{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import json\n", "\n", "import matplotlib.pyplot as plt\n", "from dataclasses import dataclass\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "this_dir = os.path.dirname(os.path.abspath(''))\n", "# results is in \"../results\"\n", "results_dir = os.path.join(this_dir, \"results\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "experiment_folder = \"casestudy_example\"\n", "experiment_name = \"cs_example_edf\"\n", "\n", "experiment_file = os.path.join(results_dir, experiment_folder, experiment_name + \".json\")\n", "if not os.path.exists(experiment_file):\n", " print(\"Experiment file not found: \", experiment_file)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "with open(experiment_file) as f:\n", " experiment_data_raw = json.load(f)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of records: 12055\n", "First record: {'entry': {'operation': 'start_work', 'chain': 0, 'node': 'node_0', 'count': 500, 'next_release_us': 99890}, 'time': 0.0001}\n", "Operation types: ['get_next_executable', 'end_work', 'next_deadline', 'wait_for_work', 'start_work']\n" ] } ], "source": [ "def pre_process_data(data):\n", " for record in data:\n", " record[\"time\"] = int(record[\"time\"])\n", "\n", " min_time = min([record[\"time\"] for record in data])\n", " for record in data:\n", " record[\"time\"] -= min_time\n", " record[\"time\"] /= (1000 * 1000)\n", "\n", " if record[\"entry\"][\"operation\"] == \"next_deadline\":\n", " #print(\"Record: \", record)\n", " record[\"entry\"][\"deadline\"] = int(record[\"entry\"][\"deadline\"])\n", " record[\"entry\"][\"deadline\"] -= min_time\n", " record[\"entry\"][\"deadline\"] /= (1000 * 1000)\n", "\n", " # data = sorted(data, key=lambda x: x[\"time\"])\n", " return data\n", "\n", "experiment_data = pre_process_data(experiment_data_raw)\n", "\n", "print(\"Number of records: \", len(experiment_data))\n", "print(\"First record: \", experiment_data[0])\n", "operation_types = list(set([record[\"entry\"][\"operation\"] for record in experiment_data]))\n", "print(\"Operation types: \", operation_types)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "@dataclass\n", "class Record:\n", " start_time: float\n", " end_time: float\n", " node_name: str\n", "\n", "@dataclass\n", "class RecordLine:\n", " node_name: str\n", " count: int\n", "\n", " def __eq__(self, other):\n", " return self.node_name == other.node_name and self.count == other.count\n", "\n", " def __hash__(self):\n", " return hash((self.node_name, self.count))\n", "\n", "def get_records(data) -> list[Record]:\n", " # used to match start_work and end_work records\n", " current_records: dict[RecordLine, Record] = {}\n", " records = []\n", " for record in data:\n", " if record[\"entry\"][\"operation\"] == \"start_work\":\n", " current_record = Record(start_time=record[\"time\"], node_name=record[\"entry\"][\"node\"], end_time=None)\n", " current_record_line = RecordLine(node_name=record[\"entry\"][\"node\"], count=record[\"entry\"][\"count\"])\n", " if current_record_line in current_records:\n", " raise Exception(\"Overlapping records\")\n", " current_records[current_record_line] = current_record\n", " elif record[\"entry\"][\"operation\"] == \"end_work\":\n", " current_record_line = RecordLine(node_name=record[\"entry\"][\"node\"], count=record[\"entry\"][\"count\"])\n", " if current_record_line not in current_records:\n", " raise Exception(\"No start record\")\n", " current_record = current_records[current_record_line]\n", " current_record.end_time = record[\"time\"]\n", " records.append(current_record)\n", " del current_records[current_record_line]\n", " return records\n", "\n", "records = get_records(experiment_data)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of nodes: 4\n" ] } ], "source": [ "num_nodes = len(set([record.node_name for record in records]))\n", "print(\"Number of nodes: \", num_nodes)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "98aed7649afd4e958ddfe602039f901b", "version_major": 2, "version_minor": 0 }, "image/png": 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", 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\n", " " ], "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib widget\n", "\n", "# swimlane plot\n", "name_to_id = {name: i for i, name in enumerate(set([record.node_name for record in records]))}\n", "fig, ax = plt.subplots()\n", "for i, record in enumerate(records):\n", " # ax.plot([record.start_time, record.end_time], [name_to_id[record.node_name], name_to_id[record.node_name]], label=record.node_name)\n", " ax.broken_barh([(record.start_time, record.end_time - record.start_time)], (name_to_id[record.node_name] - 0.4, 0.8), facecolors='blue')\n", "ax.set_yticks(range(num_nodes))\n", "ax.set_yticklabels(name_to_id.keys())\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "@dataclass\n", "class Deadline:\n", " chain_id: int\n", " deadline: float\n", " on_time: bool\n", "\n", "def get_deadlines(data) -> list[Deadline]:\n", " deadlines = []\n", " for record in data:\n", " if record[\"entry\"][\"operation\"] == \"next_deadline\" and \"on_time\" in record[\"entry\"]:\n", " deadlines.append(Deadline(chain_id=record[\"entry\"][\"chain_id\"], deadline=record[\"entry\"][\"deadline\"], on_time=record[\"entry\"][\"on_time\"]))\n", " return deadlines\n", "\n", "deadlines = get_deadlines(experiment_data)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9f92a0a66e734ecd994bf447537c04c6", "version_major": 2, "version_minor": 0 }, "image/png": 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5so9nAwAwsJyWAVhZWRmVlZX9PQ0AgAHptHwLGACA4xOAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkRgAAAmRGAAACZEYAAAJkZNAHY3NwcF154YX9PAwBg0Bs0AdjbVq9eHZdddlmceeaZUVFR0d/TAQDoM9kG4DvvvBNf+cpXYtGiRf09FQCAPlWUAJw9e3YsWbIkli5dGpWVlTFu3Lhobm4u7N+zZ0/MnTs3Ro8eHWVlZTFv3rzYv39/jzHuuuuuqKqqijFjxkRTU1McPnz4qOdZt25dTJkyJUaMGBGTJ0+O+++//6TnuGrVqvjmN78ZtbW1n/o8AQAGo6JdAdywYUOMGjUqtm3bFnfffXfcfvvt0draGt3d3TF37tw4ePBgbN68OVpbW6OjoyPmz59feOymTZuiubk5WlpaYseOHVFdXX1U3G3cuDFWrFgRq1evjra2tmhpaYnly5fHhg0binVKceTIkejq6uqxAQAMOqkIZs2alWbOnNnjvosvvjgtW7YsPfHEE2no0KFpz549hX27du1KEZG2b9+eUkqpvr4+LV68uMfj6+rq0vTp0wu3J02alB566KEex9xxxx2pvr7+E811/fr1qby8/KSOXblyZYqIo7bOzs5P9JwnZf/+lCJsNpvNZst76+jo9ZfYzs7OVLTX70GiaFcAp02b1uN2dXV1HDhwINra2qKmpiZqamoK+6ZOnRoVFRXR1tYWERFtbW1RV1fX4/H19fWFj996661ob2+PpqamGD16dGG78847o729vVinFLfddlt0dnYWtr179xbtuQAAimVYsQYePnx4j9slJSXR3d3dK2O/+eabERGxdu3ao0Jx6NChvfIcx1JaWhqlpaVFGx8AoC/0+U8BT5kyJfbu3dvj6tnu3bvj0KFDMXXq1MIx27Zt6/G4rVu3Fj6uqqqK8ePHR0dHR5x33nk9tgkTJvTNiQAADFJFuwJ4PA0NDVFbWxsLFiyINWvWxHvvvReLFy+OWbNmxYwZMyIi4uabb44bbrghZsyYEZdffnls3Lgxdu3aFRMnTiyMs2rVqliyZEmUl5dHY2NjHDlyJHbs2BGvv/563HLLLSecx549e+LgwYOxZ8+eeP/992Pnzp0REXHeeefF6NGji3LuAAADQZ8HYElJSTz22GPx9a9/Pa644ooYMmRINDY2xn333Vc4Zv78+dHe3h5Lly6Nw4cPx5e//OVYtGhRPP7444VjbrzxxjjzzDPjnnvuiVtvvTVGjRoVtbW18Y1vfOOk5rFixYoePzH8pS99KSIinn766Zg9e3avnCsAwEBUklJK/T2JwaqrqyvKy8ujs7MzysrKenfwAwciqqp6d0wAGGw6OiJ6+du7ivr6PUhk+5dAAABydVoGYEtLS49fD/PRbc6cOf09PQCAftXn3wPYFxYuXBjz5s075r6RI0f28WwAAAaW0zIAKysro7Kysr+nAQAwIJ2WbwEDAHB8AhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgM4MmAJubm+PCCy/s72kAAAx6gyYAe9s111wT55xzTowYMSKqq6vj2muvjX379vX3tAAAii7bALzyyitj06ZN8atf/Sr+5V/+Jdrb2+NP//RP+3taAABFV5QAnD17dixZsiSWLl0alZWVMW7cuGhubi7s37NnT8ydOzdGjx4dZWVlMW/evNi/f3+PMe66666oqqqKMWPGRFNTUxw+fPio51m3bl1MmTIlRowYEZMnT47777//pOf4zW9+My699NI499xz47LLLotvf/vbsXXr