Scheduling Algorithms (OS).ipynb

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  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "## First come First serve (FCFS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5314220c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the no. of processes :4\n",
      "Enter Start Time of process p1 :\n",
      "0\n",
      "Enter Start Time of process p2 :\n",
      "6\n",
      "Enter Start Time of process p3 :\n",
      "10\n",
      "Enter Start Time of process p4 :\n",
      "13\n",
      "Enter CPU Time of process p1 :\n",
      "6\n",
      "Enter CPU Time of process p2 :\n",
      "4\n",
      "Enter CPU Time of process p3 :\n",
      "5\n",
      "Enter CPU Time of process p4 :\n",
      "3\n",
      "Enter Arrival Time of process p1 :\n",
      "0\n",
      "Enter Arrival Time of process p2 :\n",
      "3\n",
      "Enter Arrival Time of process p3 :\n",
      "6\n",
      "Enter Arrival Time of process p4 :\n",
      "2\n",
      "______________________________________________________________\n",
      "\n",
      "Average Turn-Around Time : 9.0\n",
      "Average Waiting Time : 4.5\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Process</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Finish Time</th>\n",
       "      <th>CPU Time</th>\n",
       "      <th>Arrival Time</th>\n",
       "      <th>Turn Around Time</th>\n",
       "      <th>Waiting Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p2</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p3</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p4</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "  Process  Start Time  Finish Time  CPU Time  Arrival Time  Turn Around Time  \\\n",
       "0      p1           0            6         6             0                 6   \n",
       "1      p2           6           10         4             3                 7   \n",
       "2      p3          10           15         5             6                 9   \n",
       "3      p4          13           16         3             2                14   \n",
       "\n",
       "   Waiting Time  \n",
       "0             0  \n",
       "1             3  \n",
       "2             4  \n",
       "3            11  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "def FCFS(): \n",
    "    start_time=[]\n",
    "    x=int(input('Enter the no. of processes :'))\n",
    "    process=[]\n",
    "    for i in range(1,x+1):\n",
    "        process.append('p'+str(i))\n",
    "    for i in range(x):\n",
    "        print('Enter Start Time of process',process[i],':')\n",
    "        a=int(input())\n",
    "        start_time.append(a)\n",
    "    cpu_time=[]\n",
    "    for i in range(x):\n",
    "        print('Enter CPU Time of process',process[i],':')\n",
    "        a=int(input())\n",
    "        cpu_time.append(a)\n",
    "\n",
    "    arrival_time=[]\n",
    "    for i in range(x):\n",
    "        print('Enter Arrival Time of process',process[i],':')\n",
    "        a=int(input())\n",
    "        arrival_time.append(a)\n",
    "\n",
    "    finish_time=[]\n",
    "    for i in range(0,x):\n",
