From 41c46586b83543305c00dadaa8f486136c18ef59 Mon Sep 17 00:00:00 2001 From: IndikaKuma Date: Sun, 12 Sep 2021 22:22:48 +0200 Subject: [PATCH] add the microservice anomaly detection and explanation implementation --- made/Data_Scraping_Preparation.ipynb | 1719 + made/Data_Scraping_Preparation.py | 497 + made/Deployment.py | 260 + made/Experiments/README.txt | 3 + made/Experiments/runExperiments.py | 69 + made/Experiments/templates/node-cpuhog.yml | 42 + made/Experiments/templates/node-iostress.yml | 45 + made/Experiments/templates/node-memhog.yml | 42 + made/Experiments/templates/pod-cpuhog.yml | 36 + made/Experiments/templates/pod-del.yml | 39 + made/Experiments/templates/pod-iostress.yml | 37 + made/Experiments/templates/pod-memhog.yml | 35 + made/Experiments/templates/pod-netcorr.yml | 49 + made/Experiments/templates/pod-netlat.yml | 48 + made/Experiments/templates/pod-netloss.yml | 49 + made/Infrastructure/Istio/istioGateway.yml | 35 + .../kube-state-metrics-configs/README.md | 2 + .../cluster-role-binding.yaml | 15 + .../cluster-role.yaml | 110 + .../deployment.yaml | 42 + .../service-account.yaml | 8 + .../kube-state-metrics-configs/service.yaml | 19 + made/Infrastructure/Litmus/adminrbac.yml | 61 + made/Infrastructure/Litmus/chaos-exporter.yml | 45 + .../kubernetes-node-exporter/README.md | 1 + .../kubernetes-node-exporter/daemonset.yaml | 55 + .../kubernetes-node-exporter/service.yaml | 18 + .../kubernetes-prometheus/README.md | 19 + .../kubernetes-prometheus/clusterRole.yaml | 33 + .../kubernetes-prometheus/config-map.yaml | 183 + .../prometheus-deployment.yaml | 38 + .../prometheus-ingress.yaml | 33 + .../prometheus-service.yaml | 17 + .../Skydive/prom_skydive_con.yml | 54 + .../Skydive/skydive_agents_analyzer.yml | 213 + .../Teastore/teastore-clusterip.yaml | 289 + made/Load Generation/README.txt | 13 + made/Load Generation/httploadgenerator.jar | Bin 0 -> 1658907 bytes made/Load Generation/intensity.csv | 34597 +++++++ made/Load Generation/results.csv | 8319 ++ made/Load Generation/teastore_browse.lua | 93 + .../adaboost/models/best_dt_adaboost.pkl | Bin 0 -> 4383582 bytes .../adaboost/results/results_final_ada_dt.pkl | Bin 0 -> 2835 bytes .../models/best_decision_tree.pkl | Bin 0 -> 79714 bytes .../decision_tree/results/results_final.pkl | Bin 0 -> 5645 bytes .../models/best_random_forest.pkl | Bin 0 -> 5643263 bytes .../random_forest/results/results_final.pkl | Bin 0 -> 3029 bytes made/Models_XAI.ipynb | 78066 ++++++++++++++++ made/Overview.txt | 13 + made/README.txt | 3 + rule-based/src/test/resources/alert.json | 80 + .../src/test/resources/refactoring_snowss.drl | 52 + 52 files changed, 125496 insertions(+) create mode 100644 made/Data_Scraping_Preparation.ipynb create mode 100644 made/Data_Scraping_Preparation.py create mode 100644 made/Deployment.py create mode 100644 made/Experiments/README.txt create mode 100644 made/Experiments/runExperiments.py create mode 100644 made/Experiments/templates/node-cpuhog.yml create mode 100644 made/Experiments/templates/node-iostress.yml create mode 100644 made/Experiments/templates/node-memhog.yml create mode 100644 made/Experiments/templates/pod-cpuhog.yml create mode 100644 made/Experiments/templates/pod-del.yml create mode 100644 made/Experiments/templates/pod-iostress.yml create mode 100644 made/Experiments/templates/pod-memhog.yml create mode 100644 made/Experiments/templates/pod-netcorr.yml create mode 100644 made/Experiments/templates/pod-netlat.yml create mode 100644 made/Experiments/templates/pod-netloss.yml create mode 100644 made/Infrastructure/Istio/istioGateway.yml create mode 100644 made/Infrastructure/Kubernetes/kube-state-metrics-configs/README.md create mode 100644 made/Infrastructure/Kubernetes/kube-state-metrics-configs/cluster-role-binding.yaml create mode 100644 made/Infrastructure/Kubernetes/kube-state-metrics-configs/cluster-role.yaml create mode 100644 made/Infrastructure/Kubernetes/kube-state-metrics-configs/deployment.yaml create mode 100644 made/Infrastructure/Kubernetes/kube-state-metrics-configs/service-account.yaml create mode 100644 made/Infrastructure/Kubernetes/kube-state-metrics-configs/service.yaml create mode 100644 made/Infrastructure/Litmus/adminrbac.yml create mode 100644 made/Infrastructure/Litmus/chaos-exporter.yml create mode 100644 made/Infrastructure/Prometheus/kubernetes-node-exporter/README.md create mode 100644 made/Infrastructure/Prometheus/kubernetes-node-exporter/daemonset.yaml create mode 100644 made/Infrastructure/Prometheus/kubernetes-node-exporter/service.yaml create mode 100644 made/Infrastructure/Prometheus/kubernetes-prometheus/README.md create mode 100644 made/Infrastructure/Prometheus/kubernetes-prometheus/clusterRole.yaml create mode 100644 made/Infrastructure/Prometheus/kubernetes-prometheus/config-map.yaml create mode 100644 made/Infrastructure/Prometheus/kubernetes-prometheus/prometheus-deployment.yaml create mode 100644 made/Infrastructure/Prometheus/kubernetes-prometheus/prometheus-ingress.yaml create mode 100644 made/Infrastructure/Prometheus/kubernetes-prometheus/prometheus-service.yaml create mode 100644 made/Infrastructure/Skydive/prom_skydive_con.yml create mode 100644 made/Infrastructure/Skydive/skydive_agents_analyzer.yml create mode 100644 made/Infrastructure/Teastore/teastore-clusterip.yaml create mode 100644 made/Load Generation/README.txt create mode 100644 made/Load Generation/httploadgenerator.jar create mode 100644 made/Load Generation/intensity.csv create mode 100644 made/Load Generation/results.csv create mode 100644 made/Load Generation/teastore_browse.lua create mode 100644 made/Models/models/adaboost/models/best_dt_adaboost.pkl create mode 100644 made/Models/models/adaboost/results/results_final_ada_dt.pkl create mode 100644 made/Models/models/decision_tree/models/best_decision_tree.pkl create mode 100644 made/Models/models/decision_tree/results/results_final.pkl create mode 100644 made/Models/models/random_forest/models/best_random_forest.pkl create mode 100644 made/Models/models/random_forest/results/results_final.pkl create mode 100644 made/Models_XAI.ipynb create mode 100644 made/Overview.txt create mode 100644 made/README.txt create mode 100644 rule-based/src/test/resources/alert.json create mode 100644 rule-based/src/test/resources/refactoring_snowss.drl diff --git a/made/Data_Scraping_Preparation.ipynb b/made/Data_Scraping_Preparation.ipynb new file mode 100644 index 0000000..1413d49 --- /dev/null +++ b/made/Data_Scraping_Preparation.ipynb @@ -0,0 +1,1719 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "source": [ + "# Packages are imported.