059                0.092237                  0.201578   \n",
       "\n",
       "   client_elapsed_time_var  client_elapsed_time_se  client_elapsed_time_ci  \\\n",
       "0                 0.000022                0.002091                0.004099   \n",
       "1                 0.000298                0.007722                0.015136   \n",
       "2                 0.000068                0.003699                0.007249   \n",
       "3                 0.000016                0.001771                0.003471   \n",
       "4                 0.000002                0.000691                0.001355   \n",
       "\n",
       "   lambda_over_n_mean  lambda_over_n_var  lambda_over_n_se  lambda_over_n_ci  \n",
       "0            0.506352           0.000155          0.005576          0.010929  \n",
       "1            0.646860           0.000047          0.003054          0.005986  \n",
       "2            0.632386           0.000193          0.006208          0.012167  \n",
       "3            0.668272           0.000131          0.005115          0.010025  \n",
       "4            0.675176           0.000043          0.002919          0.005722  "
      ],
      "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>conc_window_average_mean</th>\n      <th>conc_window_average_var</th>\n      <th>conc_window_average_se</th>\n      <th>conc_window_average_ci</th>\n      <th>average_ready_count_mean</th>\n      <th>average_ready_count_var</th>\n      <th>average_ready_count_se</th>\n      <th>average_ready_count_ci</th>\n      <th>client_elapsed_time_mean</th>\n      <th>client_elapsed_time_var</th>\n      <th>client_elapsed_time_se</th>\n      <th>client_elapsed_time_ci</th>\n      <th>lambda_over_n_mean</th>\n      <th>lambda_over_n_var</th>\n      <th>lambda_over_n_se</th>\n      <th>lambda_over_n_ci</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.092804</td>\n      <td>0.000047</td>\n      <td>0.003058</td>\n      <td>0.005993</td>\n      <td>1.993349</td>\n      <td>0.000904</td>\n      <td>0.013450</td>\n      <td>0.026361</td>\n      <td>0.174884</td>\n      <td>0.000022</td>\n      <td>0.002091</td>\n      <td>0.004099</td>\n      <td>0.506352</td>\n      <td>0.000155</td>\n      <td>0.005576</td>\n      <td>0.010929</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.135685</td>\n      <td>0.000073</td>\n      <td>0.003808</td>\n      <td>0.007464</td>\n      <td>3.122339</td>\n      <td>0.001907</td>\n      <td>0.019529</td>\n      <td>0.038276</td>\n      <td>0.211781</td>\n      <td>0.000298</td>\n      <td>0.007722</td>\n      <td>0.015136</td>\n      <td>0.646860</td>\n      <td>0.000047</td>\n      <td>0.003054</td>\n      <td>0.005986</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.140022</td>\n      <td>0.000049</td>\n      <td>0.003132</td>\n      <td>0.006138</td>\n      <td>4.795579</td>\n      <td>0.008884</td>\n      <td>0.042153</td>\n      <td>0.082620</td>\n      <td>0.212974</td>\n      <td>0.000068</td>\n      <td>0.003699</td>\n      <td>0.007249</td>\n      <td>0.632386</td>\n      <td>0.000193</td>\n      <td>0.006208</td>\n      <td>0.012167</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.135473</td>\n      <td>0.000026</td>\n      <td>0.002299</td>\n      <td>0.004507</td>\n      <td>7.522682</td>\n      <td>0.016150</td>\n      <td>0.056833</td>\n      <td>0.111393</td>\n      <td>0.205661</td>\n      <td>0.000016</td>\n      <td>0.001771</td>\n      <td>0.003471</td>\n      <td>0.668272</td>\n      <td>0.000131</td>\n      <td>0.005115</td>\n      <td>0.010025</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.135197</td>\n      <td>0.000021</td>\n      <td>0.002031</td>\n      <td>0.003981</td>\n      <td>10.401064</td>\n      <td>0.011073</td>\n      <td>0.047059</td>\n      <td>0.092237</td>\n      <td>0.201578</td>\n      <td>0.000002</td>\n      <td>0.000691</td>\n      <td>0.001355</td>\n      <td>0.675176</td>\n      <td>0.000043</td>\n      <td>0.002919</td>\n      <td>0.005722</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "batch_seconds = 600\n",