    "        finish_time.append(start_time[i]+cpu_time[i])\n",
    "\n",
    "\n",
    "    Turn_around=[]\n",
    "    for i in range(0,x):\n",
    "        Turn_around.append(finish_time[i]-arrival_time[i])\n",
    "\n",
    "\n",
    "    waiting_Time=[]\n",
    "    for i in range(0,x):\n",
    "        waiting_Time.append(Turn_around[i]-cpu_time[i])\n",
    "\n",
    "    avg_tat=0\n",
    "    avg_wait=0\n",
    "    for i in range(0,x):\n",
    "        avg_tat=avg_tat+Turn_around[i]\n",
    "        avg_wait=avg_wait+waiting_Time[i]\n",
    "    df=pd.DataFrame(list(zip(process,start_time,finish_time,\n",
    "                         cpu_time,arrival_time,Turn_around,waiting_Time)),\n",
    "                columns=['Process','Start Time','Finish Time',\n",
    "                         'CPU Time','Arrival Time','Turn Around Time','Waiting Time'])\n",
    "    print('______________________________________________________________\\n')\n",
    "    print('Average Turn-Around Time :',avg_tat/x)\n",
    "    print(\"Average Waiting Time :\",avg_wait/x)\n",
    "    return df\n",
    "df=FCFS()   \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c60248ad",
   "metadata": {},
   "source": [
    "# Shortest Job First (SJF) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54705f16",
   "metadata": {},
   "source": [
    "> The Process having the less CPU time will be executed first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9ca70e9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the no. of processes :4\n",
      "Enter Start Time of process p1 :\n",
      "0\n",
      "Enter Start Time of process p2 :\n",
      "6\n",
      "Enter Start Time of process p3 :\n",
      "19\n",
      "Enter Start Time of process p4 :\n",
      "22\n",
      "Enter CPU Time of process p1 :\n",
      "6\n",
      "Enter CPU Time of process p2 :\n",
      "4\n",
      "Enter CPU Time of process p3 :\n",
      "2\n",
      "Enter CPU Time of process p4 :\n",
      "1\n",
      "Enter Arrival Time of process p1 :\n",
      "0\n",
      "Enter Arrival Time of process p2 :\n",
      "4\n",
      "Enter Arrival Time of process p3 :\n",
      "3\n",
      "Enter Arrival Time of process p4 :\n",
      "1\n",
      "______________________________________________________________\n",
      "\n",
      "Average Turn-Around Time : 13.0\n",
      "Average Waiting Time : 9.75\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Process</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Finish Time</th>\n",
       "      <th>CPU Time</th>\n",
       "      <th>Arrival Time</th>\n",
       "      <th>Turn Around Time</th>\n",
       "      <th>Waiting Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p2</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p3</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p4</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "  Process  Start Time  Finish Time  CPU Time  Arrival Time  Turn Around Time  \\\n",
       "0      p1           0            6         6             0                 6   \n",
       "1      p2           6           10         4             4                 6   \n",
       "2      p3          19           21         2             3                18   \n",