\r\n", + "import pandas as pd\r\n", + "import requests as req\r\n", + "import numpy as np\r\n", + "import datetime as dt\r\n", + "import time\r\n", + "import multiprocessing as mp\r\n", + "import os\r\n", + "import random\r\n", + "import sys\r\n", + "import matplotlib.pyplot as plt\r\n", + "import seaborn as sns\r\n", + "from scipy import stats\r\n", + "import statsmodels.stats.multitest as statsmodels\r\n", + "import pickle\r\n", + "np.random.seed(1)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "sys.setrecursionlimit(25000)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# Data Extracting" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# Function that requests data. Change IP to external IP of one of the VM's collecting data.\r\n", + "def request_data(job, startTime, endTime):\r\n", + " \r\n", + " print('Job request: ' + str(job) + ' started.')\r\n", + " #print(startTime)\r\n", + " #print(job)\r\n", + " #print(endTime)\r\n", + " request = req.get('http://35.246.188.180:30000/api/v1/query_range?query={job=~\"' + job + '\"}&start=' + startTime + 'Z&end=' + endTime + 'Z&step=1s')\r\n", + " #print(eval(request.content))\r\n", + " metric_data = pd.DataFrame(eval(request.content))\r\n", + " \r\n", + " return metric_data" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# Change IP to external IP of one of the VM's collecting data.\r\n", + "def get_jobs():\r\n", + " # Cluster: Get list of jobs\r\n", + " x = req.get('http://35.246.188.180:30000/api/v1/label/job/values')\r\n", + " jobList = eval(x.content)['data']\r\n", + " \r\n", + " return jobList\r\n", + "\r\n", + "# Get timestamps for next 15mins.\r\n", + "def get_timestamps(date):\r\n", + " \r\n", + " scrape_timeframes = []\r\n", + " interval = dt.timedelta(minutes=14, seconds=59)\r\n", + "\r\n", + " scrape_timeframes.append([str(date).replace(\" \", \"T\"), str(date + interval).replace(\" \", \"T\"), pd.date_range(date, (date + interval), freq='1S')])\r\n", + " \r\n", + " return scrape_timeframes\r\n", + "\r\n", + "# Info of features is saved in separate files.\r\n", + "def save_feature_info(job, dfList_info):\r\n", + " \r\n", + " dfFeatures_info = pd.concat(dfList_info, axis=1)\r\n", + " dfFeatures_info.columns = [str(column) + dfFeatures_info.columns[column] for column in range(0, len(dfFeatures_info.columns))]\r\n", + " list_features_info = [dirs for dirs in os.listdir('data_scraped/feature_info/') if job in dirs]\r\n", + " file_number = len(list_features_info) + 1\r\n", + " dfFeatures_info.to_feather('data_scraped/feature_info/features_' + job + str(file_number) + '.ftr')\r\n", + "\r\n", + "# Function to load in data and structure it into one dataframe.\r\n", + "def structureData(job, date):\r\n", + " \r\n", + " scrape_timeframes = get_timestamps(date)\r\n", + " features_dict = {'Timestamp' : scrape_timeframes[0][2]}\r\n", + "\r\n", + " dfMetric = request_data(job, scrape_timeframes[0][0], scrape_timeframes[0][1])\r\n", + " dfFeatures = pd.DataFrame(features_dict)\r\n", + " \r\n", + " print(job + ' metrics: ' + str(len(dfMetric['data'][0])))\r\n", + " \r\n", + " dfList = [pd.DataFrame(metric['values'], columns=['Timestamp', '#'.join(list(metric['metric'].values()))]) for metric in dfMetric['data'][0]]\r\n", + " dfList_info = [pd.DataFrame(metric['metric'].keys(), columns=['#'.join(list(metric['metric'].keys()))]) for metric in dfMetric['data'][0]]\r\n", + " dfList.insert(0, dfFeatures)\r\n", + " \r\n", + " for df in dfList:\r\n", + " if len(df.columns) > 1:\r\n", + " df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='s')\r\n", + " \r\n", + " else:\r\n", + " df['Timestamp'] = pd.to_datetime(df['Timestamp'])\r\n", + " \r\n", + " dfList = [df.set_index('Timestamp', drop=True) for df in dfList]\r\n", + " \r\n", + " \r\n", + " save_feature_info(job, dfList_info)\r\n", + " \r\n", + " dfFeatures = pd.concat(dfList, axis=1)\r\n", + " \r\n", + " \r\n", + " \r\n", + " print('Done: ' + job)\r\n", + " return dfFeatures" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# Function that saves the data as feather files.\r\n", + "def save_data(job, date):\r\n", + " tic = time.clock()\r\n", + " df = structureData(job, date)\r\n", + " file_number = len(os.listdir('data_scraped/' + job + '/')) + 1\r\n", + " df.reset_index(inplace=True)\r\n", + " \r\n", + " df.to_feather('data_scraped/' + job + '/' + job + str(file_number - 1) + '.ftr')\r\n", + " toc = time.clock()\r\n", + " print(job + ': ' + str(toc-tic))\r\n", + " \r\n", + " return df\r\n" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# For each separate job, data is requested in a timeframe of 15mins, structured, and then saved.\r\n", + "job_list = ['envoy-stats', 'istiod', 'kube-state-metrics', 'kubernetes-apiservers', 'kubernetes-cadvisor', 'kubernetes-nodes', 'kubernetes-pods', 'kubernetes-service-endpoints', 'litmuschaos', 'skydive']\r\n", + "def scrape_data(job_list):\r\n", + " dfDates = pd.date_range('11:00', '15:45', freq='15min')\r\n", + " interval = dt.timedelta(minutes=15, seconds=1)\r\n", + " now = dt.datetime.now()\r\n", + " \r\n", + " \r\n", + " for date in dfDates:\r\n", + " time_passed = False\r\n", + " \r\n", + " while time_passed is False:\r\n", + " \r\n", + " if now > (date + interval):\r\n", + " \r\n", + " time_passed = True\r\n", + " \r\n", + " \r\n", + " for job in job_list:\r\n", + " \r\n", + " df = save_data(job, date)\r\n", + " time.sleep(5) \r\n", + " \r\n", + " else:\r\n", + " print('Too early')\r\n", + " time.sleep(60)\r\n", + " \r\n", + " now = dt.datetime.now()" + ], + "outputs": [], + "metadata": { + "scrolled": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# The function to collect data is run.\r\n", + "scrape_data(job_list)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# Data preparation" + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "The collected data is filtered. Litmus related and constant features are removed along with features containing too many missing values. " + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 32, + "source": [ + "# Litmus related features are removed.