aibrix/benchmarks/plot/aibrix0.3-routing_vtc-basic-vs-random.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Routing algo comparision vtc-basic vs random\n",
    "\n",
    "## Benchmark Configuration\n",
    "- Dataset - https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json\n",
    "- VTC algo goal is to provide fairness to low & medium token users from high token users. So the dataset is categorized into following user categories:\n",
    "     - small (10-100 input tokens per req)\n",
    "     - medium (100-300 input tokens per req)\n",
    "     - high (300-800 input tokens per req)\n",
    "- Output tokens was limited to max of 200 as it is sufficient to measure routing algo perf.\n",
    "- 500 non streaming requests for `vtc-basic` & `random` in each run\n",
    "- QPS per run 1,2,3,5\n",
    "- Traffic patterns:\n",
    "     - Balanced: Steady traffic with balanced distribution (40% small, 35% medium, 25% high), burstiness (0.1-0.2)\n",
    "     - High Usage: Moderate load favoring medium users (30% small, 40% medium, 30% high), burstiness (0.1-0.2)\n",
    "     - Bursty: Small-user heavy with high irregular spikes (50% small, 30% medium, 20% high), high burstiness (0.4-0.6)\n",
    "     - High Med Pressure: Stress test with few small users under pressure (15% small, 50% medium, 35% high), very high burstiness (0.1-0.8)\n",
    "\n",
    "### Benchmark Script & Results\n",
    "- Custom benchmark scripts can be found at: https://github.com/Venkat2811/aibrix/blob/vtc-bench-v0.3.1/benchmarks/plot/aibrix0.3-routing/vtc-basic/bench/README.md\n",
    "- Results used in analysis can be found at: https://github.com/Venkat2811/aibrix/blob/vtc-bench-v0.3.1/benchmarks/plot/aibrix0.3-routing/run.zip\n",
    "\n",
    "### Analysis Summary\n",
    "- `vtc-basic` is not so useful in `balanced` traffic pattern as expected. In `high_usage` traffic pattern, we can see ~4.5% improvement in latency when compared to `random` with minimal impact on high token users.\n",
    "- Perf is dependant on config params. So knowing workload pattern upfront and configuring correctly is also important\n",
    "- This notebook will show that `vtc-basic` is a good starting point for fairness based routing. See complete analysis below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## System Configuration\n",
    "\n",
    "**Instance**: Lambda Labs `gpu_1x_gh200`  \n",
    "**OS**: Ubuntu 22.04.5 LTS (ARM64)  \n",
    "**Kernel**: Linux 6.8.0-1013-nvidia-64k  \n",
    "**Kubernetes**: MicroK8s v1.32.3  \n",
    "\n",
    "### GPU Setup\n",
    "- **GPU**: NVIDIA GH200 480GB (Grace Hopper)\n",
    "- **Driver**: 570.124.06\n",
    "- **Total VRAM**: 97,871 MiB (~95.6 GB)\n",
    "- **MIG Config**: 3x `nvidia.com/mig-2g.24gb` devices (24GB each)\n",
    "\n",
    "### Model Deployment  \n",
    "- **Model**: TinyLLaMA-1.1B-Chat-v1.0\n",
    "- **Engine**: vLLM (`substratusai/vllm-gh200:v0.8.3`)\n",
    "- **Replicas**: 3 pods, each isolated on separate MIG device\n",
    "- **K8s Plugin**: nvidia/k8s-device-plugin:v0.17.1 (`--mig-strategy=mixed`)\n",
    "- Custom config for VTC:\n",
    "  -  `AIBRIX_ROUTER_VTC_TOKEN_TRACKER_WINDOW_SIZE: 3`\n",
    "  -  `AIBRIX_ROUTER_VTC_TOKEN_TRACKER_MIN_TOKENS: 10`\n",
    "  -  `AIBRIX_ROUTER_VTC_TOKEN_TRACKER_MAX_TOKENS: 800`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pathlib import Path\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Set style for clean, professional plots\n",
    "plt.style.use('default')\n",
    "plt.rcParams['figure.dpi'] = 100\n",
    "plt.rcParams['savefig.dpi'] = 150\n",
    "plt.rcParams['font.size'] = 10\n",
    "plt.rcParams['axes.titlesize'] = 12\n",
    "plt.rcParams['axes.labelsize'] = 10\n",
    "plt.rcParams['xtick.labelsize'] = 9\n",
    "plt.rcParams['ytick.labelsize'] = 9\n",
    "plt.rcParams['legend.fontsize'] = 10\n",
    "sns.set_palette(\"tab10\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded data for patterns: ['balanced', 'high_usage', 'bursty', 'high_med_pressure']\n"
     ]
    }
   ],
   "source": [
    "# Load data from run directory\n",
    "def load_run_data(base_path):\n",
    "    patterns = ['balanced', 'high_usage', 'bursty', 'high_med_pressure']\n",
    "    all_data = {}\n",
    "    \n",
    "    for pattern in patterns:\n",
    "        run_dirs = list(Path(base_path).glob(f\"*{pattern}*\"))\n",
    "        if not run_dirs:\n",
    "            continue\n",
    "            \n",
    "        run_dir = run_dirs[0]\n",
    "        pattern_data = {}\n",
    "        \n",
    "        fairness_file = run_dir / \"fairness_analysis.json\"\n",