       "3      p4          22           23         1             1                22   \n",
       "\n",
       "   Waiting Time  \n",
       "0             0  \n",
       "1             2  \n",
       "2            16  \n",
       "3            21  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def SJN(): \n",
    "    start_time=[]\n",
    "    x=int(input('Enter the no. of processes :'))\n",
    "    process=[]\n",
    "    for i in range(1,x+1):\n",
    "        process.append('p'+str(i))\n",
    "    for i in range(x):\n",
    "        print('Enter Start Time of process',process[i],':')\n",
    "        a=int(input())\n",
    "        start_time.append(a)\n",
    "    cpu_time=[]\n",
    "    for i in range(x):\n",
    "        print('Enter CPU Time of process',process[i],':')\n",
    "        a=int(input())\n",
    "        cpu_time.append(a)\n",
    "\n",
    "    arrival_time=[]\n",
    "    for i in range(x):\n",
    "        print('Enter Arrival Time of process',process[i],':')\n",
    "        a=int(input())\n",
    "        arrival_time.append(a)\n",
    "\n",
    "    finish_time=[]\n",
    "    for i in range(0,x):\n",
    "        finish_time.append(start_time[i]+cpu_time[i])\n",
    "\n",
    "\n",
    "    Turn_around=[]\n",
    "    for i in range(0,x):\n",
    "        Turn_around.append(finish_time[i]-arrival_time[i])\n",
    "\n",
    "\n",
    "    waiting_Time=[]\n",
    "    for i in range(0,x):\n",
    "        waiting_Time.append(Turn_around[i]-cpu_time[i])\n",
    "\n",
    "    avg_tat=0\n",
    "    avg_wait=0\n",
    "    for i in range(0,x):\n",
    "        avg_tat=avg_tat+Turn_around[i]\n",
    "        avg_wait=avg_wait+waiting_Time[i]\n",
    "    df=pd.DataFrame(list(zip(process,start_time,finish_time,\n",
    "                         cpu_time,arrival_time,Turn_around,waiting_Time)),\n",
    "                columns=['Process','Start Time','Finish Time',\n",
    "                         'CPU Time','Arrival Time','Turn Around Time','Waiting Time'])\n",
    "    print('______________________________________________________________\\n')\n",
    "    print('Average Turn-Around Time :',avg_tat/x)\n",
    "    print(\"Average Waiting Time :\",avg_wait/x)\n",
    "    return df\n",
    "df=SJN()   \n",
    "df"
   ]
  },
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     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs0AAAD4CAYAAAAXZ9u2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAATO0lEQVR4nO3df7BmdX0f8Pdnf8gKCgw/xhIWcomSFWEJxDVTRa3FNMaB+mPiTMtYixhDiFjdZsaUdLSZzFRnp6UZR0ZNEEhokVgHizJq1GipBKYNu8jywyzYNS6yGn+ABiIM8uvTP/Zhu+JdzwO7Z5/nwus1s7PnPM89z3nvPbNz3/O93/M91d0BAAB2b9msAwAAwLxTmgEAYIDSDAAAA5RmAAAYoDQDAMCAFbMOMI3DDjusFxYWZh0DAICnsBtuuOGu7j58sfeWRGleWFjIpk2bZh0DAICnsKq6Y3fvmZ4BAAADlGYAABigNAMAwIAlMacZAIDkoYceyvbt2/PAAw/MOsqStmrVqqxevTorV66c+hilGQBgidi+fXue/exnZ2FhIVU16zhLUnfn7rvvzvbt23PMMcdMfZzpGQAAS8QDDzyQQw89VGHeA1WVQw899AmP1ivNAABLiMK8557M91BpBgCAAeY0AwAsUQvnfWavft62DacNfs13vvOdrF+/Phs3bsx+++2XhYWFvP/9788znvGMHHfccVmzZk0efPDBvPzlL8+HPvShXHPNNTn//PPz6U9/eudnvPnNb87pp5+eN7zhDTtfO/fcc3PdddflwQcfzDe+8Y2sWbMmSfLud787l1xySS6//PIcfPDBe/Xf+0QozQAATKW78/rXvz5nnnlmPvaxjyVJNm/