\r\n", + "def filter_chaos(dataset):\r\n", + "\r\n", + " filter_keywords_list = ['litmus', 'litmuschaos', 'chaos', 'chaos-runner', 'hog', 'iostress', 'helper'] \r\n", + " column_list = []\r\n", + " iteration_list = []\r\n", + " final_feature_list = []\r\n", + " checked_shape = False\r\n", + " dataframes_list = []\r\n", + " \r\n", + " df = pd.read_feather(dataset)\r\n", + "\r\n", + " for column in df.columns:\r\n", + " if any(keyword in column for keyword in filter_keywords_list) == False:\r\n", + " iteration_list.append(column)\r\n", + " \r\n", + " return iteration_list" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 33, + "source": [ + "# Function to convert datatypes to numeric.\r\n", + "def convert_datatype(column_data):\r\n", + "\r\n", + " return column_data[0][column_data[1]].apply(pd.to_numeric, errors='coerce') " + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 34, + "source": [ + "# Parallelization function.\r\n", + "def modifyParallelized(function, df):\r\n", + " \r\n", + " \r\n", + " column_list = [[df, column] for column in df.columns]\r\n", + " \r\n", + " # All the available cores are used.\r\n", + " cores=mp.cpu_count()\r\n", + "\r\n", + " # Create the multiprocessing pool of cores.\r\n", + " pool = mp.Pool(cores)\r\n", + " \r\n", + " \r\n", + " columns_converted = pool.map(function, column_list)\r\n", + " \r\n", + "\r\n", + " # Close down the pool and join.\r\n", + " pool.close()\r\n", + " pool.join()\r\n", + " #pool.clear()\r\n", + " \r\n", + " return columns_converted" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 38, + "source": [ + "# Scraped data for jobs are read, combined, filtered, and saved to pickle files. \r\n", + "job_list = ['envoy-stats', 'istiod', 'kube-state-metrics', 'kubernetes-apiservers', 'kubernetes-cadvisor', 'kubernetes-nodes', 'kubernetes-pods', 'kubernetes-service-endpoints', 'litmuschaos', 'skydive']\r\n", + "filter_keywords_list = ['litmus', 'litmuschaos', 'chaos', 'chaos-runner', 'hog', 'iostress', 'helper'] \r\n", + "\r\n", + "for job in job_list:\r\n", + " \r\n", + " if os.path.isfile('data_combined_filtered/' + job + '/' + job + '_filtered.pkl') == True:\r\n", + " continue\r\n", + " \r\n", + " column_list = []\r\n", + " iteration_list = []\r\n", + " final_feature_list = []\r\n", + " dataframes_list = []\r\n", + " \r\n", + " # Paths for each file are generated.\r\n", + " dataset_dirs = ['data_scraped/' + job + '/' + file for file in os.listdir('data_scraped/' + job) if job in file]\r\n", + " dataset_dirs.sort()\r\n", + " \r\n", + " print('Start Job: ' + job)\r\n", + " \r\n", + " df = pd.read_feather(dataset_dirs[0])\r\n", + " \r\n", + " print(df.shape)\r\n", + " \r\n", + " # Any non-litmus job is filtered on litmus related features.\r\n", + " if job != 'litmuschaos':\r\n", + " column_list = filter_chaos(dataset_dirs[0])\r\n", + " df = df[column_list]\r\n", + " \r\n", + " # NA's are dropped, features having more than 5 NA's are dropped.\r\n", + " df.dropna(axis=1, inplace=True, thresh=(len(df) - 5))\r\n", + " print(df.shape)\r\n", + " column_list = [column for column in df.columns if \"Timestamp\" not in column]\r\n", + " df[column_list] = pd.concat(modifyParallelized(convert_datatype, df[column_list]), axis=1)\r\n", + " \r\n", + " \r\n", + " print(df.shape)\r\n", + " \r\n", + " df.dropna(axis=1, inplace=True, thresh=(len(df) - 5))\r\n", + " \r\n", + " \r\n", + " column_list = df.columns\r\n", + " df.set_index('Timestamp', drop=True, inplace=True)\r\n", + " \r\n", + " # All datasets are merged into one dataset.\r\n", + " for dataset in dataset_dirs[1:]:\r\n", + " print(dataset)\r\n", + " df_concat = pd.read_feather(dataset).set_index('Timestamp', drop=True)\r\n", + " concat_columns = list(set(column_list).intersection(df_concat.columns))\r\n", + " df_concat = pd.concat(modifyParallelized(convert_datatype, df_concat[concat_columns]), axis=1)\r\n", + " \r\n", + " df = pd.concat([df, df_concat[concat_columns]], axis=0)\r\n", + " time.sleep(2)\r\n", + " \r\n", + " \r\n", + " print(df.shape)\r\n", + " \r\n", + " # For litmuschaos, only features showing which experiment is running are kept.\r\n", + " if job == 'litmuschaos':\r\n", + " \r\n", + " df.dropna(axis=0, inplace=True)\r\n", + " df = df[[column for column in df.columns if 'awaited_experiments' in column]]\r\n", + " \r\n", + " # Final filters are executed.\r\n", + " else:\r\n", + " column_list = filter_chaos(dataset_dirs[0])\r\n", + " df.reset_index(drop=False, inplace=True)\r\n", + " df.dropna(axis=1, inplace=True, thresh=(len(df) - 5))\r\n", + " df.dropna(axis=0, inplace=True)\r\n", + " df = df.loc[:, (df != df.iloc[0]).any()]\r\n", + " \r\n", + " \r\n", + " print(df.shape)\r\n", + " \r\n", + " df.to_pickle('data_combined_filtered/' + job + '/' + job + '_filtered.pkl')" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Start Job: litmuschaos\n", + "(900, 199)\n", + "(900, 119)\n", + "(900, 119)\n", + "data_scraped/litmuschaos/litmuschaos10.ftr\n", + "data_scraped/litmuschaos/litmuschaos11.ftr\n", + "data_scraped/litmuschaos/litmuschaos12.ftr\n", + "data_scraped/litmuschaos/litmuschaos13.ftr\n", + "data_scraped/litmuschaos/litmuschaos14.ftr\n", + "data_scraped/litmuschaos/litmuschaos15.ftr\n", + "data_scraped/litmuschaos/litmuschaos16.ftr\n", + "data_scraped/litmuschaos/litmuschaos17.ftr\n", + "data_scraped/litmuschaos/litmuschaos18.ftr\n", + "data_scraped/litmuschaos/litmuschaos19.ftr\n", + "data_scraped/litmuschaos/litmuschaos2.ftr\n", + "data_scraped/litmuschaos/litmuschaos20.ftr\n", + "data_scraped/litmuschaos/litmuschaos21.ftr\n", + "data_scraped/litmuschaos/litmuschaos22.ftr\n", + "data_scraped/litmuschaos/litmuschaos23.ftr\n", + "data_scraped/litmuschaos/litmuschaos24.ftr\n", + "data_scraped/litmuschaos/litmuschaos25.ftr\n", + "data_scraped/litmuschaos/litmuschaos26.ftr\n", + "data_scraped/litmuschaos/litmuschaos27.ftr\n", + "data_scraped/litmuschaos/litmuschaos28.ftr\n", + "data_scraped/litmuschaos/litmuschaos29.ftr\n", + "data_scraped/litmuschaos/litmuschaos3.ftr\n", + "data_scraped/litmuschaos/litmuschaos4.ftr\n", + "data_scraped/litmuschaos/litmuschaos5.ftr\n", + "data_scraped/litmuschaos/litmuschaos6.ftr\n", + "data_scraped/litmuschaos/litmuschaos7.ftr\n", + "data_scraped/litmuschaos/litmuschaos8.ftr\n", + "data_scraped/litmuschaos/litmuschaos9.ftr\n", + "(26100, 118)\n", + "(26099, 7)\n" + ] + } + ], + "metadata": { + "scrolled": true + } + }, + { + "cell_type": "code", + "execution_count": 9, + "source": [ + "# Feature summary of all jobs. \r\n", + "for job in job_list:\r\n", + " df = pd.read_pickle('data_combined_filtered/' + job + '/' + job + '_filtered.pkl')\r\n", + " print(job)\r\n", + " print(df.shape)" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "envoy-stats\n", + "(26096, 1845)\n", + "istiod\n", + "(26100, 148)\n", + "kube-state-metrics\n", + "(26100, 73)\n", + "kubernetes-apiservers\n", + "(26100, 6)\n", + "kubernetes-cadvisor\n", + "(26092, 3205)\n", + "kubernetes-nodes\n", + "(26090, 1467)\n", + "kubernetes-pods\n", + "(26095, 2390)\n", + "kubernetes-service-endpoints\n", + "(26095, 2386)\n", + "litmuschaos\n", + "(26099, 7)\n", + "skydive\n", + "(26100, 6)\n" + ] + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 25, + "source": [ + "# Metrics are generated for each dataset based on a 45 second sliding window. They are also saved.