    "\n",
    "# func used on each batch (part of a single experiment logs)\n",
    "parse_cols_funcs = {\n",
    "    'conc_window_average': lambda df: df['conc_window_average'].mean(),\n",
    "    'average_ready_count': lambda df: df['ready_count'].mean(),\n",
    "}\n",
    "\n",
    "# func used on each batch (part of a single experiment logs)\n",
    "parse_req_cols_funcs = {\n",
    "    'client_elapsed_time': lambda df: df['client_elapsed_time'].mean(),\n",
    "    'lambda_over_n': lambda df: df['lambda_over_n'].mean(),\n",
    "}\n",
    "\n",
    "parse_res = []\n",
    "for res_name in tqdm(overview_df['res_name']):\n",
    "    res_logger_file = f\"results/{exp_config_name}/{res_name}_logger.csv\"\n",
    "    df = parse_util.parse_logger_file(res_logger_file)\n",
    "    parse_cols_res = parse_util.parse_batch_custom_funcs(df, batch_seconds, parse_cols_funcs)\n",
    "    \n",
    "    res_reqs_file = f\"results/{exp_config_name}/{res_name}_reqs.csv\"\n",
    "    df_req = parse_util.parse_reqs_file(res_reqs_file)\n",
    "    parse_req_res = parse_util.parse_batch_custom_funcs(df_req, batch_seconds, parse_req_cols_funcs)\n",
    "\n",
    "    parse_cols_res.update(parse_req_res)\n",
    "\n",
    "    parse_res.append(parse_cols_res)\n",
    "\n",
    "parse_res_df = pd.DataFrame(data=parse_res)\n",
    "parse_res_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   target  rps                 res_name       request_func workload_name  \\\n",
       "0     1.0    1  res-2021-03-18_18-26-11  bench1_cpu_io_rps        bench1   \n",
       "1     1.0    2  res-2021-03-18_19-26-56  bench1_cpu_io_rps        bench1   \n",
       "2     1.0    3  res-2021-03-18_20-27-25  bench1_cpu_io_rps        bench1   \n",
       "3     1.0    5  res-2021-03-18_21-27-48  bench1_cpu_io_rps        bench1   \n",
       "4     1.0    7  res-2021-03-18_22-28-10  bench1_cpu_io_rps        bench1   \n",
       "\n",
       "   is_rps  exp_time_mins  conc_window_average_mean  conc_window_average_var  \\\n",
       "0    True             60                  0.092804                 0.000047   \n",
       "1    True             60                  0.135685                 0.000073   \n",
       "2    True             60                  0.140022                 0.000049   \n",
       "3    True             60                  0.135473                 0.000026   \n",
       "4    True             60                  0.135197                 0.000021   \n",
       "\n",
       "   conc_window_average_se  ...  average_ready_count_se  \\\n",
       "0                0.003058  ...                0.013450   \n",
       "1                0.003808  ...                0.019529   \n",
       "2                0.003132  ...                0.042153   \n",
       "3                0.002299  ...                0.056833   \n",
       "4                0.002031  ...                0.047059   \n",
       "\n",
       "   average_ready_count_ci  client_elapsed_time_mean  client_elapsed_time_var  \\\n",
       "0                0.026361                  0.174884                 0.000022   \n",
       "1                0.038276                  0.211781                 0.000298   \n",
       "2                0.082620                  0.212974                 0.000068   \n",
       "3                0.111393                  0.205661                 0.000016   \n",
       "4                0.092237                  0.201578                 0.000002   \n",
       "\n",
       "   client_elapsed_time_se  client_elapsed_time_ci  lambda_over_n_mean  \\\n",
       "0                0.002091                0.004099            0.506352   \n",
       "1                0.007722                0.015136            0.646860   \n",
       "2                0.003699                0.007249            0.632386   \n",
       "3                0.001771                0.003471            0.668272   \n",
       "4                0.000691                0.001355            0.675176   \n",
       "\n",
       "   lambda_over_n_var  lambda_over_n_se  lambda_over_n_ci  \n",
