NBD_CH2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e9336cec",
   "metadata": {
    "tags": []
   },
   "source": [
    "# NBD | Data Center"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fc17bcf-1090-4fb2-839a-154f705d09e2",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Challenge #2 → *workload balancing*\n",
    "\n",
    "Group composition:\n",
    "\n",
    "- Gian Alvin Guico : 2033024\n",
    "- Leonardo Di Nino : 1919479\n",
    "- Luca Mazzucco : 1997610\n",
    "\n",
    "Final report [here](https://nbviewer.org/github/LM1997610/Networking-for-Big-Data/blob/main/NBD_CH2_Report.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "34e4387e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from tabulate import tabulate\n",
    "\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c967e870",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "data.shape: (2282872, 5)\n",
      "\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>Job_ID</th>\n",
       "      <th>Task_ID</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>CPU</th>\n",
       "      <th>Memory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>375000667413</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041851</td>\n",
       "      <td>0.001169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>375000669289</td>\n",
       "      <td>0</td>\n",
       "      <td>3.386741</td>\n",
       "      <td>0.024968</td>\n",
       "      <td>0.001179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>375000670586</td>\n",
       "      <td>0</td>\n",
       "      <td>5.968153</td>\n",
       "      <td>0.024176</td>\n",
       "      <td>0.001173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>375000670588</td>\n",
       "      <td>0</td>\n",
       "      <td>5.968166</td>\n",
       "      <td>0.019552</td>\n",
       "      <td>0.001163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>375000670590</td>\n",
       "      <td>0</td>\n",
       "      <td>6.016603</td>\n",
       "      <td>0.028044</td>\n",
       "      <td>0.001179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Job_ID  Task_ID  Arrival_Time       CPU    Memory\n",
       "0  375000667413        0      0.000000  0.041851  0.001169\n",
       "1  375000669289        0      3.386741  0.024968  0.001179\n",
       "2  375000670586        0      5.968153  0.024176  0.001173\n",
       "3  375000670588        0      5.968166  0.019552  0.001163\n",
       "4  375000670590        0      6.016603  0.028044  0.001179"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv('Cell_a.zip')\n",
    "\n",
    "data = data[data['CPU']!= 0]\n",
    "data['Arrival_Time'] = data['Arrival_Time']/1e6\n",
    "data['Arrival_Time'] = data['Arrival_Time'] - data['Arrival_Time'].iloc[0] \n",
    "\n",
    "print(\"\\ndata.shape: {}\\n\".format(data.shape))\n",
    "display(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba35f653",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# constrains\n",
    "\n",
    "N = 64; µ = 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a71f899",
   "metadata": {},
   "source": [
    "--------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd08cb3e",
   "metadata": {},
   "source": [
    "### Explorative data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e656ecf6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.282872e+06\n",
       "mean     4.042883e+00\n",
       "std      1.370927e+02\n",
       "min      9.536743e-07\n",
       "25%      2.730942e-02\n",
       "50%      7.698393e-02\n",
       "75%      2.746394e-01\n",
       "max      5.278707e+04\n",
       "Name: CPU, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['CPU'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1d9dd13",
   "metadata": {},
   "source": [
    "Assuming an underlying bounded Pareto process for the task size, we wanted to estimate a maximum likelihood estimator for the value of 𝛼 through a repeated subsampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2e6d95fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "pip install distfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a8e6ae5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from distfit import distfit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3cd4a3a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1000/1000 [01:44<00:00,  9.54it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " alpha mean:  1.1908778356010248\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "alpha = []\n",
    "M = 1000\n",
    "S = 1000\n",
    "dist = distfit(distr='genpareto')\n",
    "cpu = np.array(data['CPU'])\n",
    "\n",
    "for i in tqdm(range(M)):\n",
    "    subsample = cpu[np.random.randint(0,len(cpu),size=S)]\n",
    "    alpha.append(dist.fit_transform(subsample, verbose = 0)['model']['params'][0])\n",
    "    \n",
    "print(\"\\n alpha mean: \", np.mean(alpha))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fd0aa7c",
   "metadata": {},
   "source": [