    "        if fairness_file.exists():\n",
    "            with open(fairness_file, 'r') as f:\n",
    "                pattern_data['fairness'] = json.load(f)\n",
    "                \n",
    "        all_data[pattern] = pattern_data\n",
    "        \n",
    "    return all_data\n",
    "\n",
    "base_path = \"/Users/venkat/Downloads/lambdalabs/run\"\n",
    "data = load_run_data(base_path)\n",
    "print(\"Loaded data for patterns:\", list(data.keys()))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chart 1: Small User Protection (Primary Fairness Goal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "patterns = ['balanced', 'high_usage', 'bursty', 'high_med_pressure']\n",
    "pattern_labels = [p.replace('_', ' ').title() for p in patterns]\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(12, 9))\n",
    "\n",
    "vtc_small_latencies = []\n",
    "random_small_latencies = []\n",
    "improvements = []\n",
    "\n",
    "for pattern in patterns:\n",
    "    if pattern in data and 'fairness' in data[pattern]:\n",
    "        small_data = data[pattern]['fairness']['fairness_comparison']['small']\n",
    "        vtc_small_latencies.append(small_data['vtc_avg_latency'])\n",
    "        random_small_latencies.append(small_data['random_avg_latency'])\n",
    "        improvements.append(small_data['latency_improvement_pct'])\n",
    "\n",
    "x = np.arange(len(pattern_labels))\n",
    "width = 0.35\n",
    "\n",
    "bars1 = ax.bar(x - width/2, vtc_small_latencies, width, label='VTC-Basic', \n",
    "               color='#2ca02c', alpha=0.8, edgecolor='black', linewidth=0.5)\n",
    "bars2 = ax.bar(x + width/2, random_small_latencies, width, label='Random', \n",
    "               color='#d62728', alpha=0.8, edgecolor='black', linewidth=0.5)\n",
    "\n",
    "ax.set_title('Small User Protection Analysis', fontweight='bold', fontsize=14, pad=40)\n",
    "ax.set_ylabel('Small User Latency (seconds)')\n",
    "ax.set_xlabel('Traffic Pattern')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(pattern_labels, rotation=45, ha='right')\n",
    "ax.legend(loc='upper left', fontsize=10)\n",
    "ax.grid(True, alpha=0.3, axis='y')\n",
    "\n",
    "# Add improvement indicators with proper spacing to avoid collision\n",
    "max_bar_height = max(max(vtc_small_latencies), max(random_small_latencies))\n",
    "for i, (vtc_lat, random_lat, improvement) in enumerate(zip(vtc_small_latencies, random_small_latencies, improvements)):\n",
    "    color = '#2ca02c' if improvement > 0 else '#d62728'\n",
    "    status = \"PROTECTED\" if improvement > 0 else \"NOT PROTECTED\"\n",
    "    \n",
    "    # Position improvement percentage above bars\n",
    "    y_pos = max(vtc_lat, random_lat) + max_bar_height * 0.03\n",
    "    ax.text(i, y_pos, f'{improvement:+.1f}%', ha='center', va='bottom', \n",
    "            fontsize=10, fontweight='bold', color=color)\n",
    "    \n",
    "    # Position status text below x-axis to avoid any collision with title\n",
    "    ax.text(i, -max_bar_height * 0.12, status, ha='center', va='top', fontsize=9, \n",
    "            fontweight='bold', color=color)\n",
    "\n",
    "# Adjust y-limits to accommodate status text below\n",
    "ax.set_ylim(-max_bar_height * 0.15, max_bar_height * 1.15)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chart 2: Small User Latency Under Load\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uEJsDAAAAfppIT0hIsC8U9MMPP0i7du3M6erVq7PIKAAAAJCFiM0BAAAAP02k61RRndq5Zs0a+f7776V169Zm/+HDh6Vo0aLeGCMAAAAAF4jNAQAAAD9NpD///PPyxhtvSNOmTeWhhx6SOnXqmP26uJA1rRQAAACA9xGbAwAAAH662KgG6SdPnpRz585J4cKF7ft1kaN8+fJ5enwAAAAA0kBsDgAAAPhpIl0FBQU5BeqqYsWKnhoTAAAAgHQiNgcAAAD8JJFer149CQgISNcNbtq06WrHBAAAACANxOYAAACAnybS27dvbz8dGxsrr732mtSsWVNuvvlms2/9+vWyfft26devn/dGCgAAAIDYHAAAAPDXRPrYsWPtp3v37i0DBw6UiRMnpjomKirK8yMEAAAAYEdsDgAAAGS9wIxe4eOPP5Zu3bql2t+1a1dZunSpp8YFAAAA4AqIzQEAAAA/TaTnzZtX1q1bl2q/7gsLC/PUuAAAAABcAbE5AAAA4EetXRwNGjRInnjiCbNwUaNGjcy+X3/9VRYsWCCjR4/2xhgBAAAAuEBsDgAAAPhpIv3ZZ5+VypUry8svvyzvvfee2VejRg156623pFOnTt4YIwAAAAAXiM0BAAAAP02kKw3KCcwBAAAA3yM2BwAAAPw0ka7i4+Pl+PHjkpyc7LS/fPnynhgXAAAAgHQiNgcAAAD8LJG+d+9e6dWrl/z8889O+202mwQEBEhSUpInxwcAAAAgDcTmAAAAgJ8m0nv06CHBwcHyxRdfSOnSpU2ADgAAACDrEZsDAAAAfppI37Jli2zcuFGqV6/unREBAAAASBdicwAAACBrBGb0CjVr1pSTJ096ZzQAAAAA0o3YHAAAAPDTRPrzzz8vTz/9tPz0009y6tQpOXfunNMGAAAAIGsQmwMAAAB+2tqlZcuW5meLFi2c9rOgEQAAAJC1iM0BAAAAP02kr1y50jsjAQAAAJAhxOYAAACAnybSmzRp4p2RAAAAAMgQYnMAAADATxPp6uzZszJ//nzZuXOnOX/ddddJr169JCIiwtPjAwAAAOAGsTkAAADgh4uN/v7771KlShWZOXOmnD592mwzZsww+zZt2uSdUQIAAABIhdgcAAAA8NOK9MGDB0u7du1k7ty5Ehx8+eqJiYnSu3dvGTRokKxevdob4wQAAACQArE5AAAA4KeJdK16cQzUzY0EB8vTTz8tDRs29PT4AAAAAKSB2BwAAADw09YuBQsWlAMHDqTaHxUVJQUKFPDUuAAAAABcAbE5AAAA4KeJ9M6dO8ujjz4qH374oQnQdVu8eLGZPvrQQw95Z5QAAAAAUiE2BwAAAPy0tcuLL74oAQEB0q1bN9N/UYWEhMgTTzwhzz33nDfGCAAAAMAFYnMAAADATxPpefLkkZdfflmmTp0qf//9t9lXpUoVyZcvnzfGBwAAACANxOYAAACAnybSo6OjJSkpSYoUKSK1a9e27z99+rRZ2Ej7NAIAAADwPmJzAAAAwE97pD/44IOm72JKH330kbkMAAAAQNYgNgcAAAD8NJH+66+/SrNmzVLtb9q0qbkMAAAAQNYgNgcAAAD8NJEeFxdnX8jIUUJCgly6dMlT4wIAAABwBcTmAAAAgJ8m0hs1aiRvvvlmqv1z5syRBg0aeGpcAAAAAK6A2BwAAADw08VGJ02aJC1btpStW7dKixYtzL4VK1bIhg0b5LvvvvPGGAEAAAC4QGwOAAAA+GlFeuPGjeWXX36RcuXKmUWMli9fLlWrVpU//vhDbrvtNu+MEgAAAEAqxOYAAACAn1akq7p168r777/v+dEAAAAAyBBicwAAAMAPK9LV33//LaNGjZIuXbrI8ePHzb6vv/5atm/f7unxAQAAAHCD2BwAAADww0T6qlWrpHbt2vLrr7/K0qVLJSYmxuzXvoxjx471xhgBAAAAuEBsDgAAAPhpIv3ZZ581ixp9//33kidPHvv+5s2by/r16z09PgAAAABpIDYHAAAA/DSRvm3bNunQoUOq/SVKlJCTJ096alwAAAAAroDYHAAAAPDTRHqhQoXkyJEjqfZv3rxZypYt66lxAQAAALgCYnMAAADATxPpDz74oDzzzDNy9OhRCQgIkOTkZFm3bp0MGzZMunXr5p1RAgAAAEiF2BwAAADw00T6lClTpHr16hIZGWkWM6pZs6bcfvvtcsstt8ioUaO8M0oAAAAAqRCbAwAAAFkjOKNX0EWM5s6dK2PGjDE9GTVgr1evnlSrVs07IwQAAADgErE5AAAA4KeJdItWveiWlJRkgvYzZ85I4cKFPTs6AAAAAFdEbA4AAAD4WWuXQYMGyfz5881pDdSbNGki9evXN4H7Tz/95I0xAgAAAHCB2BwAAADw00T6kiVLpE6dOub08uXLZd++fbJr1y4ZPHiwjBw50htjBAAAAOACsTkAAADgp4n0kydPSqlSpczpr776Sjp16iTXXHON9OrVy0wjBQAAAJA1iM0BAAAAP02klyxZUnbs2GGmjn7zzTdyxx13mP0XL16UoKAgb4wRAAAAgAvE5gAAAICfLjbas2dPU+lSunRpCQgIkJYtW5r9v/76q1SvXt0bYwQAAADgArE5AAAA4KeJ9HHjxkmtWrUkKipKHnjgAQkNDTX7teLl2Wef9cYYAQAAALhAbA4AAAD4aSJd3X///an2de/e3RPjAQAAAJABxOYAAACAn/RIX7x4cbpvUKth1q1bdzVjAgAAAJAGYnMAAADATxPpr7/+utSoUUNeeOEF2blzZ6rLo6Oj5auvvpIuXbpI/fr15dSpU94YKwAAAJDrEZsDAAAAftraZdWqVbJs2TKZNWuWDB8+XPLnzy8lS5aUsLAwOXPmjBw9elSKFSsmPXr0kD///NNcBgAAAMDziM0BAAAAP+6R3q5dO7OdPHlS1q5dK//++69cunTJBOn16tUzW2BgugrcAQAAAFwFYnMAAADAzxcb1eC8ffv23hkNAAAAgHQjNgcAAACyBmUqAAAAAAAAAAD4cyI9ISFB+vfvL4ULF5YiRYrIgAEDJDExMc3jtR9k3bp1TS/IMmXKyJw5c7J0vAAAAAAAAACA3MXnifRJkyaZvo47duyQ7du3y5o1a2TKlCkuj/3mm2+kX79+8tJLL8m5c+fM8U2bNs3yMQMAAAAAAAAAcg+fJ9IXLFggo0aNktKlS5tt5MiRMn/+fJfHjh49WsaMGWOS50FBQaaKvXr16lk+ZgAAAAAAAABA7pHhxUZXrlwpzZo188idnzlzRg4ePGhatVj09IEDByQ6OloiIiLs+y9cuCAbN26Uu+66S6655hpTkX7bbbfJK6+8YhLwKcXFxZnNoser5ORks2W1gIAAsQUEZPn9Asg59H3EF+9f/v6cBNh4bwWQ/d5bPXWfnozNAQAAAHgwkd66dWspV66c9OzZU7p37y6RkZGSWTExMeZnoUKF7Pus0+fPn3dKpGvS3WazyWeffSbff/+9FC1aVPr27Stdu3aVFStWpLrtqVOnyvjx41PtP3HihMTGxkpWKxYZKefz5Mny+wXgPQGnT0vgsWMSdOGCSFKS1++vl80mf7W5y+v3k52MPndWAvad9Nrta44+Nl+IHKgcIfvqlJKzJQt47b4A+EZsyVg5fvx4lt+vxrqe4MnYHABygm1FL8r60udld4kkiQ22mXjOG6JtNmn3WTvv3Hg2dLLxSTl17pRXbjswIFDyBOWRiDwREhoc6pX7AACvJNIPHTok7777rrzzzjsmUd28eXN59NFHpX379pIng4ni8PBw81Orz4sVK2Y/rQoUKODy2IEDB0qFChXMab3/atWqmWp1XXzU0fDhw2XIkCFOFen6waJ48eJSsGBByWono6KkQFhYlt8vAO9IPHlSbOfPS56KFSXfna0kyLyveLcyOk5skjef83tdbpf30gUJCPHi856cJEnHTkixtWul+qZNsuT+UvJPVZLpQE5y4dgFKVGiRJbfb5iH4kJPxuYAkN0tq3RGPq0VI+UjKkqL0jdJREhB8dbE8DibSN4UeYjc7MLFCyIhnr/dZFuynLh0Qn4+/LMcuXBEiucrLvlDeN4BZJNEuia8Bw8ebLZNmzbJW2+9ZRYA1a1Lly4mcK9Tp066bkt7nGsFzZYtW6RKlSpmn57WhLdjNbpVqV6+fHmXt6OV6imFhoaaLaXAwECzZTUdY4CLcQLIfpIvXjRJ9CK9ekmhBzubtgBZIcZmk0JFi2bJfWUXtjOnJCCv95//8Cf7SPSE5+S+Jatk1qC8Epc3yOv3CSDrYjRfxIaeuk9PxuYJCQnmdhYtWmT+tz388MMyc+ZMCQ52/ZFh2bJlZv2ivXv3mthdT+uMUQDwhR1FLsqn18VI9+t6SNfKnbweoxObOzvl5bi8d+3e8sKGF2T1wdVSvkB5CQokHgeQ9a4qgq9fv76p/O7fv79p06ILhzZo0MD0Lt++fXu6bkOnoU6ePFmOHj1qtilTpkjv3r1dHtunTx+ZNWuWqby5dOmSTJgwQVq0aGGvVgeArJB07pyElCuXpUl0+FZASLAUGNRPwiREqu72TDsGAPC0q43NJ02aJGvXrpUdO3aY49esWWNic1e++eYbk6x/6aWXzMxPPb5p06ZeeFQAkD6/loqRMgXLZUkSHVkvODBY+tbpKyGBIRKTcLlNMABki0S6VqssWbLELPypbVa+/fZbefXVV+XYsWPy119/mX0PPPBAum5r9OjRcvPNN0uNGjXM1rhxYxkxYoS5TCtaHKtann32WZM416oarVq/ePGimcoKAFnJlpgo+W68kQA9lwkqWkTyVK0iZQ9c9PVQAMArsbkm3keNGiWlS5c228iRI2X+/PlpxvBaga7J86CgIDPTtHr16l54dACQPnuLJ0mjUo2I0XOwImFFpEqhKnIp8ZKvhwIgl8pwa5cBAwbIBx98YKbBPvLII/LCCy9IrVq17Jdrr/IXX3xRypQpk67bCwkJkdmzZ5stpTlz5jid1yB9+vTpZgMAn7ElS1ABZsLkRsEFIyT0XLKvhwEAHo/Nz5w5IwcPHpS6deva9+npAwcOmDWMHNsu6vpEGzduNIn7a665xlSka9X7K6+8YhLwrsTFxZnNotdRycnJZstqmmizkWwDcpRLITYpEJK1MbqrNrO5Vha9peqCo5db5/IeDuQ0AQEBPokLM3KfGU6k61RPba/SsWNHlz3IrV6NK1euzOhNA0A2ESAS4Nl+uqcWLJBLmzZJ7I6dYouNNfuKPzVMCnXokK7rn37/fYn5caVc2rpVks9fbj1S9LHeUmLoUKfjYlatkuMzZkr8gQOSp3x5KTFksIQ3aWK/PCnmgvzdurXkv/FGKTv9RY88tgu//iYHunc3p0tPmSKFOqbvMfklH/RRBoCsiM21FYy1LpHFOn3+/HmnRLom3TWJ8dlnn8n3338vRYsWNbNIu3btKitWrHB5+1OnTjWLoaZ04sQJif3//3tZqVhkpJxnMVYgR0kOOSYBgUFiC/J872x9z3t0TT/ZcnqbOV+rcE154/Y3MpR8mbVllsz/879ZPr8+9KuEBl1+3/4n+h+ZumGq/HnqTymYp6B0uqaT9Lyup9P1n/zxSfnn3D/yadtPJU/Qld+/EpMTZeammfLDgR/k5KWTkmRLksdrPy5P1HlCPOnHqB9l9+ndEp8QL4/WeVS8LTAgUPJJPikj6SveBJB9xJaMlePHj2f5/Wqs67VEelrBsdONBgdLE4fEDADkdLE7dsjRCRPTvLzYE32dEtYpnV38oST/fxIjM85+9LHE7drl9pj4qCg5OGCg5KlcWcovmC9Hx0+QgwP/J5W/WC55IiPNMSdff82Mo8Qw5wQ8AMA/eSo2t9Yc0upzTbxbp1WBAgVcHjtw4EDTNkZpkrxatWqmWl2r