\r\n", + "def generate_metrics(job):\r\n", + " \r\n", + " df = pd.read_pickle('data_combined_filtered/' + job + '/' + job + '_filtered.pkl').set_index('Timestamp', drop=True).sort_values(by='Timestamp')\r\n", + " \r\n", + " dfMetric = df.rolling('45s').mean()\r\n", + " dfMetric.columns = ['mean_' + column for column in dfMetric.columns]\r\n", + " dfMetric.to_pickle('metrics/' + job + '/' + '_mean_' + job + '.pkl')\r\n", + " \r\n", + " dfMetric = df.rolling('45s').quantile(0.05)\r\n", + " dfMetric.columns = ['quantile05_' + column for column in dfMetric.columns]\r\n", + " dfMetric.to_pickle('metrics/' + job + '/' + '_quantile05_' + job + '.pkl')\r\n", + " \r\n", + " dfMetric = df.rolling('45s').quantile(0.25)\r\n", + " dfMetric.columns = ['quantile25_' + column for column in dfMetric.columns]\r\n", + " dfMetric.to_pickle('metrics/' + job + '/' + '_quantile25_' + job + '.pkl')\r\n", + "\r\n", + " dfMetric = df.rolling('45s').quantile(0.50)\r\n", + " dfMetric.columns = ['quantile50_' + column for column in dfMetric.columns]\r\n", + " dfMetric.to_pickle('metrics/' + job + '/' + '_quantile50_' + job + '.pkl')\r\n", + " \r\n", + " dfMetric = df.rolling('45s').quantile(0.75)\r\n", + " dfMetric.columns = ['quantile75_' + column for column in dfMetric.columns]\r\n", + " dfMetric.to_pickle('metrics/' + job + '/' + '_quantile75_' + job + '.pkl')\r\n", + " \r\n", + " dfMetric = df.rolling('45s').quantile(0.95)\r\n", + " dfMetric.columns = ['quantile95_' + column for column in dfMetric.columns]\r\n", + " dfMetric.to_pickle('metrics/' + job + '/' + '_quantile95_' + job + '.pkl')\r\n", + " \r\n", + " dfMetric = df.rolling('45s').var()\r\n", + " dfMetric.columns = ['variance_' + column for column in dfMetric.columns]\r\n", + " dfMetric.to_pickle('metrics/' + job + '/' + '_variance_' + job + '.pkl')\r\n", + " \r\n", + " dfMetric = df.rolling('45s').skew()\r\n", + " dfMetric.columns = ['skewness_' + column for column in dfMetric.columns]\r\n", + " dfMetric.to_pickle('metrics/' + job + '/' + '_skewness_' + job + '.pkl')\r\n", + " \r\n", + " dfMetric = df.rolling('45s').min()\r\n", + " dfMetric.columns = ['minimum_' + column for column in dfMetric.columns]\r\n", + " dfMetric.to_pickle('metrics/' + job + '/' + '_minimum_' + job + '.pkl')\r\n", + " \r\n", + " dfMetric = df.rolling('45s').max()\r\n", + " dfMetric.columns = ['maximum_' + column for column in dfMetric.columns]\r\n", + " dfMetric.to_pickle('metrics/' + job + '/' + '_maximum_' + job + '.pkl')\r\n", + " \r\n", + " dfMetric = df.rolling('45s').kurt()\r\n", + " dfMetric.columns = ['kurtosis_' + column for column in dfMetric.columns]\r\n", + " dfMetric.to_pickle('metrics/' + job + '/' + '_kurtosis_' + job + '.pkl')\r\n", + " \r\n", + " return job + ' Done'\r\n", + "\r\n", + "def generateParallelized(function, job_list):\r\n", + " \r\n", + " # All the available cores are used.\r\n", + " cores=mp.cpu_count()\r\n", + "\r\n", + " # Create the multiprocessing pool of cores.\r\n", + " pool = mp.Pool(cores)\r\n", + " \r\n", + " \r\n", + " columns_converted = pool.map(function, job_list)\r\n", + " \r\n", + "\r\n", + " # Close down the pool and join.\r\n", + " pool.close()\r\n", + " pool.join()\r\n", + " #pool.clear()\r\n", + " \r\n", + " return columns_converted \r\n" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# Metrics are generated for each dataset.\r\n", + "generateParallelized(generate_metrics, [job for job in job_list if job != 'litmuschaos'])" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 32, + "source": [ + "# Rated features are also saved.\r\n", + "df = pd.DataFrame()\r\n", + "for job in [job for job in job_list if job != 'litmuschaos']:\r\n", + " df_new = pd.read_pickle('data_combined_filtered/' + job + '/' + job + '_filtered.pkl').set_index('Timestamp', drop=True)\r\n", + " df = pd.concat([df, df_new[[column for column in df_new.columns if 'total' in column]]], axis=1)\r\n", + " print(job)\r\n", + " print(df_new.shape)\r\n", + "\r\n", + "df.sort_values(by='Timestamp', inplace=True)\r\n", + "df.columns = ['rated_' + column for column in df.columns]\r\n", + "df = df.diff()\r\n", + "df.reset_index(drop=False, inplace=True)\r\n", + "df.to_pickle('data_combined_filtered/totals-rated/totals-rated_filtered.pkl')" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 33, + "source": [ + "# Metrics are generated for the rated metrics.\r\n", + "generateParallelized(generate_metrics, ['totals-rated'])" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "source": [ + "# Read litmus data\r\n", + "df = pd.read_pickle('data_combined_filtered/litmuschaos/litmuschaos_filtered.pkl')" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 43, + "source": [ + "# Rename litmus features to right experiment names. \r\n", + "\r\n", + "new_names_dict = {'litmuschaos_awaited_experiments#teastore-chaos#teastore-chaos-node-cpu-hog#default#35.246.222.195:31111#litmuschaos' : 'Node CPU hog',\r\n", + " 'litmuschaos_awaited_experiments#teastore-chaos#teastore-chaos-node-io-stress#default#35.246.222.195:31111#litmuschaos' : 'Node IO stress',\r\n", + " 'litmuschaos_awaited_experiments#teastore-chaos#teastore-chaos-node-memory-hog#default#35.246.222.195:31111#litmuschaos' : 'Node memory hog',\r\n", + " 'litmuschaos_awaited_experiments#teastore-chaos#teastore-chaos-pod-cpu-hog#default#35.246.222.195:31111#litmuschaos' : 'Pod CPU hog',\r\n", + " 'litmuschaos_awaited_experiments#teastore-chaos#teastore-chaos-pod-io-stress#default#35.246.222.195:31111#litmuschaos' : 'Pod IO stress',\r\n", + " 'litmuschaos_awaited_experiments#teastore-chaos#teastore-chaos-pod-memory-hog#default#35.246.222.195:31111#litmuschaos' : 'Pod memory hog',\r\n", + " 'litmuschaos_namespace_scoped_awaited_experiments#default#35.246.222.195:31111#litmuschaos' : 