    "Retrieving some statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e80e48f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "global_var = data['CPU'].var()\n",
    "\n",
    "min_load = data['CPU'].min()\n",
    "max_load = data['CPU'].max()\n",
    "\n",
    "ranging = (min_load, round(max_load,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "12eada8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "count = data.groupby('Job_ID')[\"Task_ID\"].count()\n",
    "data2 = data.merge(count, on='Job_ID')\n",
    "\n",
    "max_multi = data2[data2['Task_ID_y']!=0]['CPU'].max()\n",
    "min_multi = data2[data2['Task_ID_y']!=0]['CPU'].min()\n",
    "inner_multi_var = data2[data2['Task_ID_y']!=0]['CPU'].var()\n",
    "\n",
    "max_single = data2[data2['Task_ID_y']==1]['CPU'].max()\n",
    "min_single = data2[data2['Task_ID_y']==1]['CPU'].min()\n",
    "inner_single_var = data2[data2['Task_ID_y']==1]['CPU'].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d3733320",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "╒═════════════════════════════════════╤══════════════════════════════════╕\n",
      "│           Global variance           │            18794.413             │\n",
      "├─────────────────────────────────────┼──────────────────────────────────┤\n",
      "│                Range                │ [9.5367431640625e-07, 52787.072] │\n",
      "├─────────────────────────────────────┼──────────────────────────────────┤\n",
      "│    Fraction of single-task-jobs     │              0.948               │\n",
      "├─────────────────────────────────────┼──────────────────────────────────┤\n",
      "│     Fraction of multi-task-jobs     │              0.052               │\n",
      "├─────────────────────────────────────┼──────────────────────────────────┤\n",
      "│           Single-task max           │            52787.072             │\n",
      "├─────────────────────────────────────┼──────────────────────────────────┤\n",
      "│           Single-task min           │       9.5367431640625e-07        │\n",
      "├─────────────────────────────────────┼──────────────────────────────────┤\n",
      "│ Inner variance in single-task group │             17815.96             │\n",
      "├─────────────────────────────────────┼──────────────────────────────────┤\n",
      "│         Multi-task job max          │            52787.072             │\n",
      "├─────────────────────────────────────┼──────────────────────────────────┤\n",
      "│         Multi-task job min          │       9.5367431640625e-07        │\n",
      "├─────────────────────────────────────┼──────────────────────────────────┤\n",
      "│ Inner variance in multi-task group  │            18794.413             │\n",
      "╘═════════════════════════════════════╧══════════════════════════════════╛\n"
     ]
    }
   ],
   "source": [
    "a_N = pd.unique(data2['Job_ID']).shape[0]\n",
    "\n",
    "one_task = data2[data2['Task_ID_y']==1].shape[0]\n",
    "multi_task = data2[data2['Task_ID_y']>1].shape[0]\n",
    "\n",
    "stat = pd.Series({\n",
    "    'Global variance': round(global_var,3),\n",
    "    'Range':list(ranging),\n",
    "    'Fraction of single-task-jobs': round(one_task/a_N,3),\n",
    "    'Fraction of multi-task-jobs': round(1-one_task/a_N,3), \n",
    "    'Single-task max': round(max_single,3),\n",
    "    'Single-task min': min_single,\n",
    "    'Inner variance in single-task group': round(inner_single_var,3),\n",
    "    'Multi-task job max': round(max_multi,3),\n",
    "    'Multi-task job min': min_multi,\n",
    "    'Inner variance in multi-task group': round(inner_multi_var,3)})\n",
    "\n",
    "print()\n",
    "print(tabulate(stat.to_frame(), tablefmt=\"fancy_grid\", numalign='center', stralign=\"center\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8ca67853",
   "metadata": {},
   "outputs": [],
   "source": [
    "jobs = pd.unique(data['Job_ID'])\n",
    "temp = {job:k for (job,k) in zip(jobs, count)}\n",
    "\n",
    "multitask = [job for job in jobs if temp[job]>1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cd9221ee-3761-434e-b327-6e8c8de2d7fb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "new_stat = pd.Series({\n",
    "    'Variance of total CPU required':inner_multi_var,\n",
    "    'Max value for total CPU required':max_multi,\n",
    "    'Min value for total CPU required':min_multi})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d962af",
   "metadata": {},
   "source": [
    "## Baseline implementation: \n",
    "**Least_Work_Left**  for dispatching and **First_Come_First_Service** for scheduling:\n",
    "\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "50e04814",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from Dispatcher_LWL import Dispatcher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9608ac3c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Dispatcher → Least_Work_Left\n",
      "\n",
      " number of servers: 64 → First_Come_First_Service \n",
      "\n",
      "\t status: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ...] \n",