4FPS3u1DhgxxqkjXVo3FixeXggULSlY7GRUlBcLCsvx+AXhPYPVEzXhLQFKSfd+fZ3bK6M2u13pQA2o8Js1L337F214e9bU9ia4CbCLJSUnpXjhaE+ALdyx0Hm9goNmSkpNk8OrBcuzCMZnRdIZ8tf8reXnzy1Iqfym5u/Ld5tg1B9fIusPrZHqT6RIWkr73rk/2fCKLdi1KVe3p6QW2fzr4kyz7e5k53aNOD/E2m9jkolyUw3LY6/cFIGtdOHZBSpQokeX3G5aBmDDDiXStJilZsqT06tUrVU9FrSh55plnMnqTAJDtJcfGSt4610vJ4cNTXXZ64btXTJIXvPsuyVOlqiQeOyan0+hH606Bli2lcOdOGh3L0XGpK/7UhbVrxRYfL4U6tJd89eqZn8emPicX1q2TPA8+KPH//itnFr4rRfv0kZA0pubnJrbYOAkIc13dCQD+wlOxufY4L1eunGzZskWqVKli9ulpTXY7VqNblerly5fPUJsDrZZ3VTFvJZKy2uW2ALRkAHK62KRYqVu0toyt83SqyxbsXSQxiReueBvn4s/LSzvmSN6gvHIpybk3d3r7sU/9daokJCdI3uC89v7eel3dDpw/IPuj90uzyGbSuGxjKZq3qElMrzq4Su6pco+53rTfp0nDkg2lVcVW6X7su8/stp/+7r7vpHR49ovv4xLjJDQ41GUy3abfZgDIUWw2m0/iwozcZ4YT6W+88Ya8//77qfZfd9118uCDD5JIB4BMKP6//5mf5778MnPX7/+kvY1KWmwJCeZngDWVPSTk8v74y/uPPfe8BBUtKkV7p39Kpi0pSU6+8Yac++JLSThyxHwYCC5ZUsJq15ISQ4dJSMm0v01Ojo+XU/Pmybkvv5KEqCgJCAmRsJo1pUivnlKgWTNzTPQXX8rhYcPM6QrvvSv5GjaU2N17ZP+995p9hV6eKaFNGkniv1Fy6t4Hzb7wYQMlf9fO5nTCjl1yYd47Er/5D7Gdj5HAEsUlrGVTyd+3lwTmy2eOid+wSc48NsCcLjB8qCTu2y+x3/0okpgkJdZ8k+7nAgB8wZOxec+ePWXy5MnSuHFjc37KlCnSu3dvl8f26dPHtJRp3bq1FClSRCZMmCAtWrSwV6sDQE7x6q65cib+rAys8bi8svONDF//m3++kV+O/GKS5JoY/v3Y706Xa6JcWe1aQgJDnPa/v/N9k2yf1mRauu+z9ju1nc63Wno5Ab/gzgWm0n3mxpmy6/QuOR17WuKS4qRoWFG5sfSNMqDeAHO549gXbl8o/577V2ITY6VI3iJSvUh16XFdD2lQskGq+2ny4eXZT3WL15WXm79sTmvbl3d3vCt/nPxDLiRckGJ5i0mTck3MbeQLuRyPbz6+WQatHGROD6o/yNyftozRav0vO2bu8xEAeEOG0/xHjx51uYiQTss8cuSIp8YFAPAwTUJrxfr5H1aYXugxK3405/M1bCAxa9dJzMqVpqd6YN68GertfvKVWRK/b5/YLl2S5IsXJX7/fjm3bLkkuultZktMlKjH+ly+7t9/m0r55AsX5OKGDXLwiX5yetHlaaj5brjBfp2Lmzebn5c2b7LvS9h2eYptwpY/7PvyNKxnfsb98puc7t5X4n5cLbYzZ0USEyX58BG5uPADOdO7v9gcFr2zxLw2Vy59+Mnl46lUBJANeDI2Hz16tNx8881So0YNs2lCfcSIEeYy7YGum+XZZ581ifM6deqYqvWLFy/Ku+++64FHBAD+Y8fZ3bL03+VSKbyCdK3cKcPXv5hwUaZtmCZ5AvPIiEaX309TqhhR0SSyfz/6u+ll/t2/35n9WoGuie43tr4hHap2MAlsTzh64ah8/+/3EnU+yiS2tZf6sYvHTBV8z296SnxSvDluy/Et8vSqp2XbyW1yLv6cxCfHm+v+FPWTbDvxX5sbdzYc3SBPrnhS1hxaI9Fx0ea+9DY+3P2hSZxrEj+lBX8ukE//+tQcDwD+JsMV6Roor1u3TipVquS0X/eVKcNiDwDgr7Tau+Szz5jFRvc0bCgBoaHmfOg118ih9u0lb926EtH2Hnv1ulaIX8mljZeT2nnr1ZPIN+ZIQFCQxB88KDGrV0tQIedWAI6iv/hCLv76qzld4I6WUmrCBEk4fFiiHu8rSSdPyvHpMySibVtT0R5Sobwk/HtALm3ecvk+/z+hrl8CxP9hJdIv/wwID5fga6qa0+enTBdJSJDgGtdKxPMTJKhUCYn9fqWcGzlBEnfslkuffiH5HrzPaVyaXI94cZKENr5Jkg7x5TAA/+fJ2DwkJERmz55ttpTmzJnjdD4oKEimT59uNgDIiZJtyTJ120xJlmR5tvYgCQnMcPpEXt/6uhy/eFz61ukr5Qu6bomlC44+f/vzMmLNCGn20eVZmfdUvkc6V+8sk9dPNm1MtFLcGpO2PggKdL+g6rbu22Tk2pH23uV63qKJ7NktZkvNojUlIk+EaX+jFeM61oMxB03Su0X5FrL1xFZz3/lD8svHbT+WkvlKmj7uG45tMKdd3c+qzqucxqGV71pZf03ha2TszWOlRL4SJhE/+dfJpvXMV/u+kg7VOjhdR5PrE26ZII1KN5IjMcTjAPxLhv8TPPbYYzJo0CBJSEiQ5s2b2xc5evrpp2XoUBanAwB/VqR7dyn80EOScPy4hJQoYdq8aA/3+L/3ScUPF5s+6YdHjpRLW7ZKYFiYFLrvPinx9FMmQe5KyP8naeL+/ltOzn7NJOXDalSXor17u+0ZeWH1Gvvp4gMHSnDhwmYrdP99cmrOG2K7eFEu/r5RCjRvZirpozWRvuVyIv3i5i0SXKqUBBctKnHbd4gtOVnit17+cBBS73oJCAyUxH8PSFLUQbMvceduOdXucqsXR/EbNqZKpOe9p7WEtbz8ASa4auVMPMMAkLWIzQHAOz498IX8eXantC7bQhoVq5/h6/915i95b8d7Ui68nPSu7bpNlkXbqvzwwA9y5MIRiQiNMMlrbb2ildmD6w82vdU1Ya2V5FrVfUuZW2T8LeNNP/WM0up3TZJP/326HIo5lKoq/J/of8zP0vlL26vq52ydI9cVvU6qFa5mFkDV5P+VaMW73r7ac2aPPPzVw6mO2XR8U6pE+p0V75QmkZdbxFQuRDwOIJsn0p966ik5deqU9OvXT+Lj4+2rm2r/xeEuFtkDAPgXTZ7nKVfOnE48c0ZOzJ4tEe3aSd7rr5f9nTpL7LZtUmr8OLm4fr2cfucdyVOpohR+8HL/8ZSK9XtCYnfvlksbN5pjLXkqVpTIefMkT7myLq+XdPaM/XSwQ0uCkFL/nU46c9re3iV66SeSdPq0XNy0SRIOHJACbVpLcNFiErt9uyRs3CxJ+/+9fL8N6pqfyafPXvF5SI4+l2pf8DXVrng9APAnxOYA4B1v7lkowQFBcnfZVrI7eq/TZVrFvTd6r1QvkEcK5Cng8vrz/pwnibZEue+a+8xioupi4kX75XtO7zEV2iXzX67u1iKUMuH/zSR67rfnTBL+4RoPy6wts0zVd5fqXaR4vuLy8qaXzeUZ6Ztumb5xuizaebmNoitWYr1lhZampczyv5eb+7aqzvXxPnfbc3J7udvd3s/Z2CvH49oyJqUqhS4veA0AOSKRrm/uzz//vOmhuHPnTsmbN69Uq1ZNQkOv/I0kAMC/nHjlFdPGpfiQIZIUEyOxf/whoTVqSOFOnSRv7dpy7quv5cK6n9NMpAcXKyYVF70nCceOSdyePRK3Z6+cfO01if/nHzn1xhwpPXGiy+sFFSpsP5149KgEVb3cjiXh6H/TN4MKXz4mv0OfdO3JrvLVq2cWRj3z3nty4Z0P7P3MrUR6YJFC9uvkvf9eKTjq6VRj0GmxKQWE/v9CrACQTRCbA4B3XEy8JIm2JBnwW+pFm/ee3ye91jwmLzV7ybRBcXn9hMtJc01665ZSl6+6SNcaXeWZRqlvXxf53Hhso7zS7BUJCQqR9YfXm/396/WX8JBw00dcFzDNjO/+udyDXRf9nH/nfKlUsJKsPrha+v/Y3+m4wIBAmdB4gjx1w1OmOl4XANUWMPui98nzvz1vT6QHiOtZqIXC/ovH21VpJ0MbDk1XPJ6eancAyDaLjVrCw8PlhhtukFq1ahGoA8BVSjp3TpLOnpXkS5fs+2yXYs0+vcxy+NnhsrN6DbM5Xf/8eVNdnhxz3r4vOTbO7NPNldjde+TsRx9LsT6PmV7kphVLQIC9jUtA8P9/15pGWxd15sOPJHrZMpOMz3fjjVKwTWsJjChoLks87fp+Vf7bbrOfPvHKLDPG2J075ezSpZfvO29eydeggTkdUrasBJe5XKke8+NKe092Taar+HWXP1gE5Mtr+qGr4ArlJSjycjX8peVfS+yPq8zzqVXocavXyZn/PS0JGy+3igGAnIDYHAB857Utr0ntd2qbzWpnklmxibEy8/eZclPpm6RZ+Wb2pLYKCggyMXtwQLA5nRm6aKh1m/mD85uFRjUxn9JvR36Td7a/Y3q8a1uXVhVb2fu8n4n9L84vGHo59ld/n/3bfjqyQKSUDS9r/2JAk/X62M7FnZOfD/8sw9cMNy1mACBHV6RfuHBBnnvuOdN78fjx45KcnOx0+b59+zw5PgDIFQ706Gkqsx2dfPVVs2k/8EqfXE4wp+Vgvyfl4oYNTvvOvPuu2VSNXTtTXefY1KkSUrKkFOnZ05wPzJ/ftFG5uHmzxKxaZTYV3vRyj0JXdOHP6M8+c3lZ+G23pnk9XdQ0eulSufj773L+u+/M5qjEkCESFPHfYqValR79+TKR5GQJCAuTsBo1TKI/sHhxST5xwhwTUqf2f8l/nXY6YpicHfCUSFy8RA8ZkWoM+bu6rrIHgOyE2BwAvGNNmy9T7au3/HJcXLtQTXnt1tekUNG0e5S/0vyVVPt6ftNTfj/2uzn9e9ffXVZfv7X9LZPcfrXFq/Z9Tco1ke2ntsv7u943Pc7PxJ2R9lXbZ+px6W1pmxZNkLdc0tKe9E7p8IXD8uLvL5otpcZlG9tP1ypay36617e9zM9Haj5i+sIPbjBYnl3zrMQnxcvodaNT3U6naztl6jEAQLZJpPfu3VtWrVoljzzyiJQuXdrtYnIAAP907rvvTA/0si/NlECHysUyzz8nRydMlENDh0lgeLgUG9BfIu69N83bKdCqlamY12py7WGu/ddDIsuZRUrTagejNOEdOX+enHpzrpz7+mtJiIqSgJAQCa1ZQ4r27CkFWjhPkc1nJdK1Gr1WLXvCPOT62hK34sfLp+tfbutiCb25kRRZ+IZcmL9Q4jdtFdv5GAksUliCK5aX0Ka32avXASA7IzYHgJzj6IWj8tafb8n919xvFva0PFr7UYmOjzaLl2rf9Xsq3yPDGg7L1H1YrWRWHVxlWqtopXmzyGby5IonnY7TBLm2ZNGq8RMXT0iyLdn0dG9evrn0rdPXaXHQbSe3ydf7vpZTcaecbuOGUjfIay1ek/d2vid/nPhDYhJipHBoYZO412T8NYWvydRjAABfCbC5akrlRqFCheTLL7+Uxo3/+wYyOzh37pxERERIdHS0FCz439SjrNLv7nvkqbCwLL9fAJ4XHxUlRbp1k8JdHrLv00Uwz3/7rZR0sbDb6YXvSnDRIlLw7ruv6n5jbDa3VS+50akzpyQgb9Yljc48M0Z+ObtZlj9webFWANnfhaUX5Iu3vsi2sSmxeeYQmwM5z+CWh6Vdw0ekW5XO9n2/n9wsXx76XsbWSb1ezoK9i6RYWBFpF9kmU/dHbO6buHzcz+Nk8/HNTguzAsgZLmSDuDzDPdILFy4sRYoUuZrxAQAAAPAAYnMAAADAT1u7TJw4UcaMGSPvvPOO5MuXzzujAgA/Z0tKcjofGBYml7b+If88+F+VuqNiT/w3/RHZly0xUWx0TQDgR4jNAeCyQJtIks05Rg8LCpMtp7ZJhx8fcXmdATUey6LRwVMSkxMlQAjIAWSTRPr06dPl77//lpIlS0rFihUlJCTE6fJNmzZ5cnwA4H8CAiTx6BGnXWE1a0rFxR/4bEjwPu2ElnDksFwok+F/nQDgNcTmAHBZwUsBcuTiUad9tQrXkE+bv+uzMcHz8fiRC0ckKDDI10MBkEtlOBvQvn3mVoYGgJxCq88v/LJeki9dksC8eX09HGSRxN17JeHIEdnXpISvhwIAdsTmAHDZ9cdCZcWR9XKp5iXJG0yMnhPtPbvXLMhaNC+96QFkk0T62LFjvTMSAMgmAgsWlMTDh+XouHFSfOBACSlb1tdDghfZkpMlcfsuOT35OTleOFCiKub39ZAAwI7YHAAuu+VwAfm26lEZuXGiDKnVX8rlZzHKnCLZliy7T++WFza8YNq65AumlRkA38jU/PSzZ8/KkiVLzDTSp556yixwpNNGdUppWRJKAHK4wDx5JLhkSbm0cZMc6PWohJQpI0GFC4kEZHj95gxJEJHzVMA7uRR7SQKCvdsLP/H4cUk4eVKOFwmUDx4pK8lB9GQE4F+IzQFApOSlEBm6vrjMtG2WHif6SJn8paVQaCEJ9FKMHi8ioXnDvHLb2VFsbGwmM0zuJSUnyfFLx+XUpVMSEBAgpcNLm58A4AsZfpv7448/pGXLlhIRESH//POPPPbYYyZY/+STT+TAgQOycOFC74wUAPyItnQJqVBBki9ckMTTpyXxxAmv3+fGpCRpdm87r99PdvL1d8skpJpzP2BPsgWJXKwaLPtbl5RDkfnEFkjQDsC/EJsDwH+qRYfJSz+UlW3FLsquwqfkUvAJsXnpvjYmJUrze+/10q1nP5//8rmEVPVOXB4UECTF8hYzLXtIogPIVon0IUOGSI8ePeSFF16QAgUK2Pffdddd0qVLF0+PDwD8VkBgoAQVKGC2rLAmNlYeGjEiS+4ru1i292fJfy+tVgDkXsTmAOAsT3KgNDgebjZvOhsbK89Mfsar95GdrHl9jeSvQ1wOIGfL8BynDRs2yOOPP55qv04bPXrUeYVsAAAAAN5DbA4AAAD4aSI9NDRUzp07l2r/nj17pHjx4p4aFwAAAIArIDYHAAAA/DSR3q5dO5kwYYIkJOiyd2L6U2n/xWeeeUbuu+8+b4wRAAAAgAvE5gAAAICfJtKnT58uMTExUqJECbl06ZI0adJEqlatanoyTp482TujBAAAAJAKsTkAAADgp4uNRkREyPfffy/r1q2TrVu3msC9fv360rJlS++MEAAAAIBLxOYAAACAnybSFy5cKJ07d5bGjRubzRIfHy+LFy+Wbt26eXqMAAAAAFwgNgcAAAD8tLVLz549JTo6OtX+8+fPm8sAAAAAZA1icwAAAMBPE+k2m80sYpTSwYMHzdRSAAAAAFmD2BwAAADws9Yu9erVM0G6bi1atJDg4P+umpSUJPv375fWrVt7a5wAAAAA/h+xOQAAAOCnifT27dubn1u2bJE777xTwsPD7ZflyZNHKlasKPfdd593RgkAAADAjtgcAAAA8NNE+tixY81PDcp1QaOwsDBvjgsAAABAGojNAQAAAD9NpFu6d+/unZEAAAAAyBBicwAAAMBPE+nac3HmzJny0UcfyYEDByQ+Pt7p8tOnT3tyfAAAAADSQGwOAAAAZI3AjF5h/PjxMmPGDDOFNDo6WoYMGSIdO3aUwMBAGTdunHdGCQAAACAVYnMAAADATxPpixYtkrlz58rQoUMlODhYHnroIZk3b56MGTNG1q9f751RAgAAAEiF2BwAAADw00T60aNHpXbt2uZ0eHi4qXxR99xzj3z55ZeeHyEAAAAAl4jNAQAAAD9NpJcrV06OHDliTlepUkW+++47c3rDhg0SGhrq+RECAAAAcInYHAAAAPDTRHqHDh1kxYoV5vSAAQNk9OjRUq1aNenWrZv06tXLG2MEAAAA4AKxOQAAAJA1gjN6heeee85+Whc1qlChgvz8888mYG/btq2nxwcAAAAgDcTmAAAAgJ9WpKd00003yZAhQ+TGG2+UKVOmeGZUAAAAADKM2BwAAADw00S6RXsz6lRSAAAAAL5FbA4AAAD4aSIdAAAAAAAAAICciEQ6AAAAAAAAAABukEgHAAAAAAAAAMCNYEknXbTInRMnTqT3pgAAAABcBWJzAAAAwE8T6Zs3b77iMbfffvvVjgcAAADAFRCbAwAAAH6aSF+5cqV3RwIAAAAgXYjNAQAAgKxFj3QAAAAAAAAAANwgkQ4AAAAAAAAAgBsk0gEAAAAAAAAAcINEOgAAAAAAAAAAnkqkJyYmyoQJE+TgwYMZuRoAAAAADyM2BwAAAPw0kR4cHCzTpk0zQTsAAAAA3yE2BwAAAPy4tUvz5s1l1apV3hkNAAAAgHQjNgcAAACyRnBGr9CmTRt59tlnZdu2bdKgQQPJnz+/0+Xt2rXz5PgAAAAApIHYHAAAAPDTRHq/fv3MzxkzZqS6LCAgQJKSkjwzMgAAAABuEZsDAAAAfppIT05O9s5IAAAAAGQIsTkAAADgpz3SHcXGxnpuJAAAAAAyjdgcAAAA8KNEuk4PnThxopQtW1bCw8Nl3759Zv/o0aNl/vz53hgjAAAAABeIzQEAAAA/TaRPnjxZ3n77bXnhhRckT5489v21atWSefPmeXp8AAAAANJAbA4AAAD4aSJ94cKF8uabb8rDDz8sQUFB9v116tSRXbt2eXp8AAAAANJAbA4AAAD4aSL90KFDUrVqVZcLHSUkJHhqXAAAAACugNgcAAAA8NNEes2aNWXNmjWp9i9ZskTq1avnqXEBAAAAuAJicwAAACBrBGf0CmPGjJHu3bub6hetdPnkk09k9+7dZlrpF1984Z1RAgAAAEiF2BwAAADw04r0e++9V5YvXy4//PCD5M+f3wTvO3fuNPvuuOMO74wSAAAAQCrE5gAAAICfVqSr2267Tb7//nvPjwYAAABAhhCbAwAAAH5YkR4VFSUHDx60n//tt99k0KBB8uabb3p6bAAAAADcIDYHAAAA/DSR3qVLF1m5cqU5ffToUWnZsqUJ2EeOHCkTJkzwxhgBAAAAuEBsDgAAAPhpIv3PP/+URo0amdMfffSR1K5dW37++WdZtGiRvP322xkeQEJCgvTv318KFy4sRYoUkQEDBkhiYqLb61y6dEmqVq0qhQoVyvD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",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
    "fig.suptitle('Small User Latency Under Load', fontsize=16, fontweight='bold')\n",