enO9+97s56qij8tznPjebN2/Oww8/nFNPPTWf/OQnc8ghh0z12R/84AeTJNu2bcvpp5+ezZs373xv13I9K6ZnAAAwlauvvjorV67MOeecs/O1k046KS972ct+4utWrFiRl7zkJdm6deteOe/CwkLuuuuubNu2Lc9//vPz1re+NSeccELe+MY35otf/GJOOeWUHHvssbn++uuTJPfdd1/e8pa35EUvelFOPvnkfOpTn9rjDEozAABTufXWW/PCF75w8Ovuv//+fOlLX8ratWv3eoatW7fmne98Z26++ebcdtttufzyy3Pttdfm/PPPz/ve974kyXvf+96ceuqp2bhxY66++uq8613vyn333bdH510S0zNu+dY9e33ODnvfNPOgAICnrq9//es56aSTUlV57Wtfm1e/+tX58pe/vOjXPtlVQI455pidZfz444/PK1/5ylRV1q5dm23btiVJvvCFL+Sqq67K+eefn2THUn3f/OY3c9xxxz2pcyZLpDQDADB7xx9/fK644ordvv/YnOZdHXroofnhD3/4E6/94Ac/yGGHHfakMuy33347t5ctW7Zzf9myZXn44YeT7Jh7/YlPfGLnzYR7g+kZAABM5dRTT82Pf/zjfOQjH9n52saNG3c7mpwkxx57bL797W9ny5YtSZI77rgjN910U0466aTRcr7qVa/KBRdckO5Oktx44417/JlGmgEAlqh9PTWyqnLllVdm/fr12bBhQ1atWrVzybnd2W+//XLZZZflrLPOygMPPJCVK1fmoosuykEHHTRazve85z1Zv359TjzxxHR3FhYWfmLJuyejHmvg82y/I47tI858/6xjMMCcZgAY15YtW/ZoXi7/32Lfy6q6obvXLfb1pmcAAMAApRkAAAYozQAAS8hSmFo7757M91BpBgBYIlatWpW7775bcd4D3Z277747q1atekLHWT0DAGCJWL16dbZv357vf//7s46ypK1atSqrV69+QsfMrDRX1QVJzuruZ80qAwDAUrJy5cocc8wxs47xtDST6RlVtS7JwbM4NwAAPFGjleaqWqiq26rq0qq6uaquqKr9q2p5kv+c5PfGOjcAAOxNY0/PWJPkN7v7uqq6JMnbkjyU5Kru/ruqGvn0AACw58YuzXd293WT7cuSnJdk/ySvGDqwqs5OcnaSLD/w8LHyAQDAoLHnND9+PZQXJXlekq1VtS3J/lW1ddEDuy/s7nXdvW75/uM9mxwAAIaMXZqPrqoXT7bPSPIfu/sfdfdCdy8kub+7nzdyBgAA2CNjl+YtSc6sqpuTHJLkwyOfDwAA9rqx5zQ/2t3n7O5NazQDALAUeIw2AAAMGG2kubu3JTlhrM8HAIB9xUgzAAAMUJoBAGCA0gwAAAOUZgAAGKA0AwDAAKUZAAAGjP1wk71i7ZEHZdOG02YdAwCApykjzQAAMEBpBgCAAUozAAAMUJoBAGCA0gwAAAOUZgAAGKA0AwDAAKUZAAAGKM0AADBAaQYAgAFKMwAADFCaAQBggNIMAAADlGYAABigNAMAwAClGQAABijNAAAwQGkGAIABSjMAAAxQmgEAYIDSDAAAA5RmAAAYoDQDAMAApRkAAAYozQAAMEBpBgCAAStmHWAat3zrniyc95lZx4CnhG0bTpt1BABYcow0AwDAAKUZAAAGKM0AADBAaQYAgAFKMwAADFCaAQBggNIMAAADlGYAABigNAMAwAClGQAABuzz0lxVF1fVTVV1c1VdUVXP2tcZAADgiZjFSPO/7e5f6u4Tk3wzydtnkAEAAKY2WmmuqoWquq2qLt1lVHn/7r538n4leWaSHisDAADsDWOPNK9JcuFkVPneJG9Lkqr60yTfSfL8JBcsdmBVnV1Vm6pq0yP33zNyTAAA2L2xS/Od3X3dZPuyJC9Nku4+K8nPJdmS5F8sdmB3X9jd67p73fL9Dxo5JgAA7N7YpfnxUy927nf3I0n+e5LfGDkDAADskbFL89FV9eLJ9hlJrq2q5yU75zT/8yS3jZwBAAD2yIqRP39LkjOr6k+S/N8kH07yl1V1YJJKclOS3xk5AwAA7JGxS/Oj3X3O4147ZeRzAgDAXuWJgAAAMGC0kebu3pbkhLE+HwAA9hUjzQAAMEBpBgCAAUozAAAMmKo0V9UBVbVssv2LVfWaqlo5bjQAAJgP0440X5NkVVUdmeRLSc5K8mdjhQIAgHky7eoZ1d33V9VvJrmgu/9TVd04ZrBdrT3yoGzacNq+Oh0AAPyEaUeaa/I47Dcm+czktbEfjAIAAHNh2tK8PsnvJ7myu79aVb+Q5OrRUgEAwByZarS4u7+c5MtJMrkh8K7ufseYwQAAYF5Mu3rG5VV1YFUdkORvktxeVe8aNxoAAMyHaadnvKC7703yuiSfTXJ0kjeNFQoAAObJtKV55WRd5tcl+VR3P5SkR0sFAABzZNrS/CdJtiU5IMk1VfXzSe4dKxQAAMyTaW8E/ECSD+zy0h1V9U/HiQQAAPNl2hsBn1NVF1fVX0z2X5DkzFGTAQDAnJh2esafJfl8kp+b7H8tO9ZuBgCAp7xpS/Nh3f3xJI8mSXc/nOSR0VIBAMAcmbY031dVh2ayYkZV/eMk94yWCgAA5shUNwIm+d0kVyV5blVdl+TwJG8YLRUAAMyRaVfP+EpV/ZMka5JUktsnazUDAMBT3rSrZ5yb5Fnd/dXuvjXJs6rqbeNGAwCA+TDtnObf6u6/f2ynu3+Y5LdGSQQAAHNm2tK8rKrqsZ2qWp7kGeNEAgCA+TLtjYCfT/Lxqvrj7FhB45wknxstFQAAzJFpS/O/S/LbSX4nO24E/EKSi8YKBQAA82Ta1TMeraqLk1ybHSPNt3e3h5sAAPC0MFVprqpXJLk0ybbsGGk+qqrO7O5rRksGAABzYtrpGf8lya919+1JUlW/mOTPk7xwrGAAADAvpl09Y+VjhTlJuvtrSVaOEwkAAObLtCPNN0zmNP+3yf4bk9wwTiQAAJgv05bmc5Kcm+Qd2TGn+ZokHxorFAAAzJPB0lxVy5Lc0N0nJPmj8SMBAMB8GZzT3N2PJrmpqo7eB3kAAGDuTDs944gkX62q65Pc99iL3f2aUVI9zi3fuicL531mX5wKAJ6QbRtOm3UEYB+YtjT/4agpAABgjv3M0lxVq7LjJsDnJbklycXd/fC+CAYAAPNiaE7zpUnWZUdhfnV2POQEAACeVoamZ7ygu9cmyWSd5uvHjwQAAPNlaKT5occ2TMsAAODpamik+Zeq6t7JdiV55mS/knR3HzhqOgAAmAM/szR39/J9FQQAAObV4MNNAADg6U5pBgCAAfu8NFfVR6vq9qq6taouqaqV+zoDAAA8EbMYaf5okucnWZvkmUneOoMMAAAwtdFKc1UtVNVtVXVpVd1cVVdU1f7d/dmeyI51n1ePlQEAAPaGsUea1yS5sLtPTHJvkrc99sZkWsabknxusQOr6uyq2lRVmx65/56RYwIAwO6NXZrv7O7rJtuXJXnpLu99KMk13f1Xix3Y3Rd297ruXrd8/4NGjgkAALs39HCTPdWL7VfVHyQ5PMlvj3x+AADYY2OPNB9dVS+ebJ+R5NqqemuSVyU5o7sfHfn8AACwx8YuzVuSnFlVNyc5JMmHk/xxkuck+d9Vtbmq/sPIGQAAYI+MPT3j0e4+Zx+fEwAA9ipPBAQAgAGjjfp297YkJ4z1+QAAsK8YaQYAgAFKMwAADFCaAQBggNIMAAADlGYAABigNAMAwIAl8aCRtUcelE0bTpt1DAAAnqaMNAMAwAClGQAABijNAAAwQGkGAIABSjMAAAxQmgEAYIDSDAAAA5RmAAAYoDQDAMAApRkAAAYozQAAMEBpBgCAAUozAAAMUJoBAGCA0gwAAAOUZgAAGKA0AwDAAKUZAAAGKM0AADBAaQYAgAFKMwAADFCaAQBggNIMAAADlGYAABigNAMAwAClGQAABqyYdYBp3PKte7Jw3mdmHQMAgJFt23DarCMsykgzAAAMUJoBAGCA0gwAAAOUZgAAGKA0AwDAAKUZAAAGKM0AADBAaQYAgAFKMwAADNjnpbmq3l5VW6uqq+qwfX1+AAB4omYx0nxdkl9NcscMzg0AAE/YirE+uKoWknwuyV8nOTnJ15L86+6+cfL+WKcGAIC9auyR5jVJLuzuE5Pcm+RtI58PAAD2urFL853dfd1k+7IkL532wKo6u6o2VdWmR+6/Z5x0AAAwhbFLcw/s7/7A7gu7e113r1u+/0F7ORYAAExv7NJ8dFW9eLJ9RpJrRz4fAADsdWOX5i1Jzqyqm5MckuTDVfWOqtqeZHWSm6vqopEzAADAHhlt9YyJR7v7nMe99oHJHwAAWBI8ERAAAAaMNtLc3duSnDDW5wMAwL5ipBkAAAYozQAAMEBpBgCAAUozAAAMUJoBAGCA0gwAAAPGfrjJXrH2yIOyacNps44BAMDTlJFmAAAYoDQDAMAApRkAAAYozQAAMEBpBgCAAUozAAAMUJoBAGCA0gwAAAOUZgAAGKA0AwDAAKUZAAAGKM0AADBAaQYAgAHV3bPOMKiq/iHJ7bPOwaDDktw16xAMcp2WDtdqaXCdlgbXaemY5bX6+e4+fLE3VuzrJE/S7d29btYh+NmqapPrNP9cp6XDtVoaXKelwXVaOub1WpmeAQAAA5RmAAAYsFRK84WzDsBUXKelwXVaOlyrpcF1Whpcp6VjLq/VkrgREAAAZmmpjDQDAMDMKM0AADBgrktzVf16Vd1eVVur6rxZ52FxVXVJVX2vqm6ddRZ2r6qOqqqrq2pLVX21qt4560z8tKpaVVXXV9VNk+v0h7POxO5V1fKqurGqPj3rLOxeVW2rqluqanNVbZp1HhZXVQdX1RVVddvkZ9WLZ51pV3M7p7mqlif5WpJ/lmR7ko1Jzujuv5lpMH5KVb08yY+S/NfuPmHWeVhcVR2R5Iju/kpVPTvJDUle5//UfKmqSnJAd/+oqlYmuTbJO7v7/8w4Gouoqt9Nsi7Jgd19+qzzsLiq2pZkXXd7uMkcq6pLk/xVd19UVc9Isn93//2MY+00zyPNv5Jka3f/bXc/mORjSV4740wsoruvSfKDWefgZ+vuv+vur0y2/yHJliRHzjYVj9c7/Giyu3LyZz5HN57mqmp1ktOSXDTrLLDUVdWBSV6e5OIk6e4H56kwJ/Ndmo9Mcucu+9vjBzzsFVW1kOTkJH894ygsYvIr/81JvpfkL7vbdZpP70/ye0kenXEOhnWSL1TVDVV19qzDsKhfSPL9JH86mfJ0UVUdMOtQu5rn0lyLvGa0BfZQVT0rySeSrO/ue2edh5/W3Y9090lJVif5laoy7WnOVNXpSb7X3TfMOgtTOaW7fznJq5OcO5lWyHxZkeSXk3y4u09Ocl+SubqfbZ5L8/YkR+2yvzrJt2eUBZ4SJnNkP5Hko939P2adh59t8qvJ/5Xk12ebhEWckuQ1k7myH0tyalVdNttI7E53f3vy9/eSXJkdU0CZL9uTbN/lN2tXZEeJnhvzXJo3Jjm2qo6ZTAb/l0mumnEmWLImN5hdnGRLd//RrPOwuKo6vKoOnmw/M8mvJrltpqH4Kd39+929ursXsuPn0//s7n8141gsoqoOmNz8nMmv+38tidWe5kx3fyfJnVW1ZvLSK5PM1Y3qK2YdYHe6++GqenuSzydZnuSS7v7qjGOxiKr68ySvSHJYVW1P8gfdffFsU7GIU5K8Kcktk/mySfLvu/uzs4vEIo5IculkBaFlST7e3ZYzgyfvOUmu3DFukBVJLu/uz802Ervxb5J8dDJY+rdJzppxnp8wt0vOAQDAvJjn6RkAADAXlGYAABigNAMAwAClGQAABijNAAAwQGkGAIABSjMAAAz4f2vsY90iVnh1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot('Process','CPU Time',kind='barh',figsize=(12,4));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd8f1e18",
   "metadata": {},
   "source": [
    "> Setting the index according ascending order of CPU Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59273d1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "l1=np.sort(df['CPU Time'])\n",
    "index=[]\n",
    "for i in range(0,len(l1)):\n",
    "    for j in range(0,len(l1)):\n",
    "        if l1[i]==df['CPU Time'][j]:\n",
