FPID_Contoller.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn as sl\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "csv_file = 'https://raw.githubusercontent.com/nimamahmoudi/faas-keep-alive-modeling/master/datasets/wc98/invocation_count.csv'\n",
    "df = pd.read_csv(csv_file, index_col=0, parse_dates=True)\n",
    "f = plt.figure(figsize=(10,12))\n",
    "df['1998-06-30 08:00:01':'1998-07-01 08:00:00'].plot()\n",
    "users = df['1998-06-30 08:00:01':'1998-07-01 08:00:00'].values\n",
    "plt.subplot(211)\n",
    "plt.plot(users)\n",
    "scaling = sl.preprocessing.MinMaxScaler()\n",
    "scaled_users = np.ceil(120*scaling.fit_transform(users)).astype(int)\n",
    "plt.subplot(212)\n",
    "plt.plot(scaled_users)\n",
    "plt.show(block = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import subprocess\n",
    "import time\n",
    "import sys\n",
    "import time\n",
    "import math\n",
    "from kubernetes import client, config\n",
    "import os\n",
    "\n",
    "output = subprocess.check_output('kubectl get pods', shell=True)\n",
    "os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"]=\"/home/ubuntu/jupyter/google.json\"\n",
    "dir_path = os.path.dirname(os.path.realpath('__file__'))\n",
    "config.load_kube_config()\n",
    "DEPLOYMENT_NAME = 'wordpress'\n",
    "DEPLOYMENT_NS = 'default'\n",
    "\n",
    "\n",
    "api_instance = client.ExtensionsV1beta1Api()\n",
    "\n",
    "def get_replica_and_ready():\n",
    "    api_response = api_instance.read_namespaced_deployment(DEPLOYMENT_NAME, DEPLOYMENT_NS)\n",
    "    return api_response.status.replicas, api_response.status.ready_replicas\n",
    "\n",
    "def set_replica_num(rnum):\n",
    "    rnum = int(rnum)\n",
    "    if rnum < 1:\n",
    "        rnum = 1\n",
    "    api_response = api_instance.read_namespaced_deployment(DEPLOYMENT_NAME, DEPLOYMENT_NS)\n",
    "    api_response.spec.replicas = rnum\n",
    "    api_instance.patch_namespaced_deployment_scale(DEPLOYMENT_NAME, DEPLOYMENT_NS, api_response)\n",
    "u_pre = 6\n",
    "\n",
    "print(get_replica_and_ready())\n",
    "set_replica_num(u_pre)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Control Initialization KP KI KD and Setpoint \n",
    "KP = 0.0004\n",
    "KI = 0.0004\n",
    "KD = 0.00005\n",
    "r_setpoint = 800\n",
    "e = np.zeros([3,1])\n",
    "e_pre = np.zeros([3,1])\n",
    "x_PID = np.zeros([3,1])\n",
    "r = r_setpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% Change working directory from the workspace root to the ipynb file location. Turn this addition off with the DataScience.changeDirOnImportExport setting\n",
    "import os\n",
    "from tqdm.auto import tqdm\n",
    "try:\n",
    "    os.chdir(os.path.join(os.getcwd(), 'examples'))\n",
    "    print(os.getcwd())\n",
    "except:\n",
    "    pass\n",
    "import pacs_load_tester as load_tester\n",
    "\n",
    "#%%\n",
    "def custom_sensing():\n",
    "    r1,r2 = get_replica_and_ready()\n",
    "    return {'r1':r1 , 'r2':r2}\n",
    "\n",
    "lt = load_tester.pacsLoadTester(hatch_rate=1, temp_stat_max_len=5, base='http://35.232.158.172:8089/')\n",
    "lt.custom_sensing = custom_sensing\n",
    "lt.custom_sensing()\n",
    "tqdm.pandas()\n",
    "\n",
    "loop_timer = load_tester.TimerClass()\n",
    "total_timer = load_tester.TimerClass()\n",
    "user_sequence = [30,60,30,60,30]\n",
    "lt.custom_sensing = custom_sensing\n",
    "lt.change_count(user_sequence[0])\n",
    "lt.start_capturing()\n",
    "loop_time_in_secs = load_tester.get_loop_time_in_secs('20s')\n",
    "loop_timer.tic()\n",
    "total_timer.tic()\n",
    "results = None\n",
    "\n",
    "for i in tqdm(range(len(user_sequence)*36)):\n",
    "    print(\"U(t)  : \", u_pre)\n",
    "    user_count = user_sequence[math.floor(i/36)]\n",
    "    lt.change_count(user_count)\n",
    "\n",
    "    sleep_time = loop_time_in_secs - loop_timer.toc()\n",
    "    if sleep_time > 0:\n",
    "        time.sleep(sleep_time)\n",
    "\n",
    "    loop_timer.tic()\n",
    "\n",
    "    result = lt.get_all_stats()\n",
    "    df_result = pd.DataFrame(data=result)\n",
    "\n",
    "    if results is None:\n",
    "        results = df_result\n",
    "    else:\n",
    "        results = results.append(df_result)\n",