'Anomalous'}\r\n", + "\r\n", + "df.rename(columns=new_names_dict, inplace=True)\r\n" + ], + "outputs": [], + "metadata": { + "scrolled": true + } + }, + { + "cell_type": "code", + "execution_count": 44, + "source": [ + "# All experiments are combined into one multi-class feature. \r\n", + "conditions = [\r\n", + " (df['Node CPU hog'] == 1),\r\n", + " (df['Node IO stress'] == 1),\r\n", + " (df['Node memory hog'] == 1),\r\n", + " (df['Pod CPU hog'] == 1),\r\n", + " (df['Pod IO stress'] == 1),\r\n", + " (df['Pod memory hog'] == 1),\r\n", + " (df['Anomalous'] == 0)]\r\n", + "\r\n", + "options = ['Node CPU hog', 'Node IO stress', 'Node memory hog', 'Pod CPU hog', 'Pod IO stress', 'Pod memory hog', 'Non-anomalous']\r\n", + "options_numbers = [0, 1, 2, 3, 4, 5, 6]\r\n", + "df['Anomaly_name'] = np.select(conditions, options)\r\n", + "df['Anomaly_nr'] = np.select(conditions, options_numbers)\r\n" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 46, + "source": [ + "# The changes are saved into a new file.\r\n", + "df.to_pickle('finalLitmus/litmusfinal.pkl')" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# Feature Selection" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 38, + "source": [ + "# Function to calculate p-value for given columns in a df.\r\n", + "def calcPValue(df, column):\r\n", + " \r\n", + " pvalue = stats.ks_2samp(df.loc[df['Anomalous']==0, column].values.tolist(), df.loc[df['Anomalous']==1, column].values.tolist())[1]\r\n", + " \r\n", + " return pvalue\r\n", + "\r\n", + "feature_pvalues = []\r\n", + "job_list = ['envoy-stats', 'istiod', 'kube-state-metrics', 'kubernetes-apiservers', 'kubernetes-cadvisor', 'kubernetes-nodes', 'kubernetes-pods', 'kubernetes-service-endpoints', 'totals-rated', 'skydive']\r\n", + "folders = ['data_combined_filtered/', 'metrics/']" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 39, + "source": [ + "# Get paths of files.\r\n", + "def getFilePaths():\r\n", + " \r\n", + " \r\n", + " directories = []\r\n", + " \r\n", + " \r\n", + " for root, dirs, files in os.walk('metrics/', topdown=False):\r\n", + " for name in files:\r\n", + " \r\n", + " if ('ipynb' not in name) and ('litmus' not in name):\r\n", + " \r\n", + " directories.append(os.path.join(root, name))\r\n", + " \r\n", + " return directories" + ], + "outputs": [], + "metadata": { + "scrolled": false + } + }, + { + "cell_type": "code", + "execution_count": 40, + "source": [ + "def calc_p_value(directory):\r\n", + " \r\n", + " \r\n", + " dfFeatures = pd.DataFrame(columns=['Feature', 'p_value'])\r\n", + " df_litmus = pd.read_pickle('finalLitmus/litmusfinal.pkl')\r\n", + " if 'data_combined_filtered' in directory:\r\n", + " df_metric = pd.read_pickle(directory).set_index('Timestamp', drop=True)\r\n", + " else:\r\n", + " df_metric = pd.read_pickle(directory)\r\n", + " \r\n", + " df_metric = pd.concat([df_metric, df_litmus['Anomalous']], axis=1)\r\n", + " df_metric.dropna(axis=1, inplace=True, thresh=(len(df_metric) - 20))\r\n", + " df_metric.dropna(axis=0, inplace=True)\r\n", + " \r\n", + " dfFeatures = pd.DataFrame([(column, calcPValue(df_metric, column)) for column in df_metric.columns if ((column != 'anomaly') and (column != 'time') and (column != 'Anomalous'))], columns = ['Feature', 'p_value'])\r\n", + " \r\n", + " return dfFeatures" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 41, + "source": [ + "# Parallelization of p-value calculation.\r\n", + "def pvalue_parallelized(function, directories):\r\n", + " \r\n", + " # All the available cores are used.\r\n", + " cores=mp.cpu_count()\r\n", + "\r\n", + " # Create the multiprocessing pool of cores.\r\n", + " pool = mp.Pool(cores)\r\n", + " \r\n", + " \r\n", + " #Schrijf hier vstack en pd.df\r\n", + " feature_list = pd.DataFrame(np.vstack(pool.map(function, directories)), columns=['Features', 'P_value'])\r\n", + " \r\n", + "\r\n", + " # Close down the pool and join.\r\n", + " pool.close()\r\n", + " pool.join()\r\n", + " #pool.clear()\r\n", + " \r\n", + " return feature_list" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 7, + "source": [ + "df_pvalue = pvalue_parallelized(calc_p_value, getFilePaths())" + ], + "outputs": [], + "metadata": { + "scrolled": true + } + }, + { + "cell_type": "code", + "execution_count": 8, + "source": [ + "df_pvalue.sort_values(by='P_value')" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Features P_value\n", + "75994 quantile25_node_filesystem_files_free#/dev/sda... 0\n", + "77345 quantile25_prometheus_sd_discovered_targets#ku... 0\n", + "85583 quantile95_node_filesystem_files_free#/dev/sda... 0\n", + "85582 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" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 9, + "source": [ + "# P-values are saved.\r\n", + "df_pvalue.to_pickle('feature selection/pvalues_onlymetrics.pkl')" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 6, + "source": [ + "# Function for feature selection.\r\n", + "def select_features(alpha):\r\n", + " df_pvalue = pd.read_pickle('feature selection/pvalues_onlymetrics.pkl')\r\n", + " by_results = statsmodels.multipletests(df_pvalue['P_value'], alpha=alpha, method='fdr_by')\r\n", + " df_pvalue['Rejected'] = by_results[0]\r\n", + " df_pvalue['P_vals_corrected'] = by_results[1]\r\n", + " df_filtered = df_pvalue.loc[df_pvalue.Rejected==True]\r\n", + " \r\n", + " return df_filtered" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 34, + "source": [ + "# Features are selected.\r\n", + "df = select_features(0.000000000000000000000000000000000000001)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# Data Analysis" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 75, + "source": [ + "pd.read_pickle('finalLitmus/litmusfinal.pkl')" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Node CPU hog Node IO stress Node memory hog \\\n", + "Timestamp \n", + "2021-07-27 18:17:00 0.0 0.0 0.0 \n", + "2021-07-27 18:17:01 0.0 0.0 0.0 \n", + "2021-07-27 18:17:02 0.0 0.0 0.0 \n", + "2021-07-27 18:17:03 0.0 0.0 0.0 \n", + "2021-07-27 18:17:04 0.0 0.0 0.0 \n", + "... ... ... ... \n", + "2021-07-27 20:31:55 0.0 0.0 0.0 \n", + "2021-07-27 20:31:56 0.0 0.0 0.0 \n", + "2021-07-27 20:31:57 0.0 0.0 0.0 \n", + "2021-07-27 20:31:58 0.0 0.0 0.0 \n", + "2021-07-27 20:31:59 0.0 0.0 0.0 \n", + "\n", + " Pod CPU hog Pod IO stress Pod memory hog Anomalous \\\n", + "Timestamp \n", + "2021-07-27 18:17:00 0.0 0.0 0.0 0.0 \n", + "2021-07-27 18:17:01 0.0 0.0 0.0 0.0 \n", + "2021-07-27 18:17:02 0.0 0.0 0.0 0.0 \n", + "2021-07-27 18:17:03 0.0 0.0 0.0 0.0 \n", + "2021-07-27 18:17:04 0.0 0.0 0.0 0.0 \n", + "... ... ... ... ... \n", + "2021-07-27 20:31:55 0.0 1.0 0.0 1.0 \n", + "2021-07-27 20:31:56 0.0 1.0 0.0 1.0 \n", + "2021-07-27 20:31:57 0.0 1.0 0.0 1.0 \n", + "2021-07-27 20:31:58 0.0 1.0 0.0 1.0 \n", + "2021-07-27 20:31:59 0.0 1.0 0.0 1.0 \n", + "\n", + " Anomaly_name Anomaly_nr \n", + "Timestamp \n", + "2021-07-27 18:17:00 Non-anomalous 6 \n", + "2021-07-27 18:17:01 Non-anomalous 6 \n", + "2021-07-27 18:17:02 Non-anomalous 6 \n", + "2021-07-27 18:17:03 Non-anomalous 6 \n", + "2021-07-27 18:17:04 Non-anomalous 6 \n", + "... ... ... \n", + "2021-07-27 20:31:55 Pod IO stress 4 \n", + "2021-07-27 20:31:56 Pod IO stress 4 \n", + "2021-07-27 20:31:57 Pod IO stress 4 \n", + "2021-07-27 20:31:58 Pod IO stress 4 \n", + "2021-07-27 20:31:59 Pod IO stress 4 \n", + "\n", + "[26099 rows x 9 columns]" + ], + "text/html": [ + "