       "0           0.000155          0.005576          0.010929  \n",
       "1           0.000047          0.003054          0.005986  \n",
       "2           0.000193          0.006208          0.012167  \n",
       "3           0.000131          0.005115          0.010025  \n",
       "4           0.000043          0.002919          0.005722  \n",
       "\n",
       "[5 rows x 23 columns]"
      ],
      "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>rps</th>\n      <th>res_name</th>\n      <th>request_func</th>\n      <th>workload_name</th>\n      <th>is_rps</th>\n      <th>exp_time_mins</th>\n      <th>conc_window_average_mean</th>\n      <th>conc_window_average_var</th>\n      <th>conc_window_average_se</th>\n      <th>...</th>\n      <th>average_ready_count_se</th>\n      <th>average_ready_count_ci</th>\n      <th>client_elapsed_time_mean</th>\n      <th>client_elapsed_time_var</th>\n      <th>client_elapsed_time_se</th>\n      <th>client_elapsed_time_ci</th>\n      <th>lambda_over_n_mean</th>\n      <th>lambda_over_n_var</th>\n      <th>lambda_over_n_se</th>\n      <th>lambda_over_n_ci</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>1</td>\n      <td>res-2021-03-18_18-26-11</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n      <td>0.092804</td>\n      <td>0.000047</td>\n      <td>0.003058</td>\n      <td>...</td>\n      <td>0.013450</td>\n      <td>0.026361</td>\n      <td>0.174884</td>\n      <td>0.000022</td>\n      <td>0.002091</td>\n      <td>0.004099</td>\n      <td>0.506352</td>\n      <td>0.000155</td>\n      <td>0.005576</td>\n      <td>0.010929</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>2</td>\n      <td>res-2021-03-18_19-26-56</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n      <td>0.135685</td>\n      <td>0.000073</td>\n      <td>0.003808</td>\n      <td>...</td>\n      <td>0.019529</td>\n      <td>0.038276</td>\n      <td>0.211781</td>\n      <td>0.000298</td>\n      <td>0.007722</td>\n      <td>0.015136</td>\n      <td>0.646860</td>\n      <td>0.000047</td>\n      <td>0.003054</td>\n      <td>0.005986</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.0</td>\n      <td>3</td>\n      <td>res-2021-03-18_20-27-25</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n      <td>0.140022</td>\n      <td>0.000049</td>\n      <td>0.003132</td>\n      <td>...</td>\n      <td>0.042153</td>\n      <td>0.082620</td>\n      <td>0.212974</td>\n      <td>0.000068</td>\n      <td>0.003699</td>\n      <td>0.007249</td>\n      <td>0.632386</td>\n      <td>0.000193</td>\n      <td>0.006208</td>\n      <td>0.012167</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.0</td>\n      <td>5</td>\n      <td>res-2021-03-18_21-27-48</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n      <td>0.135473</td>\n      <td>0.000026</td>\n      <td>0.002299</td>\n      <td>...</td>\n      <td>0.056833</td>\n      <td>0.111393</td>\n      <td>0.205661</td>\n      <td>0.000016</td>\n      <td>0.001771</td>\n      <td>0.003471</td>\n      <td>0.668272</td>\n      <td>0.000131</td>\n      <td>0.005115</td>\n      <td>0.010025</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.0</td>\n      <td>7</td>\n      <td>res-2021-03-18_22-28-10</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n      <td>0.135197</td>\n      <td>0.000021</td>\n      <td>0.002031</td>\n      <td>...</td>\n      <td>0.047059</td>\n      <td>0.092237</td>\n      <td>0.201578</td>\n      <td>0.000002</td>\n      <td>0.000691</td>\n      <td>0.001355</td>\n      <td>0.675176</td>\n      <td>0.000043</td>\n      <td>0.002919</td>\n      <td>0.005722</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 23 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "orig_overview_parsed_df = pd.concat([overview_df, parse_res_df], axis=1)\n",
    "orig_overview_parsed_df.head()"
   ]
  },
  {
   "source": [
    "# Make Workload Plots"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Index(['target', 'rps', 'res_name', 'request_func', 'workload_name', 'is_rps',\n",
       "       'exp_time_mins', 'conc_window_average_mean', 'conc_window_average_var',\n",