    "              index.append(j)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a13fea3",
   "metadata": {},
   "source": [
    "> Assigning the ascending index to DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "83a1d284",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.reindex(index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f0b73fd",
   "metadata": {},
   "source": [
    "> Plot Showing the Shortest Job will be Executed First"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "45c7ec2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot('Process','CPU Time',kind='barh',figsize=(12,4));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60f007fd",
   "metadata": {},
   "source": [
    "## Priority Scheduling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7ff3ed5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the no. of processes :4\n",
      "Enter Start Time of process p1 :\n",
      "0\n",
      "Enter Start Time of process p2 :\n",
      "6\n",
      "Enter Start Time of process p3 :\n",
      "10\n",
      "Enter Start Time of process p4 :\n",
      "13\n",
      "Enter CPU Time of process p1 :\n",
      "6\n",
      "Enter CPU Time of process p2 :\n",
      "4\n",
      "Enter CPU Time of process p3 :\n",
      "3\n",
      "Enter CPU Time of process p4 :\n",
      "1\n",
      "Enter Arrival Time of process p1 :\n",
      "0\n",
      "Enter Arrival Time of process p2 :\n",
      "4\n",
      "Enter Arrival Time of process p3 :\n",
      "3\n",
      "Enter Arrival Time of process p4 :\n",
      "1\n",
      "Enter Priority of process p1 :\n",
      "4\n",
      "Enter Priority of process p2 :\n",
      "2\n",
      "Enter Priority of process p3 :\n",
      "1\n",
      "Enter Priority of process p4 :\n",
      "3\n",
      "______________________________________________________________\n",
      "\n",
      "Average Turn-Around Time : 8.75\n",
      "Average Waiting Time : 5.25\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>Process</th>\n",
       "      <th>Priority</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Finish Time</th>\n",
       "      <th>CPU Time</th>\n",
       "      <th>Arrival Time</th>\n",
       "      <th>Turn Around Time</th>\n",
       "      <th>Waiting Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p4</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Process  Priority  Start Time  Finish Time  CPU Time  Arrival Time  \\\n",
       "0      p1         4           0            6         6             0   \n",
       "1      p2         2           6           10         4             4   \n",
       "2      p3         1          10           13         3             3   \n",
       "3      p4         3          13           14         1             1   \n",
       "\n",
       "   Turn Around Time  Waiting Time  \n",
       "0                 6             0  \n",
       "1                 6             2  \n",
       "2                10             7  \n",
       "3                13            12  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def PS(): \n",
    "    start_time=[]\n",
    "    x=int(input('Enter the no. of processes :'))\n",
    "    process=[]\n",