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Node CPU hogNode IO stressNode memory hogPod CPU hogPod IO stressPod memory hogAnomalousAnomaly_nameAnomaly_nr
Timestamp
2021-07-27 18:17:000.00.00.00.00.00.00.0Non-anomalous6
2021-07-27 18:17:010.00.00.00.00.00.00.0Non-anomalous6
2021-07-27 18:17:020.00.00.00.00.00.00.0Non-anomalous6
2021-07-27 18:17:030.00.00.00.00.00.00.0Non-anomalous6
2021-07-27 18:17:040.00.00.00.00.00.00.0Non-anomalous6
..............................
2021-07-27 20:31:550.00.00.00.01.00.01.0Pod IO stress4
2021-07-27 20:31:560.00.00.00.01.00.01.0Pod IO stress4
2021-07-27 20:31:570.00.00.00.01.00.01.0Pod IO stress4
2021-07-27 20:31:580.00.00.00.01.00.01.0Pod IO stress4
2021-07-27 20:31:590.00.00.00.01.00.01.0Pod IO stress4
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26099 rows × 9 columns

\n", + "
" + ] + }, + "metadata": {}, + "execution_count": 75 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 2, + "source": [ + "# Barplot dataframe\r\n", + "df = pd.read_pickle('finalLitmus/litmusfinal.pkl')\r\n", + "barplot = df.groupby(['Anomaly_name'], as_index=False).count()\r\n", + "barplot = barplot[['Anomaly_name', 'Node CPU hog']][1:]" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 20, + "source": [ + "barplot['Anomaly_name']" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1 Node CPU hog\n", + "2 Node IO stress\n", + "3 Node memory hog\n", + "4 Non-anomalous\n", + "5 Pod CPU hog\n", + "6 Pod IO stress\n", + "7 Pod memory hog\n", + "Name: Anomaly_name, dtype: object" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 3, + "source": [ + "sns.set_style('darkgrid') # darkgrid, white grid, dark, white and ticks\r\n", + "plt.figure(figsize=(8,4), tight_layout=True)\r\n", + "plt.rc('axes', titlesize=18) # fontsize of the axes title\r\n", + "plt.rc('axes', labelsize=14) # fontsize of the x and y labels\r\n", + "plt.rc('xtick', labelsize=13) # fontsize of the tick labels\r\n", + "plt.rc('ytick', labelsize=13) # fontsize of the tick labels\r\n", + "plt.rc('legend', fontsize=13) # legend fontsize\r\n", + "plt.rc('font', size=13) # controls default text sizes" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {} + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 4, + "source": [ + "# Show class imbalance.\r\n", + "np.random.seed(1)\r\n", + "plt.figure(figsize=(14,8), tight_layout=True)\r\n", + "ax = sns.barplot(x=barplot['Anomaly_name'], y=barplot['Node CPU hog'], palette='mako', ci=None)\r\n", + "ax.set(title='Class distribution', xlabel='Anomaly', ylabel='Appearances')\r\n", + "\r\n", + "\r\n", + "for p in ax.patches:\r\n", + " ax.annotate(format(p.get_height(), '.1f'), \r\n", + " (p.get_x() + p.get_width() / 2., p.get_height()), \r\n", + " ha = 'center', va = 'center', \r\n", + " xytext = (0, 9), \r\n", + " textcoords = 'offset points')\r\n", + "plt.show()" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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" + }, + "metadata": {} + } + ], + "metadata": {} + }, + { + "cell_type": "markdown", + "source": [ + "# Pre-processing" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 14, + "source": [ + "df.reset_index(inplace=True)" + ], + "outputs": [], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 12, + "source": [ + "df_final = pd.read_pickle('finalLitmus/litmusfinal.pkl')\r\n", + "df_final" + ], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Node CPU hog Node IO stress Node memory hog \\\n", + "Timestamp \n", + "2021-07-27 18:17:00 0.0 0.0 0.0 \n", + "2021-07-27 18:17:01 0.0 0.0 0.0 \n", + "2021-07-27 18:17:02 0.0 0.0 0.0 \n", + "2021-07-27 18:17:03 0.0 0.0 0.0 \n", + "2021-07-27 18:17:04 0.0 0.0 0.0 \n", + "... ... ... ... \n", + "2021-07-27 20:31:55 0.0 0.0 0.0 \n", + "2021-07-27 20:31:56 0.0 0.0 0.0 \n", + "2021-07-27 20:31:57 0.0 0.0 0.0 \n", + "2021-07-27 20:31:58 0.0 0.0 0.0 \n", + "2021-07-27 20:31:59 0.0 0.0 0.0 \n", + "\n", + " Pod CPU hog Pod IO stress Pod memory hog Anomalous \\\n", + "Timestamp \n", + "2021-07-27 18:17:00 0.0 0.0 0.0 0.0 \n", + "2021-07-27 18:17:01 0.0 0.0 0.0 0.0 \n", + "2021-07-27 18:17:02 0.0 0.0 0.0 0.0 \n", + "2021-07-27 18:17:03 0.0 0.0 0.0 0.0 \n", + "2021-07-27 18:17:04 0.0 0.0 0.0 0.0 \n", + "... ... ... ... ... \n", + "2021-07-27 20:31:55 0.0 1.0 0.0 1.0 \n", + "2021-07-27 20:31:56 0.0 1.0 0.0 1.0 \n", + "2021-07-27 20:31:57 0.0 1.0 0.0 1.0 \n", + "2021-07-27 20:31:58 0.0 1.0 0.0 1.0 \n", + "2021-07-27 20:31:59 0.0 1.0 0.0 1.0 \n", + "\n", + " Anomaly_name Anomaly_nr \n", + "Timestamp \n", + "2021-07-27 18:17:00 Non-anomalous 6 \n", + "2021-07-27 18:17:01 Non-anomalous 6 \n", + "2021-07-27 18:17:02 Non-anomalous 6 \n", + "2021-07-27 18:17:03 Non-anomalous 6 \n", + "2021-07-27 18:17:04 Non-anomalous 6 \n", + "... ... ... \n", + "2021-07-27 20:31:55 Pod IO stress 4 \n", + "2021-07-27 20:31:56 Pod IO stress 4 \n", + "2021-07-27 20:31:57 Pod IO stress 4 \n", + "2021-07-27 20:31:58 Pod IO stress 4 \n", + "2021-07-27 20:31:59 Pod IO stress 4 \n", + "\n", + "[26099 rows x 9 columns]" + ], + "text/html": [ + "
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Node CPU hogNode IO stressNode memory hogPod CPU hogPod IO stressPod memory hogAnomalousAnomaly_nameAnomaly_nr
Timestamp
2021-07-27 18:17:000.00.00.00.00.00.00.0Non-anomalous6
2021-07-27 18:17:010.00.00.00.00.00.00.0Non-anomalous6
2021-07-27 18:17:020.00.00.00.00.00.00.0Non-anomalous6
2021-07-27 18:17:030.00.00.00.00.00.00.0Non-anomalous6
2021-07-27 18:17:040.00.00.00.00.00.00.0Non-anomalous6
..............................