       "       'conc_window_average_se', 'conc_window_average_ci',\n",
       "       'average_ready_count_mean', 'average_ready_count_var',\n",
       "       'average_ready_count_se', 'average_ready_count_ci',\n",
       "       'client_elapsed_time_mean', 'client_elapsed_time_var',\n",
       "       'client_elapsed_time_se', 'client_elapsed_time_ci',\n",
       "       'lambda_over_n_mean', 'lambda_over_n_var', 'lambda_over_n_se',\n",
       "       'lambda_over_n_ci'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "# list all available columns\n",
    "orig_overview_parsed_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   target  rps                 res_name       request_func workload_name  \\\n",
       "0     1.0    1  res-2021-03-18_18-26-11  bench1_cpu_io_rps        bench1   \n",
       "1     1.0    2  res-2021-03-18_19-26-56  bench1_cpu_io_rps        bench1   \n",
       "2     1.0    3  res-2021-03-18_20-27-25  bench1_cpu_io_rps        bench1   \n",
       "3     1.0    5  res-2021-03-18_21-27-48  bench1_cpu_io_rps        bench1   \n",
       "4     1.0    7  res-2021-03-18_22-28-10  bench1_cpu_io_rps        bench1   \n",
       "\n",
       "   is_rps  exp_time_mins  conc_window_average_mean  conc_window_average_var  \\\n",
       "0    True             60                  0.092804                 0.000047   \n",
       "1    True             60                  0.135685                 0.000073   \n",
       "2    True             60                  0.140022                 0.000049   \n",
       "3    True             60                  0.135473                 0.000026   \n",
       "4    True             60                  0.135197                 0.000021   \n",
       "\n",
       "   conc_window_average_se  ...  average_ready_count_se  \\\n",
       "0                0.003058  ...                0.013450   \n",
       "1                0.003808  ...                0.019529   \n",
       "2                0.003132  ...                0.042153   \n",
       "3                0.002299  ...                0.056833   \n",
       "4                0.002031  ...                0.047059   \n",
       "\n",
       "   average_ready_count_ci  client_elapsed_time_mean  client_elapsed_time_var  \\\n",
       "0                0.026361                  0.174884                 0.000022   \n",
       "1                0.038276                  0.211781                 0.000298   \n",
       "2                0.082620                  0.212974                 0.000068   \n",
       "3                0.111393                  0.205661                 0.000016   \n",
       "4                0.092237                  0.201578                 0.000002   \n",
       "\n",
       "   client_elapsed_time_se  client_elapsed_time_ci  lambda_over_n_mean  \\\n",
       "0                0.002091                0.004099            0.506352   \n",
       "1                0.007722                0.015136            0.646860   \n",
       "2                0.003699                0.007249            0.632386   \n",
       "3                0.001771                0.003471            0.668272   \n",
       "4                0.000691                0.001355            0.675176   \n",
       "\n",
       "   lambda_over_n_var  lambda_over_n_se  lambda_over_n_ci  \n",
       "0           0.000155          0.005576          0.010929  \n",
       "1           0.000047          0.003054          0.005986  \n",
       "2           0.000193          0.006208          0.012167  \n",
       "3           0.000131          0.005115          0.010025  \n",
       "4           0.000043          0.002919          0.005722  \n",
       "\n",
       "[5 rows x 23 columns]"
      ],