    "    for i in range(1,x+1):\n",
    "        process.append('p'+str(i))\n",
    "    for i in range(x):\n",
    "        print('Enter Start Time of process',process[i],':')\n",
    "        a=int(input())\n",
    "        start_time.append(a)\n",
    "    cpu_time=[]\n",
    "    for i in range(x):\n",
    "        print('Enter CPU Time of process',process[i],':')\n",
    "        a=int(input())\n",
    "        cpu_time.append(a)\n",
    "\n",
    "    arrival_time=[]\n",
    "    for i in range(x):\n",
    "        print('Enter Arrival Time of process',process[i],':')\n",
    "        a=int(input())\n",
    "        arrival_time.append(a)\n",
    "    \n",
    "    priority=[]\n",
    "    for i in range(x):\n",
    "        print('Enter Priority of process',process[i],':')\n",
    "        a=int(input())\n",
    "        priority.append(a)\n",
    "\n",
    "    finish_time=[]\n",
    "    for i in range(0,x):\n",
    "        finish_time.append(start_time[i]+cpu_time[i])\n",
    "\n",
    "\n",
    "    Turn_around=[]\n",
    "    for i in range(0,x):\n",
    "        Turn_around.append(finish_time[i]-arrival_time[i])\n",
    "\n",
    "\n",
    "    waiting_Time=[]\n",
    "    for i in range(0,x):\n",
    "        waiting_Time.append(Turn_around[i]-cpu_time[i])\n",
    "        \n",
    "\n",
    "    \n",
    "    avg_tat=0\n",
    "    avg_wait=0\n",
    "    for i in range(0,x):\n",
    "        avg_tat=avg_tat+Turn_around[i]\n",
    "        avg_wait=avg_wait+waiting_Time[i]\n",
    "        \n",
    "    df=pd.DataFrame(list(zip(process,priority,start_time,finish_time,\n",
    "                         cpu_time,arrival_time,Turn_around,waiting_Time)),\n",
    "                columns=['Process','Priority','Start Time','Finish Time',\n",
    "                         'CPU Time','Arrival Time','Turn Around Time','Waiting Time'])\n",
    "    print('______________________________________________________________\\n')\n",
    "    print('Average Turn-Around Time :',avg_tat/x)\n",
    "    print(\"Average Waiting Time :\",avg_wait/x)\n",
    "    return df\n",
    "df=PS()   \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ca837a2",
   "metadata": {},
   "source": [
    "> Setting the index according ascending order of Priority"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b24615d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "l1=np.sort(df['Priority'])\n",
    "index=[]\n",
    "for i in range(0,len(l1)):\n",
    "    for j in range(0,len(l1)):\n",
    "        if l1[i]==df['Priority'][j]:\n",
    "              index.append(j)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "916d8cee",
   "metadata": {},
   "source": [
    "> Assigning the ascending index to DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1bfb67ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.reindex(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "73917b25",
   "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>Process</th>\n",
       "      <th>Priority</th>\n",
       "      <th>Start Time</th>\n",
       "      <th>Finish Time</th>\n",
       "      <th>CPU Time</th>\n",
       "      <th>Arrival Time</th>\n",