      "\n",
      "\t wk_load: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ...] \n",
      "\n",
      "\t memory: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ...] \n",
      "\n",
      "\t queue: [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "baseline = Dispatcher(N, data[\"Arrival_Time\"].iloc[0])\n",
    "baseline.print_status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e65ed5ab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2282872/2282872 [10:32<00:00, 3606.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Closing 2 active server queues: [0, 1] → 2 tasks...  done ✓ \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(range(data.shape[0])):\n",
    "    \n",
    "    task = data.iloc[i].to_dict()\n",
    "    \n",
    "    baseline.queue_manager(task)\n",
    "    baseline.Least_Work_Left(task)\n",
    "    baseline.clock_update(task['Arrival_Time'])\n",
    "    \n",
    "baseline_df = baseline.Closing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d3f72175",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Baseline :\n",
      " Mean job_response_time: 27470.201\n",
      " Mean job_slow_down: 1203727.169\n",
      " Mean utilization: 0.53842\n"
     ]
    }
   ],
   "source": [
    "arrival_times = data.groupby('Job_ID')['Arrival_Time'].min()\n",
    "base_completion_times = baseline_df.groupby('Job')['Completion_Time'].max()\n",
    "\n",
    "base_job_response_times = np.array(base_completion_times)-np.array(arrival_times)\n",
    "base_mean_job = round(np.mean(base_job_response_times), 3)\n",
    "\n",
    "tot_cpu = data.groupby('Job_ID')['CPU'].sum() / µ\n",
    "base_slow_down = base_job_response_times/np.array(tot_cpu)\n",
    "base_mean_slow = round(np.mean(base_slow_down), 3)\n",
    "\n",
    "ut_coeffs = [sum(baseline.servers[i].details)/max(base_completion_times) for i in range(N)]\n",
    "\n",
    "print(\"\\nBaseline :\")\n",
    "print(\" Mean job_response_time:\", base_mean_job)\n",
    "print(\" Mean job_slow_down:\", base_mean_slow)\n",
    "print(\" Mean utilization:\", round(np.mean(ut_coeffs),5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fcc7b36",
   "metadata": {},
   "source": [
    "----------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab451a8e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Custom design: \n",
    "**Size-Interval Task Assignment-E (and LWL)** for dispatching and **First_Come_First_Service** for scheduling:\n",
    "\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "729b0f3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "144209.119797647\n"
     ]
    }
   ],
   "source": [
    "X = data['CPU'].sort_values()\n",
    "\n",
    "quantum = sum(X)/64; print(quantum)    # fill-up value for each chunk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "02ddef2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2282872/2282872 [00:01<00:00, 1751365.95it/s]\n"
     ]
    }
   ],
   "source": [
    "ls, cumsum, cluster = [], 0, 0\n",
    "\n",
    "for value in tqdm(X):\n",
    "    if cumsum + value <= int(quantum):\n",
    "        cumsum += value\n",
    "    else:\n",
    "        cumsum = 0\n",
    "        cluster+=1\n",
    "    \n",
    "    ls.append(cluster)\n",
    "\n",
    "sorted_df = data.sort_values('CPU')\n",
    "sorted_df['cumsum'] = ls\n",
    "data = sorted_df.sort_values('Job_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "af4cac57",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data.groupby(\"cumsum\")[\"CPU\"].sum().plot(kind=\"bar\", alpha=0.75, rot=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9085510",
   "metadata": {},
   "source": [
    "--------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "297753e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from Dispatcher_SITA import Size_Int_Task_Ass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b038bd14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Dispatcher → Size_Int_Task_Ass\n",
      "\n",
      " number of servers: 64 → First_Come_First_Service \n",
      "\n",
      "\t status: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ...] \n",
      "\n",
      "\t wk_load: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ...] \n",
      "\n",
      "\t memory: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 ...] \n",
      "\n",
      "\t queue: [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "costum =  Size_Int_Task_Ass(N, data[\"Arrival_Time\"].iloc[0])\n",
    "costum.print_status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e873e0cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2282872/2282872 [06:09<00:00, 6184.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Closing 12 active server queues: [0, 1, 5, 6, 10, 11, 12, 46, 49, 51, 60, 62] → 2128 tasks...  done ✓ \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(range(data.shape[0])):\n",
    "    \n",
    "    task = data.iloc[i].to_dict()\n",
    "    \n",
    "    costum.queue_manager(task)\n",
    "    costum.SITA_dispatch(task)\n",