    "\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, pattern in enumerate(patterns):\n",
    "    ax = axes[i]\n",
    "    \n",
    "    if pattern in data and 'fairness' in data[pattern]:\n",
    "        small_data = data[pattern]['fairness']['fairness_comparison']['small']\n",
    "        \n",
    "        vtc_latency = small_data['vtc_avg_latency']\n",
    "        random_latency = small_data['random_avg_latency']\n",
    "        improvement_pct = small_data['latency_improvement_pct']\n",
    "        \n",
    "        methods = ['Random', 'VTC-Basic']\n",
    "        latencies = [random_latency, vtc_latency]\n",
    "        colors = ['#d62728', '#2ca02c']\n",
    "        \n",
    "        bars = ax.bar(methods, latencies, color=colors, alpha=0.8, \n",
    "                     edgecolor='black', linewidth=0.5)\n",
    "        \n",
    "        # Add value labels on bars\n",
    "        for bar, lat in zip(bars, latencies):\n",
    "            height = bar.get_height()\n",
    "            ax.text(bar.get_x() + bar.get_width()/2., height + height*0.01,\n",
    "                   f'{lat:.2f}s', ha='center', va='bottom', fontweight='bold', fontsize=10)\n",
    "        \n",
    "        # Add improvement annotation with color coding\n",
    "        if improvement_pct > 0:\n",
    "            improvement_text = f'🔵 {improvement_pct:.1f}% faster'\n",
    "            text_color = '#2ca02c'\n",
    "        elif improvement_pct < 0:\n",
    "            improvement_text = f'🔴 {abs(improvement_pct):.1f}% slower'\n",
    "            text_color = '#d62728'\n",
    "        else:\n",
    "            improvement_text = f'🟡 No change'\n",
    "            text_color = '#ff7f0e'\n",
    "            \n",
    "        ax.text(0.5, max(latencies) * 0.7, improvement_text, \n",
    "               ha='center', va='center', fontweight='bold', \n",
    "               fontsize=11, color=text_color, transform=ax.transData,\n",
    "               bbox=dict(boxstyle=\"round,pad=0.3\", facecolor='white', alpha=0.9, edgecolor=text_color))\n",
    "        \n",
    "        ax.set_title(f'{pattern_labels[i]}', fontsize=12, fontweight='bold')\n",
    "        ax.set_ylabel('Small User Latency (seconds)', fontsize=10)\n",
    "        ax.grid(True, alpha=0.3, axis='y')\n",
    "        ax.set_ylim(0, max(latencies) * 1.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chart 3a: Overall Performance - Ensuring No Degradation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = ['small', 'medium', 'high']\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(12, 8))\n",
    "\n",
    "vtc_overall = []\n",
    "random_overall = []\n",
    "\n",
    "for pattern in patterns:\n",
    "    if pattern in data and 'fairness' in data[pattern]:\n",
    "        fairness_data = data[pattern]['fairness']\n",
    "        vtc_avg = sum(fairness_data['algorithms']['vtc-basic'][cat]['avg_latency'] for cat in categories) / 3\n",
    "        random_avg = sum(fairness_data['algorithms']['random'][cat]['avg_latency'] for cat in categories) / 3\n",
    "        vtc_overall.append(vtc_avg)\n",
    "        random_overall.append(random_avg)\n",
    "\n",
    "x = np.arange(len(pattern_labels))\n",
    "width = 0.35\n",
    "\n",
    "bars1 = ax.bar(x - width/2, vtc_overall, width, label='VTC-Basic', \n",
    "               color='#1f77b4', alpha=0.8, edgecolor='black', linewidth=0.5)\n",
    "bars2 = ax.bar(x + width/2, random_overall, width, label='Random', \n",
    "               color='#ff7f0e', alpha=0.8, edgecolor='black', linewidth=0.5)\n",
    "\n",
    "ax.set_title('Overall Performance - Ensuring No Degradation', fontweight='bold', fontsize=14, pad=20)\n",
    "ax.set_ylabel('Average Latency (seconds)')\n",
    "ax.set_xlabel('Traffic Pattern')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(pattern_labels, rotation=0, ha='center')\n",
    "ax.legend(loc='upper left', fontsize=10)\n",
    "ax.grid(True, alpha=0.3, axis='y')\n",
    "\n",
    "# Calculate max height for proper y-axis scaling\n",
    "max_height = max(max(vtc_overall), max(random_overall))\n",
    "\n",
    "# Add performance indicators with proper alignment\n",
    "for i, (vtc_lat, random_lat) in enumerate(zip(vtc_overall, random_overall)):\n",
    "    improvement = ((random_lat - vtc_lat) / random_lat) * 100\n",
    "    \n",
    "    if improvement >= 0:\n",
    "        color, symbol = '#2ca02c', '✓'\n",
    "    elif improvement >= -5:\n",
    "        color, symbol = '#ff7f0e', '≈'\n",
    "    else:\n",
    "        color, symbol = '#d62728', '✗'\n",
    "    \n",
    "    y_pos = max(vtc_lat, random_lat) + max_height * 0.03\n",