2021-07-27 20:31:550.00.00.00.01.00.01.0Pod IO stress4
2021-07-27 20:31:560.00.00.00.01.00.01.0Pod IO stress4
2021-07-27 20:31:570.00.00.00.01.00.01.0Pod IO stress4
2021-07-27 20:31:580.00.00.00.01.00.01.0Pod IO stress4
2021-07-27 20:31:590.00.00.00.01.00.01.0Pod IO stress4
\n", + "

26099 rows × 9 columns

\n", + "
" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 3, + "source": [ + "# Remove 9000 random non-anomalous instances. Show new distribution\r\n", + "df_final = pd.read_pickle('finalLitmus/litmusfinal.pkl')\r\n", + "df_final = df_final[~df_final.index.isin(random.sample(list(df_final[df_final['Anomaly_name']=='Non-anomalous'].index), k=9000))]\r\n", + "barplot = df_final.groupby(['Anomaly_name'], as_index=False).count()\r\n", + "barplot = barplot[['Anomaly_name', 'Node CPU hog']][1:]\r\n", + "\r\n", + "plt.figure(figsize=(14,8), tight_layout=True)\r\n", + "ax = sns.barplot(x=barplot['Anomaly_name'], y=barplot['Node CPU hog'], palette='mako', ci=None)\r\n", + "ax.set(title='Class distribution', xlabel='Anomaly', ylabel='Appearances')\r\n", + "\r\n", + "\r\n", + "for p in ax.patches:\r\n", + " ax.annotate(format(p.get_height(), '.1f'), \r\n", + " (p.get_x() + p.get_width() / 2., p.get_height()), \r\n", + " ha = 'center', va = 'center', \r\n", + " xytext = (0, 9), \r\n", + " textcoords = 'offset points')\r\n", + "plt.show()" + ], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 120, + "source": [ + "# All feature selection in 1.\r\n", + "directories = getFilePaths()\r\n", + "df = pd.read_pickle('finalLitmus/litmusfinal.pkl')\r\n", + "\r\n", + "df_selection = select_features(0.000000000000000000000000000000000000001)\r\n", + "df_selection.to_pickle('feature selection/features_selected7.pkl')\r\n", + "\r\n", + "df_processed = df[~df.index.isin(random.sample(list(df[df['Anomaly_name']=='Non-anomalous'].index), k=9000))]\r\n", + "column_list = df_selection['Features']\r\n", + "\r\n", + "for file in directories:\r\n", + " print(file)\r\n", + " df_new = pd.read_pickle(file)\r\n", + " if file[:4] == 'data':\r\n", + " df_new.set_index('Timestamp', inplace=True)\r\n", + " \r\n", + " \r\n", + " concat_columns = list(set(column_list).intersection(df_new.columns))\r\n", + " df_processed = pd.concat([df_processed, df_new[concat_columns]], axis=1)\r\n", + "\r\n", + "df_processed.dropna(axis=0, inplace=True)\r\n", + "df_processed.drop(['Node CPU hog', 'Node IO stress', 'Node memory hog', 'Pod CPU hog', 'Pod IO stress', 'Pod memory hog', 'Anomalous'], axis=1, inplace=True)\r\n", + "df_processed.drop([column for column in df_processed.columns if 'sd_discovered' in column], axis=1, inplace=True)\r\n", + "df_processed.to_pickle('feature selection/multi-class-final7.pkl')" + ], + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "metrics/kubernetes-pods/_kurtosis_kubernetes-pods.pkl\n", + "metrics/kubernetes-pods/_quantile25_kubernetes-pods.pkl\n", + "metrics/kubernetes-pods/_maximum_kubernetes-pods.pkl\n", + "metrics/kubernetes-pods/_quantile05_kubernetes-pods.pkl\n", + "metrics/kubernetes-pods/_minimum_kubernetes-pods.pkl\n", + "metrics/kubernetes-pods/_skewness_kubernetes-pods.pkl\n", + "metrics/kubernetes-pods/_variance_kubernetes-pods.pkl\n", + "metrics/kubernetes-pods/_quantile95_kubernetes-pods.pkl\n", + "metrics/kubernetes-pods/_quantile75_kubernetes-pods.pkl\n", + "metrics/kubernetes-pods/_mean_kubernetes-pods.pkl\n", + "metrics/kubernetes-pods/_quantile50_kubernetes-pods.pkl\n", + "metrics/kubernetes-nodes/_quantile95_kubernetes-nodes.pkl\n", + "metrics/kubernetes-nodes/_quantile25_kubernetes-nodes.pkl\n", + "metrics/kubernetes-nodes/_mean_kubernetes-nodes.pkl\n", + "metrics/kubernetes-nodes/_quantile05_kubernetes-nodes.pkl\n", + "metrics/kubernetes-nodes/_quantile75_kubernetes-nodes.pkl\n", + "metrics/kubernetes-nodes/_minimum_kubernetes-nodes.pkl\n", + "metrics/kubernetes-nodes/_quantile50_kubernetes-nodes.pkl\n", + "metrics/kubernetes-nodes/_variance_kubernetes-nodes.pkl\n", + "metrics/kubernetes-nodes/_kurtosis_kubernetes-nodes.pkl\n", + "metrics/kubernetes-nodes/_maximum_kubernetes-nodes.pkl\n", + "metrics/kubernetes-nodes/_skewness_kubernetes-nodes.pkl\n", + "metrics/totals-rated/_skewness_totals-rated.pkl\n", + "metrics/totals-rated/_quantile05_totals-rated.pkl\n", + "metrics/totals-rated/_kurtosis_totals-rated.pkl\n", + "metrics/totals-rated/_quantile75_totals-rated.pkl\n", + "metrics/totals-rated/_maximum_totals-rated.pkl\n", + "metrics/totals-rated/_mean_totals-rated.pkl\n", + "metrics/totals-rated/_quantile25_totals-rated.pkl\n", + "metrics/totals-rated/_quantile50_totals-rated.pkl\n", + "metrics/totals-rated/_quantile95_totals-rated.pkl\n", + "metrics/totals-rated/_minimum_totals-rated.pkl\n", + "metrics/totals-rated/_variance_totals-rated