    "    costum.clock_update(task['Arrival_Time'])\n",
    "    \n",
    "costum_df = costum.Closing()   ## → 2128 tasks..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "caaf0833",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Costum :\n",
      " Mean job_response_time: 756.528\n",
      " Mean job_slow_down: 1614.606\n",
      " Mean utilization: 0.5070270312798624\n"
     ]
    }
   ],
   "source": [
    "#arrival_times = data.groupby('Job_ID')['Arrival_Time'].min()\n",
    "costum_completion_times = costum_df.groupby('Job')['Completion_Time'].max()\n",
    "\n",
    "costum_job_response_times = np.array(costum_completion_times )-np.array(arrival_times)\n",
    "costum_mean_job = round(np.mean(costum_job_response_times), 3)\n",
    "\n",
    "#tot_cpu = data.groupby('Job_ID')['CPU'].sum() / µ \n",
    "custum_slow_down = costum_job_response_times/np.array(tot_cpu)\n",
    "custum_mean_slow = round(np.mean(custum_slow_down), 3)\n",
    "\n",
    "c_ut_coeffs = [sum(costum.servers[i].details)/max(costum_completion_times) for i in range(N)]\n",
    "\n",
    "print(\"\\nCostum :\")\n",
    "print(\" Mean job_response_time:\", costum_mean_job)\n",
    "print(\" Mean job_slow_down:\", custum_mean_slow)\n",
    "print(\" Mean utilization:\", np.mean(c_ut_coeffs))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c98a4c6-ba71-45a0-8cef-f5d5f48b7b88",
   "metadata": {},
   "source": [
    "## Results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ee4eb3ef-e34c-4198-a87c-430e65520fff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from visualization import make_plots as mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bb3f54b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1300x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mp.ecdf_plot(base_job_response_times, \n",
    "             costum_job_response_times, \n",
    "             base_slow_down, custum_slow_down)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1e0c7da9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mp.coeff_plot(ut_coeffs, c_ut_coeffs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "74988de8",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_m_load = 129\n",
    "cust_m_load = (1752627*7 + data.shape[0]-1752627)/data.shape[0]\n",
    "\n",
    "metrics = pd.DataFrame({\n",
    "    'Mean Job_Response_Time': [base_mean_job, costum_mean_job],\n",
    "    'Mean Job_Slow_Down': [base_mean_slow, custum_mean_slow],\n",
    "    'Mean Utilization': [np.mean(ut_coeffs), np.mean(c_ut_coeffs)],\n",
    "    'Mean Message_Load': [base_m_load, round(cust_m_load,3)]})\n",
    "\n",
    "metrics.index = ['Baseline','Custom']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f83cdf8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "╒══════════╤══════════════════════════╤══════════════════════╤════════════════════╤═════════════════════╕\n",
      "│          │  Mean Job_Response_Time  │  Mean Job_Slow_Down  │  Mean Utilization  │  Mean Message_Load  │\n",
      "╞══════════╪══════════════════════════╪══════════════════════╪════════════════════╪═════════════════════╡\n",
      "│ Baseline │         27470.2          │     1.20373e+06      │      0.538425      │         129         │\n",
      "├──────────┼──────────────────────────┼──────────────────────┼────────────────────┼─────────────────────┤\n",
      "│  Custom  │         756.528          │       1614.61        │      0.507027      │        5.606        │\n",
      "╘══════════╧══════════════════════════╧══════════════════════╧════════════════════╧═════════════════════╛\n"
     ]
    }
   ],
   "source": [
    "print()\n",
    "print(tabulate(metrics, tablefmt=\"fancy_grid\", headers= metrics.columns, numalign='center', stralign=\"center\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "690b2759-b010-4c42-bf4e-40bf9061abe6",
   "metadata": {},
   "source": [
    "## Analysis and comments:\n",
    "\n",
    "-  As we can see surely our analysis has been experimentally confirmed: the SITA-E with FCFS servers strongly improved time performances.\\\n",
    "   The right tail of the distributions in the custom case is very weaker.\n",
    "\n",
    "- For what concerns utilization we didn’t achieved any improvement: this is due to the strong balancing we induced that sets every server on the same amount of work time-wise speaking. The sub-LWL we designed over server 0, 62 and 63 surely affects their utilization.\n",
    "\n",
    "- Message load in the baseline case is a constant for all task, since 128 messages are needed to retrieve the least amount of work left (64 queries and 64 answers), plus 1 message to send the task; in the custom scenario we have to average between pure SITA where we only need 1 message and the sub-LWL where we need 7 messages for task (3 queries over server 0, 62 and 63, 3 answers and 1 task-message)\n",
    "\n",
    "- . . ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "217d8ac8-c660-4a19-9c40-45f928bda474",
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