1nj33Xc/9XkDAAwGRbsCuGHDhhg1alRs27Yt7r777rj99tujtbU1uru7Y+7cuXHw4MHYvHlztLa2RkdHR8yfP7/w2E2bNkVzc3O0tLTEjh07orq6+qi427hxY6xYsSJWr14dbW1t0dLSEsuXL48NGzZ84rkePHgwNm7cGJdddlkMHz78lM8dAGBAS0Uwa9asNHPmzB73XXzxxWnZsmXpiSeeSEOHDk179uwp7Nu1a1eKiLR9+/aUUkr19fVp8eLFPR5fV1eXpk+fXrg9adKk9NBDD/U45o477kj19fUnPc+lS5emM888M0VEuvTSS9Orr776sccfPnw4dXZ2Fra9e/emiEidnZ0n/Zwnbf/+lCJsNpvNZst76+jo9ZfYzs7OVLTX70GiaFcAp02b1uN2dXV1HDhwINra2qKmpiZqamoK+6ZOnRoVFRXR1tYWERFtbW1RV1fX4/H19fWFj996661ob2+PpqamGD16dGG78847o729/aTneOutt8azzz4bTzzxRAwdOjSuu+66SCkd9/jvfOc7UV5eXtg+eg4AAIPFsGIN/H/fSi0pKYnu7u5eGfvNN9+MiIi1a9ceFYpDhw496XHGjh0bY8eOjfPPPz+mTJkSNTU1sXXr1h6x+VG33XZb3HLLLYXbXV1dxYvAsWMjfvvbiN/9LiZPS/HLn39kX3d3xOuvf/DxWWfF5C8N6bn/ZI4xxmkzRgwZ8rH7TzTG5P93Vvzy58cf45j7i3wuk6fHB8ceY4yI+NjnOdX1GCifl7763EaceIyjjumn9eiLz8tRX4PHWI+PW9O+/Nz2xuflVNe0t9bjqGNSijhy5IPXwnPOCXpfSfq4S16f0uzZs+PCCy+MNWvWFO774z/+46ioqIgFCxbEnDlz4sUXXyzE0+7du+OCCy6In/3sZzFjxoy47LLL4ktf+lJ8//vfLzy+vr4+3n777di5c2dERHz+85+PhQsXxvLly3tlznv27Ilzzz03nn766Zg9e/ZJPaarqyvKy8ujs7MzysrKemUex1JS8sH/C592vzFO7zFO5FSf42Tn0JvncrxjS0o++O+prMlg+bz0xhgnsz+id47pi/Xoi8/LicbqjXmeyvN/dH9E//8b1Fvr0Zufu5PRV6/fA1nRrgAeT0NDQ9TW1saCBQtizZo18d5778XixYtj1qxZMWPGjIiIuPnmm+OGG26IGTNmxOWXXx4bN26MXbt2xcSJEwvjrFq1KpYsWRLl5eXR2NgYR44ciR07dsTrr7/e4yrdsWzbti1+9rOfxcyZM+Oss86K9vb2WL58eUyaNOm4V/8AAE4Xff57AEtKSuKxxx6Ls846K6644opoaGiIiRMnxiOPPFI4Zv78+bF8+fJYunRpXHTRRfHSSy/FokWLeoxz4403xrp162L9+vVRW1sbs2bNigcffDAmTJhwwjmceeaZ8eijj8ZVV10VX/ziF6OpqSmmTZsWmzdvjtLS0l4/ZwCAgaQobwHnwlvAxhgIY5yIt4A/+TwGwuelN8bwFnDvz8dbwL3/HCd7TG/yFnDGfwkEACBXp2UAtrS09Pj1MB/d5syZ09/TAwDoV33+QyB9YeHChTFv3rxj7hs5cmQfzwYAYGA5LQOwsrIyKisr+3saAAAD0mn5FjAAAMcnAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMiMAAQAyIwABADIjAAEAMjOsvyfAiaV0avuNcXqPUeznONk59Oa5HO/YvliPkzmmr+ZxqmP0xdfoyR7TF8/TG/M40VgDZT366nM3UOZB73MFEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDMCEAAgMwIQACAzAhAAIDPD+nsCg1lKKSIiurq6+nkmAMDJ+vB1+8PX8RwJwFPwxhtvRERETU1NP88EAPik3njjjSgvL+/vafSLkpRz/p6i7u7u2LdvX4wZMyZKSkp6bdyurq6oqamJvXv3RllZWa+Ny//PGvcN61x81rhvWOfi68s1TinFG2+8EePHj48hQ/L8bjhXAE/BkCFD4uyzzy7a+GVlZf6hKTJr3Desc/FZ475hnYuvr9Y41yt/H8ozewEAMiYAAQAyIwAHoNLS0li5cmWUlpb291ROW9a4b1jn4rPGfcM6F5817lt+CAQAIDOuAAIAZEYAAgBkRgACAGRGAAIAZEYA9oHvf//78YUvfCFGjBgRdXV1sX379o89/p//+Z9j8uTJMWLEiKitrY1/+7d/67E/pRQrVqyI6urqGDlyZDQ0NMTzzz9fzFMYFHp7nR999NG4+uqr4zOf+UyUlJTEzp07izj7waE31/jdd9+NZcuWRW1tbYwaNSrGjx8f1113Xezbt6/YpzHg9fbXcnNzc0yePDlGjRoVZ511VjQ0NMS2bduKeQoDXm+v8UctXLgwSkpKYs2aNb0868Gnt9f5hhtuiJKSkh5bY2NjMU/h9JUoqocffjidccYZ6R//8R/Trl270k033ZQqKirS/v37j3n8T3/60zR06NB09913p927d6e/+Zu/ScOHD0//8z//UzjmrrvuSuXl5elf//Vf089//vN0zTXXpAkTJqS33367r05rwCnGOv/whz9Mq1atSmvXrk0RkZ599tk+OpuBqbfX+NChQ6mhoSE98sgj6Ze//GXasmVLuuSSS9JFF13Ul6c14BTja3njxo2ptbU1tbe3p+eeey41NTWlsrKydODAgb46rQGlGGv8oUcffTRNnz49jR8/Pn3ve98r8pkMbMVY5+uvvz41Njam3/72t4Xt4MGDfXVKpxUBWGSXXHJJ+su//MvC7ffffz+NHz8+fec73znm8fPmzUt/9Ed/1OO+urq69Bd/8RcppZS6u7vTuHHj0j333FPYf+jQoVRaWpr+6Z/+qQhnMDj09jp/1IsvvigAU3HX+EPbt29PEZFeeuml3pn0INQX69zZ2ZkiIj355JO9M+lBplhr/Jvf/CZ9/vOfT88991w699xzsw/AYqzz9ddfn+bOnVuU+ebGW8BF9M4778QzzzwTDQ0NhfuGDBkSDQ0NsWXLlmM+ZsuWLT2Oj4j4gz/4g8LxL774Yrzyyis9jikvL4+6urrjjnm6K8Y601NfrXFnZ2eUlJRERUVFr8x7sOmLdX7nnXfigQceiPLy8pg+fXrvTX6QKNYad3d3x7XXXhu33nprXHDBBcWZ/CBSzK/ln/zkJ/G5z30uvvjFL8aiRYvitdde6/0TyIAALKJXX3013n///aiqqupxf1VVVbzyyivHfMwrr7zyscd/+N9PMubprhjrTE99scaHDx+OZcuWxZ/92Z/1yR+CH4iKuc4/+tGPYvTo0TFixIj43ve+F62trTF27NjePYFBoFhr/N3vfjeGDRsWS5Ys6f1JD0LFWufGxsb44Q9/GE899VR897vfjc2bN8ecOXPi/fff7/2TOM0N6+8JALz77rsxb968SCnF3/3d3/X3dE5LV155ZezcuTNeffXVWLt2bcybNy+2bdsWn/vc5/p7aoPeM888E3/7t38b//3f/x0lJSX9PZ3T2le/+tXCx7W1tTFt2rSYNGlS/OQnP4mrrrqqH2c2+LgCWERjx46NoUOHxv79+3vcv3///hg3btwxHzNu3LiPPf7D/36SMU93xVhneirmGn8Yfy+99FK0trZme/UvorjrPGrUqDjvvPPi0ksvjR/84AcxbNiw+MEPftC7JzAIFGON/+u//isOHDgQ55xzTgwbNiyGDRsWL730Uvz1X/91fOELXyjKeQx0ffXv8sSJE2Ps2LHxwgsvnPqkMyMAi+iMM86Iiy66KJ566qnCfd3d3fHUU09FfX39MR9TX1/f4/iIiNbW1sLxEyZMiHHjxvU4pqurK7Zt23bcMU93xVhneirWGn8Yf88//3w8+eST8ZnPfKY4JzBI9OXXcnd3dxw5cuTUJz3IFGONr7322vjFL34RO3fuLGzjx4+PW2+9NR5//PHincwA1ldfy7/5zW/itddei+rq6t6ZeE76+6dQTncPP/xwKi0tTQ8++GDavXt3+trXvpYqKirSK6+8klJK6dprr03f/va3C8f/9Kc/TcOGDUv33ntvamtrSytXrjzmr4GpqKhIjz32WPrFL36R5s6d69fAFGGdX3vttfTss8+mH//4xyki0sMPP5yeffbZ9Nvf/rbPz28g6O01fuedd9I111yTzj777LRz584ev9bhyJEj/XKOA0Fvr/Obb76ZbrvttrRly5b061//Ou3YsSP9+Z//eSotLU3PPfdcv5xjfyvGvxf/l58C7v11fuONN9K3vvWttGXLlvTiiy+mJ598Mv3+7/9++r3f+710+PDhfjnHwUwA9oH77rsvnXPOOemMM85Il1xySdq6dWth36xZs9L111/f4/hNmzal888/P51xxhnpggsuSD/+8Y977O/u7k7Lly9PVVVVqbS0NF111VXpV7/6VV+cyoDW2+u8fv36FBFHbStXruyDsxmYenONP/z1Osfann766T46o4GpN9f57bffTn/yJ3+Sxo8fn84444xUXV2drrnmmrR9+/a+Op0Bqbf/vfi/BOAHenOd//d//zddffXV6bOf/WwaPnx4Ovfcc9NNN91UCEo+mZKUUuqfa48AAPQH3wMIAJAZAQgAkBkBCACQGQEIAJAZAQgAkBkBCACQGQEIAJAZAQgAkBkBCACQGQEIAJAZAQgAkBkBCACQGQEIAJAZAQgAkBkBCACQGQEIAJAZAQgAkBkBCACQGQEIAJAZAQgAkBkBCACQGQEIAJAZAQgAkBkBCACQGQEIAJAZAQgAkBkBCACQGQEIAJAZAQgAkBkBCACQGQEIAJCZ/w/Wdq4F5OFHHAAAAABJRU5ErkJggg==", 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