    "    ax.text(x[i], y_pos, f'{symbol} {improvement:+.1f}%', ha='center', va='bottom',\n",
    "            fontsize=10, fontweight='bold', color=color)\n",
    "\n",
    "# Adjust y-axis limits to prevent text overlap with border\n",
    "ax.set_ylim(0, max_height * 1.15)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chart 3b: Improvement by User Category (Detailed Breakdown)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 8))\n",
    "\n",
    "improvement_matrix = []\n",
    "for pattern in patterns:\n",
    "    if pattern in data and 'fairness' in data[pattern]:\n",
    "        fairness_data = data[pattern]['fairness']\n",
    "        row = [fairness_data['fairness_comparison'][cat]['latency_improvement_pct'] for cat in categories]\n",
    "        improvement_matrix.append(row)\n",
    "\n",
    "improvement_matrix = np.array(improvement_matrix)\n",
    "im = ax.imshow(improvement_matrix, cmap='RdYlGn', aspect='auto', vmin=-5, vmax=5)\n",
    "\n",
    "for i in range(len(patterns)):\n",
    "    for j in range(len(categories)):\n",
    "        value = improvement_matrix[i, j]\n",
    "        text_color = 'white' if abs(value) > 2.5 else 'black'\n",
    "        ax.text(j, i, f'{value:.1f}%', ha=\"center\", va=\"center\", \n",
    "                color=text_color, fontweight='bold', fontsize=11)\n",
    "\n",
    "ax.set_xticks(range(len(categories)))\n",
    "ax.set_xticklabels([cat.title() for cat in categories])\n",
    "ax.set_yticks(range(len(patterns)))\n",
    "ax.set_yticklabels(pattern_labels)\n",
    "ax.set_title('Latency Improvement by User Category', fontweight='bold', fontsize=14, pad=20)\n",
    "ax.set_xlabel('User Categories', fontweight='bold')\n",
    "ax.set_ylabel('Traffic Patterns', fontweight='bold')\n",
    "\n",
    "cbar = plt.colorbar(im, ax=ax, shrink=0.8, aspect=20)\n",
    "cbar.set_label('Latency Improvement (%)', rotation=270, labelpad=20, fontsize=10)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COMPREHENSIVE FAIRNESS ANALYSIS\n",
      "============================================================\n",
      "Focus: VTC fairness impact across all user categories\n",
      "------------------------------------------------------------\n",
      "DETAILED BREAKDOWN BY PATTERN:\n",
      "\n",
      "📊 BALANCED:\n",
      "  🟠 Small : VTC=0.81s  Random=0.80s  →  -1.1%\n",
      "  🟠 Medium: VTC=0.87s  Random=0.87s  →  -0.4%\n",
      "  🔴 High  : VTC=0.91s  Random=0.89s  →  -2.5%\n",
      "\n",
      "📊 HIGH USAGE:\n",
      "  🟢 Small : VTC=0.79s  Random=0.83s  →  +4.4%\n",
      "  🟢 Medium: VTC=0.85s  Random=0.90s  →  +4.9%\n",
      "  🟠 High  : VTC=0.89s  Random=0.89s  →  -0.1%\n",
      "\n",
      "📊 BURSTY:\n",
      "  🟡 Small : VTC=0.84s  Random=0.85s  →  +0.5%\n",
      "  🟡 Medium: VTC=0.86s  Random=0.86s  →  +0.4%\n",
      "  🟠 High  : VTC=0.90s  Random=0.89s  →  -1.4%\n",
      "\n",
      "📊 HIGH MED PRESSURE:\n",
      "  🟢 Small : VTC=0.84s  Random=0.86s  →  +2.9%\n",
      "  🔴 Medium: VTC=0.89s  Random=0.86s  →  -2.9%\n",
      "  🟠 High  : VTC=0.92s  Random=0.92s  →  -0.8%\n",
      "\n",
      "============================================================\n",
      "FAIRNESS SUMMARY BY USER CATEGORY:\n",
      "============================================================\n",
      "\n",
      "🎯 Small Users (Primary Fairness Target):\n",
      "  • Patterns with improvement: 3/4 (75%)\n",
      "  • Average latency change: +1.7%\n",
      "  ≈ Modest protection achieved\n",
      "\n",
      "⚖️ Medium Users (Balance Check):\n",
      "  • Patterns with improvement: 2/4 (50%)\n",
      "  • Average latency change: +0.5%\n",
      "  ✓ Minimal impact - fairness maintained\n",
      "\n",
      "📊 Large Users (Impact Minimization):\n",
      "  • Patterns with improvement: 0/4 (0%)\n",
      "  • Average latency change: -1.2%\n",
      "  ≈ Low impact - acceptable tradeoff\n",
      "\n",
      "============================================================\n",
      "OVERALL FAIRNESS VERDICT:\n",
      "============================================================\n",
      "📈 Small user improvement: +1.7% (fairness gain)\n",
      "📊 Medium user impact: +0.5% (minimal degradation)\n",
      "📉 Large user impact: -1.2% (acceptable tradeoff)\n",
      "\n",
      "🎯 Fairness Score: +0.8%\n",
      "   (Small user gains vs. average impact on others)\n",
      "🟡 VTC PROVIDES MODEST FAIRNESS\n",
      "   → Some small user protection with acceptable tradeoffs\n"
     ]
    }
   ],
   "source": [
    "print(\"COMPREHENSIVE FAIRNESS ANALYSIS\")\n",
    "print(\"=\" * 60)\n",