    "\n",
    "    #####Identification\n",
    "    if (result['current_response_time_average'])!=[]:\n",
    "        if result['current_response_time_average'][-1]!=None :\n",
    "            e_pre[0] = e[0]\n",
    "            e_pre[1] = e[1]\n",
    "            e_pre[2] = e[2]\n",
    "            e[0]= -(r - np.nanmean(np.array(result['current_response_time_average'],dtype=float)))\n",
    "            current_response = np.nanmean(np.array(result['current_response_time_average'],dtype=float))\n",
    "            if e[0]>=-300 and e[0]<=0:\n",
    "                e[0] = 0\n",
    "            e[1]= e_pre[0]\n",
    "            e[2]= e_pre[1]\n",
    "            x_PID[0] = e[0] - e[1]\n",
    "            x_PID[1] = e[0]  \n",
    "            x_PID[2] = e[0] - 2*e[1] + e[2]\n",
    "            print(\"Error 0 : \",e[0], \"Error 1 : \", e[1],\"Error 2 : \", e[2])\n",
    "            if u_pre ==40 or u_pre <= 1:\n",
    "                x_PID[1] = 0    \n",
    "            u_pre =u_pre+ KP*x_PID[0] + KI*x_PID[1] + KD*x_PID[2]\n",
    "            try :\n",
    "                set_replica_num(np.ceil(u_pre))\n",
    "            except :\n",
    "                print(\"exception occured\")\n",
    "            \n",
    "            print(\"Number of Replica Request: \",result['custom_r1'][-1],\"Number of Replica Ready: \",result['custom_r2'][-1],\"control signal : \",u_pre,\"error :\", r_setpoint-current_response)\n",
    "            print(\"x1 : \",x_PID[0],\"x2 : \",x_PID[1], \"x3 : \",x_PID[2], \"Tracking error : \",e[0])\n",
    "            print(\"Current Response Time is : \" , current_response)\n",
    "lt.stop_test()\n",
    "\n",
    "results, filename = lt.prepare_results_from_df(results)\n",
    "\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "res = results\n",
    "res = res[res['total_rps'] > 0]\n",
    "responsetime_avg = np.nanmean(res['current_response_time_average'])\n",
    "responsetime_var = np.sqrt(np.var(res['current_response_time_average']))\n",
    "container_avg = np.average(res['custom_r2'])\n",
    "container_var = np.sqrt(np.var(res['custom_r2']))\n",
    "print(\"average res : \", responsetime_avg , \"Var Res : \",responsetime_var, \"average Container : \",container_avg, \"Var Container : \", container_var )\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "get_ipython().run_line_magic('matplotlib', 'inline')\n",
    "\n",
    "plt.figure(figsize=(12,18),dpi=200)\n",
    "plt.subplot(511)\n",
    "#plt.plot(res['elapsed_min'], res['min_response_time'], label='min_response_time')\n",
    "#plt.plot(res['elapsed_min'], res['current_response_time_percentile_50'], label='median_response_time')\n",
    "plt.plot(res['elapsed_min'], res['current_response_time_average'], label='avg_response_time')\n",
    "plt.plot(res['elapsed_min'],r_setpoint*np.ones(len(res['current_response_time_average'])), label='setpoint')\n",
    "#plt.plot(res['elapsed_min'], res['current_response_time_percentile_95'], label='95th percentile')\n",
    "#plt.plot(res['elapsed_min'], res['max_response_time'], label='max_response_time')\n",
    "plt.ylim((0,4000))\n",
    "plt.xlabel('Time (minutes)')\n",
    "plt.ylabel('Average Response Time (ms)')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(512)\n",
    "plt.plot(res['elapsed_min'], res['user_count'])\n",
    "plt.xlabel('Time (minutes)')\n",
    "plt.ylabel('Num of Users')\n",
    "\n",
    "plt.subplot(513)\n",
    "plt.plot(res['elapsed_min'], res['total_rps'])\n",
    "plt.xlabel('Time (minutes)')\n",
    "plt.ylabel('Throughput (req/s)')\n",
    "\n",
    "plt.subplot(514)\n",
    "plt.plot(res['elapsed_min'], res['fail_ratio'])\n",
    "plt.xlabel('Time (minutes)')\n",
    "plt.ylabel('Fail Ratio')\n",
    "\n",
    "plt.subplot(515)\n",
    "plt.plot(res['elapsed_min'], res['custom_r1'])\n",
    "plt.plot(res['elapsed_min'], res['custom_r2'])\n",
    "plt.xlabel('Time (minutes)')\n",
    "plt.ylabel('Custom Value')\n",
    "\n",
    "\n",
    "\n",
    "filename = filename.replace('aa.csv', '')\n",
    "plt.savefig(filename + '.png', dpi=300)\n",
    "plt.savefig(filename + '.pdf')\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  }
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