       "      <th>Turn Around Time</th>\n",
       "      <th>Waiting Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>p3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>p2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>p4</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>p1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Process  Priority  Start Time  Finish Time  CPU Time  Arrival Time  \\\n",
       "2      p3         1          10           13         3             3   \n",
       "1      p2         2           6           10         4             4   \n",
       "3      p4         3          13           14         1             1   \n",
       "0      p1         4           0            6         6             0   \n",
       "\n",
       "   Turn Around Time  Waiting Time  \n",
       "2                10             7  \n",
       "1                 6             2  \n",
       "3                13            12  \n",
       "0                 6             0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f945a5b3",
   "metadata": {},
   "source": [
    "## Round Robin (RR) Scheduling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "df7260f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the number of processes :4\n",
      "Enter the CPU Time for  p1 :\n",
      "6\n",
      "Enter the CPU Time for  p2 :\n",
      "4\n",
      "Enter the CPU Time for  p3 :\n",
      "3\n",
      "Enter the CPU Time for  p4 :\n",
      "1\n",
      "Enter Value of Time quantum :2\n",
      "______________________________________________________________________\n",
      "Order of processes going to be executed :\n",
      "______________________________________________________________________\n",
      "p1==>p2==>p3==>p4==>p1==>p2==>p3==>p1==>"
     ]
    }
   ],
   "source": [
    "x=int(input('Enter the number of processes :'))\n",
    "\n",
    "states=[]\n",
    "for i in range(1,x+1):\n",
    "    states.append('p'+str(i))\n",
    "cpu_time=[]\n",
    "\n",
    "\n",
    "for i in range(x):\n",
    "    print('Enter the CPU Time for ',states[i],':')\n",
    "    a=int(input())\n",
    "    cpu_time.append(a)\n",
    "q=int(input('Enter Value of Time quantum :'))\n",
    "\n",
    "t=0\n",
    "ready=[]\n",
    "cnt=0\n",
    "remain=cpu_time\n",
    "\n",
    "print('______________________________________________________________________')\n",
    "print('Order of processes going to be executed :')\n",
    "print('______________________________________________________________________')\n",
    "\n",
    "while(True):\n",
    "        all_zeros=True\n",
    "        \n",
    "        for i in range(x):\n",
    "            if remain[i]>q:\n",
    "                all_zeros=False\n",
    "                t+=q\n",
    "                print('p'+str(i+1),end='==>')\n",
    "                remain[i]-=q\n",
    "            else:\n",
    "                if remain[i]>0:\n",
    "                            t+=remain[i]\n",
    "                            print('p'+str(i+1),end='==>')\n",
    "                remain[i]=0\n",
    "        if(all_zeros==True):\n",
    "            break\n"
   ]
  }
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