    "print(\"Focus: VTC fairness impact across all user categories\")\n",
    "print(\"-\" * 60)\n",
    "\n",
    "# Initialize tracking variables for all categories\n",
    "categories = ['small', 'medium', 'high']\n",
    "category_stats = {cat: {'protected': 0, 'total_improvement': 0} for cat in categories}\n",
    "\n",
    "print(\"DETAILED BREAKDOWN BY PATTERN:\")\n",
    "for pattern in patterns:\n",
    "    if pattern in data and 'fairness' in data[pattern]:\n",
    "        print(f\"\\n📊 {pattern.upper().replace('_', ' ')}:\")\n",
    "        \n",
    "        for category in categories:\n",
    "            cat_data = data[pattern]['fairness']['fairness_comparison'][category]\n",
    "            improvement = cat_data['latency_improvement_pct']\n",
    "            vtc_latency = cat_data['vtc_avg_latency']\n",
    "            random_latency = cat_data['random_avg_latency']\n",
    "            \n",
    "            category_stats[category]['total_improvement'] += improvement\n",
    "            \n",
    "            if improvement > 0:\n",
    "                status = \"🟢\" if improvement > 2 else \"🟡\"\n",
    "                category_stats[category]['protected'] += 1\n",
    "            else:\n",
    "                status = \"🔴\" if improvement < -2 else \"🟠\"\n",
    "            \n",
    "            print(f\"  {status} {category.capitalize():6s}: VTC={vtc_latency:.2f}s  Random={random_latency:.2f}s  →  {improvement:+.1f}%\")\n",
    "\n",
    "print(f\"\\n\" + \"=\" * 60)\n",
    "print(\"FAIRNESS SUMMARY BY USER CATEGORY:\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "for category in categories:\n",
    "    avg_improvement = category_stats[category]['total_improvement'] / len(patterns)\n",
    "    protection_rate = (category_stats[category]['protected'] / len(patterns)) * 100\n",
    "    \n",
    "    if category == 'small':\n",
    "        user_type = \"Small Users (Primary Fairness Target)\"\n",
    "        emoji = \"🎯\"\n",
    "    elif category == 'medium':\n",
    "        user_type = \"Medium Users (Balance Check)\"\n",
    "        emoji = \"⚖️\"\n",
    "    else:\n",
    "        user_type = \"Large Users (Impact Minimization)\"\n",
    "        emoji = \"📊\"\n",
    "    \n",
    "    print(f\"\\n{emoji} {user_type}:\")\n",
    "    print(f\"  • Patterns with improvement: {category_stats[category]['protected']}/{len(patterns)} ({protection_rate:.0f}%)\")\n",
    "    print(f\"  • Average latency change: {avg_improvement:+.1f}%\")\n",
    "    \n",
    "    if category == 'small':\n",
    "        if avg_improvement > 2:\n",
    "            print(f\"  ✓ Strong protection achieved\")\n",
    "        elif avg_improvement > 0:\n",
    "            print(f\"  ≈ Modest protection achieved\")\n",
    "        else:\n",
    "            print(f\"  ✗ Protection insufficient\")\n",
    "    else:\n",
    "        if abs(avg_improvement) < 1:\n",
    "            print(f\"  ✓ Minimal impact - fairness maintained\")\n",
    "        elif abs(avg_improvement) < 3:\n",
    "            print(f\"  ≈ Low impact - acceptable tradeoff\")\n",
    "        else:\n",
    "            print(f\"  ⚠️  Significant impact - review needed\")\n",
    "\n",
    "print(f\"\\n\" + \"=\" * 60)\n",
    "print(\"OVERALL FAIRNESS VERDICT:\")\n",
    "print(\"=\" * 60)\n",
    "\n",
    "small_avg = category_stats['small']['total_improvement'] / len(patterns)\n",
    "medium_avg = category_stats['medium']['total_improvement'] / len(patterns)\n",
    "large_avg = category_stats['high']['total_improvement'] / len(patterns)\n",
    "\n",
    "print(f\"📈 Small user improvement: {small_avg:+.1f}% (fairness gain)\")\n",
    "print(f\"📊 Medium user impact: {medium_avg:+.1f}% (minimal degradation)\")\n",
    "print(f\"📉 Large user impact: {large_avg:+.1f}% (acceptable tradeoff)\")\n",
    "\n",
    "# Calculate fairness score\n",
    "fairness_score = small_avg - (abs(medium_avg) + abs(large_avg))/2\n",
    "print(f\"\\n🎯 Fairness Score: {fairness_score:+.1f}%\")\n",
    "print(f\"   (Small user gains vs. average impact on others)\")\n",
    "\n",
    "if fairness_score > 1:\n",
    "    print(\"✅ VTC ACHIEVES EFFECTIVE FAIRNESS\")\n",
    "    print(\"   → Small users protected with minimal cost to others\")\n",
    "elif fairness_score > 0:\n",
    "    print(\"🟡 VTC PROVIDES MODEST FAIRNESS\")\n",
    "    print(\"   → Some small user protection with acceptable tradeoffs\")\n",
    "else:\n",
    "    print(\"❌ VTC FAIRNESS INEFFECTIVE\") \n",
    "    print(\"   → Costs outweigh small user benefits\")\n"
   ]
  }
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