      "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>rps</th>\n      <th>res_name</th>\n      <th>request_func</th>\n      <th>workload_name</th>\n      <th>is_rps</th>\n      <th>exp_time_mins</th>\n      <th>conc_window_average_mean</th>\n      <th>conc_window_average_var</th>\n      <th>conc_window_average_se</th>\n      <th>...</th>\n      <th>average_ready_count_se</th>\n      <th>average_ready_count_ci</th>\n      <th>client_elapsed_time_mean</th>\n      <th>client_elapsed_time_var</th>\n      <th>client_elapsed_time_se</th>\n      <th>client_elapsed_time_ci</th>\n      <th>lambda_over_n_mean</th>\n      <th>lambda_over_n_var</th>\n      <th>lambda_over_n_se</th>\n      <th>lambda_over_n_ci</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>1</td>\n      <td>res-2021-03-18_18-26-11</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n      <td>0.092804</td>\n      <td>0.000047</td>\n      <td>0.003058</td>\n      <td>...</td>\n      <td>0.013450</td>\n      <td>0.026361</td>\n      <td>0.174884</td>\n      <td>0.000022</td>\n      <td>0.002091</td>\n      <td>0.004099</td>\n      <td>0.506352</td>\n      <td>0.000155</td>\n      <td>0.005576</td>\n      <td>0.010929</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>2</td>\n      <td>res-2021-03-18_19-26-56</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n      <td>0.135685</td>\n      <td>0.000073</td>\n      <td>0.003808</td>\n      <td>...</td>\n      <td>0.019529</td>\n      <td>0.038276</td>\n      <td>0.211781</td>\n      <td>0.000298</td>\n      <td>0.007722</td>\n      <td>0.015136</td>\n      <td>0.646860</td>\n      <td>0.000047</td>\n      <td>0.003054</td>\n      <td>0.005986</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.0</td>\n      <td>3</td>\n      <td>res-2021-03-18_20-27-25</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n      <td>0.140022</td>\n      <td>0.000049</td>\n      <td>0.003132</td>\n      <td>...</td>\n      <td>0.042153</td>\n      <td>0.082620</td>\n      <td>0.212974</td>\n      <td>0.000068</td>\n      <td>0.003699</td>\n      <td>0.007249</td>\n      <td>0.632386</td>\n      <td>0.000193</td>\n      <td>0.006208</td>\n      <td>0.012167</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.0</td>\n      <td>5</td>\n      <td>res-2021-03-18_21-27-48</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n      <td>0.135473</td>\n      <td>0.000026</td>\n      <td>0.002299</td>\n      <td>...</td>\n      <td>0.056833</td>\n      <td>0.111393</td>\n      <td>0.205661</td>\n      <td>0.000016</td>\n      <td>0.001771</td>\n      <td>0.003471</td>\n      <td>0.668272</td>\n      <td>0.000131</td>\n      <td>0.005115</td>\n      <td>0.010025</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.0</td>\n      <td>7</td>\n      <td>res-2021-03-18_22-28-10</td>\n      <td>bench1_cpu_io_rps</td>\n      <td>bench1</td>\n      <td>True</td>\n      <td>60</td>\n      <td>0.135197</td>\n      <td>0.000021</td>\n      <td>0.002031</td>\n      <td>...</td>\n      <td>0.047059</td>\n      <td>0.092237</td>\n      <td>0.201578</td>\n      <td>0.000002</td>\n      <td>0.000691</td>\n      <td>0.001355</td>\n      <td>0.675176</td>\n      <td>0.000043</td>\n      <td>0.002919</td>\n      <td>0.005722</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 23 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "# filter for some values\n",
    "# plot_targets = [1,2,5,10]\n",
    "plot_targets = [1,1.5,2,2.5,3,5,10]\n",
    "overview_parsed_df = orig_overview_parsed_df[orig_overview_parsed_df['target'].isin(plot_targets)]\n",
    "overview_parsed_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "rps_ticks = overview_parsed_df['rps'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(4,2.5))\n",
    "for target_value in overview_parsed_df['target'].unique():\n",
    "    sub_df = overview_parsed_df.loc[overview_parsed_df['target'] == target_value, :]\n",
    "    # plt.semilogx(sub_df['rps'], sub_df['average_ready_count_mean'])\n",
    "    plt.errorbar(sub_df['rps'], sub_df['average_ready_count_mean'], yerr=sub_df['average_ready_count_ci'], label=f\"Target={target_value}\")\n",
    "\n",
    "plt.gca().set_xscale('log')\n",
    "plt.xticks(rps_ticks, rps_ticks)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"Arrival Rate (req/s)\")\n",
    "plt.ylabel(\"Average Ready Containers\")\n",
    "plt.gcf().subplots_adjust(left=0.13, bottom=0.20)\n",
    "parse_util.tmp_fig_save('01_average_ready_containers_vs_arrival_rate', exp_config_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(4,2.5))\n",
    "for target_value in overview_parsed_df['target'].unique():\n",
    "    sub_df = overview_parsed_df.loc[overview_parsed_df['target'] == target_value, :]\n",
    "    # plt.semilogx(sub_df['rps'], sub_df['conc_window_average_mean'])\n",
    "    plt.errorbar(sub_df['rps'], sub_df['conc_window_average_mean'], yerr=sub_df['conc_window_average_ci'], label=f\"Target={target_value}\")\n",
    "\n",
    "plt.gca().set_xscale('log')\n",
    "plt.xticks(rps_ticks, rps_ticks)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"Arrival Rate (req/s)\")\n",
    "plt.ylabel(\"Average Concurrency\")\n",
    "plt.gcf().subplots_adjust(left=0.13, bottom=0.20)\n",
    "parse_util.tmp_fig_save('02_conc_window_average_vs_arrival_rate', exp_config_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(4,2.5))\n",
    "for target_value in overview_parsed_df['target'].unique():\n",
    "    sub_df = overview_parsed_df.loc[overview_parsed_df['target'] == target_value, :]\n",
    "    # plt.semilogx(sub_df['rps'], sub_df['conc_window_average_mean'])\n",
    "    plt.errorbar(sub_df['rps'], sub_df['client_elapsed_time_mean'], yerr=sub_df['client_elapsed_time_ci'], label=f\"Target={target_value}\")\n",
    "\n",
    "plt.gca().set_xscale('log')\n",
    "plt.xticks(rps_ticks, rps_ticks)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"Arrival Rate (req/s)\")\n",
    "plt.ylabel(\"Average Response Time (s)\")\n",
    "plt.gcf().subplots_adjust(left=0.13, bottom=0.20)\n",
    "parse_util.tmp_fig_save('03_average_resp_time_vs_arrival_rate', exp_config_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 288x180 with 2 Axes>",
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\n"
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# let's see if we can make plots for effect of CC\n",
    "from matplotlib import ticker\n",
    "\n",
    "for plot_arrival_rate in [1,2,5,10,20]:\n",
    "    sub_overview_df = orig_overview_parsed_df[orig_overview_parsed_df['rps'] == plot_arrival_rate]\n",
    "\n",
    "    plt.figure(figsize=(4,2.5))\n",
    "    color = 'k'\n",
    "    ax1 = plt.gca()\n",
    "    ax1.errorbar(sub_overview_df['target'], sub_overview_df['average_ready_count_mean'], yerr=sub_overview_df['average_ready_count_ci'], color=color)\n",
    "    ax1.set_xlabel('Target Value')\n",
    "    ax1.set_ylabel('Instance Count', color=color)\n",
    "    ax1.tick_params(axis='y', labelcolor=color)\n",
    "    plt.grid(True, axis='x')\n",
    "\n",
    "    color = 'tab:red'\n",
    "    ax2 = plt.gca().twinx()\n",
    "    ax2.errorbar(sub_overview_df['target'], sub_overview_df['client_elapsed_time_mean'], yerr=sub_overview_df['client_elapsed_time_ci'], ls='--', color=color)\n",
    "    ax2.set_ylabel('Response Time (s)', color=color)\n",
    "    ax2.tick_params(axis='y', labelcolor=color)\n",
    "    ax2.grid(None)\n",
    "    # ax2.set_ylim([0.4,0.6])\n",
    "\n",
    "    # aligning ticks for grids\n",
    "    l = ax1.get_ylim()\n",
    "    l2 = ax2.get_ylim()\n",
    "    f = lambda x : l2[0]+(x-l[0])/(l[1]-l[0])*(l2[1]-l2[0])\n",
    "    ticks = f(ax1.get_yticks())\n",
    "    ax2.yaxis.set_major_locator(ticker.FixedLocator(ticks))\n",
    "\n",
    "    # final config\n",
    "    plt.gcf().subplots_adjust(left=0.13, bottom=0.20)\n",
    "    plt.tight_layout()\n",
    "\n",
    "    parse_util.tmp_fig_save(f'04_inst_count_resp_time_target_arrival_{plot_arrival_rate}', exp_config_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "overview_results_dir = f\"results/overview/inst_count_resp_time_target_arrival/\"\n",
    "!mkdir -p {overview_results_dir}\n",
    "save_overview_df = orig_overview_parsed_df.loc[:, ['rps', 'target', 'average_ready_count_mean', 'average_ready_count_ci', 'client_elapsed_time_mean', 'client_elapsed_time_ci']]\n",
    "save_overview_df.to_csv(f\"{overview_results_dir}{exp_config_name}.csv\")"
   ]
  }
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