.pkl\n", + "metrics/kubernetes-service-endpoints/_quantile75_kubernetes-service-endpoints.pkl\n", + "metrics/kubernetes-service-endpoints/_mean_kubernetes-service-endpoints.pkl\n", + "metrics/kubernetes-service-endpoints/_kurtosis_kubernetes-service-endpoints.pkl\n", + "metrics/kubernetes-service-endpoints/_maximum_kubernetes-service-endpoints.pkl\n", + "metrics/kubernetes-service-endpoints/_minimum_kubernetes-service-endpoints.pkl\n", + "metrics/kubernetes-service-endpoints/_quantile25_kubernetes-service-endpoints.pkl\n", + "metrics/kubernetes-service-endpoints/_quantile05_kubernetes-service-endpoints.pkl\n", + "metrics/kubernetes-service-endpoints/_quantile50_kubernetes-service-endpoints.pkl\n", + "metrics/kubernetes-service-endpoints/_variance_kubernetes-service-endpoints.pkl\n", + "metrics/kubernetes-service-endpoints/_quantile95_kubernetes-service-endpoints.pkl\n", + "metrics/kubernetes-service-endpoints/_skewness_kubernetes-service-endpoints.pkl\n", + "metrics/envoy-stats/_kurtosis_envoy-stats.pkl\n", + "metrics/envoy-stats/_minimum_envoy-stats.pkl\n", + "metrics/envoy-stats/_quantile75_envoy-stats.pkl\n", + "metrics/envoy-stats/_skewness_envoy-stats.pkl\n", + "metrics/envoy-stats/_mean_envoy-stats.pkl\n", + "metrics/envoy-stats/_quantile50_envoy-stats.pkl\n", + "metrics/envoy-stats/_quantile05_envoy-stats.pkl\n", + "metrics/envoy-stats/_maximum_envoy-stats.pkl\n", + "metrics/envoy-stats/_variance_envoy-stats.pkl\n", + "metrics/envoy-stats/_quantile25_envoy-stats.pkl\n", + "metrics/envoy-stats/_quantile95_envoy-stats.pkl\n", + "metrics/istiod/_quantile75_istiod.pkl\n", + "metrics/istiod/_quantile50_istiod.pkl\n", + "metrics/istiod/_variance_istiod.pkl\n", + "metrics/istiod/_quantile25_istiod.pkl\n", + "metrics/istiod/_maximum_istiod.pkl\n", + "metrics/istiod/_skewness_istiod.pkl\n", + "metrics/istiod/_mean_istiod.pkl\n", + "metrics/istiod/_quantile95_istiod.pkl\n", + "metrics/istiod/_minimum_istiod.pkl\n", + "metrics/istiod/_kurtosis_istiod.pkl\n", + "metrics/istiod/_quantile05_istiod.pkl\n", + "metrics/kube-state-metrics/_quantile95_kube-state-metrics.pkl\n", + "metrics/kube-state-metrics/_quantile25_kube-state-metrics.pkl\n", + "metrics/kube-state-metrics/_quantile05_kube-state-metrics.pkl\n", + "metrics/kube-state-metrics/_variance_kube-state-metrics.pkl\n", + "metrics/kube-state-metrics/_quantile50_kube-state-metrics.pkl\n", + "metrics/kube-state-metrics/_mean_kube-state-metrics.pkl\n", + "metrics/kube-state-metrics/_minimum_kube-state-metrics.pkl\n", + "metrics/kube-state-metrics/_skewness_kube-state-metrics.pkl\n", + "metrics/kube-state-metrics/_kurtosis_kube-state-metrics.pkl\n", + "metrics/kube-state-metrics/_quantile75_kube-state-metrics.pkl\n", + "metrics/kube-state-metrics/_maximum_kube-state-metrics.pkl\n", + "metrics/kubernetes-cadvisor/_mean_kubernetes-cadvisor.pkl\n", + "metrics/kubernetes-cadvisor/_quantile95_kubernetes-cadvisor.pkl\n", + "metrics/kubernetes-cadvisor/_kurtosis_kubernetes-cadvisor.pkl\n", + "metrics/kubernetes-cadvisor/_quantile75_kubernetes-cadvisor.pkl\n", + "metrics/kubernetes-cadvisor/_quantile25_kubernetes-cadvisor.pkl\n", + "metrics/kubernetes-cadvisor/_skewness_kubernetes-cadvisor.pkl\n", + "metrics/kubernetes-cadvisor/_quantile50_kubernetes-cadvisor.pkl\n", + "metrics/kubernetes-cadvisor/_variance_kubernetes-cadvisor.pkl\n", + "metrics/kubernetes-cadvisor/_quantile05_kubernetes-cadvisor.pkl\n", + "metrics/kubernetes-cadvisor/_maximum_kubernetes-cadvisor.pkl\n", + "metrics/kubernetes-cadvisor/_minimum_kubernetes-cadvisor.pkl\n", + "metrics/kubernetes-apiservers/_quantile75_kubernetes-apiservers.pkl\n", + "metrics/kubernetes-apiservers/_mean_kubernetes-apiservers.pkl\n", + "metrics/kubernetes-apiservers/_skewness_kubernetes-apiservers.pkl\n", + "metrics/kubernetes-apiservers/_minimum_kubernetes-apiservers.pkl\n", + "metrics/kubernetes-apiservers/_variance_kubernetes-apiservers.pkl\n", + "metrics/kubernetes-apiservers/_quantile25_kubernetes-apiservers.pkl\n", + "metrics/kubernetes-apiservers/_quantile95_kubernetes-apiservers.pkl\n", + "metrics/kubernetes-apiservers/_quantile50_kubernetes-apiservers.pkl\n", + "metrics/kubernetes-apiservers/_quantile05_kubernetes-apiservers.pkl\n", + "metrics/kubernetes-apiservers/_maximum_kubernetes-apiservers.pkl\n", + "metrics/kubernetes-apiservers/_kurtosis_kubernetes-apiservers.pkl\n", + "metrics/skydive/_maximum_skydive.pkl\n", + "metrics/skydive/_skewness_skydive.pkl\n", + "metrics/skydive/_quantile95_skydive.pkl\n", + "metrics/skydive/_quantile05_skydive.pkl\n", + "metrics/skydive/_minimum_skydive.pkl\n", + "metrics/skydive/_variance_skydive.pkl\n", + "metrics/skydive/_quantile75_skydive.pkl\n", + "metrics/skydive/_quantile50_skydive.pkl\n", + "metrics/skydive/_mean_skydive.pkl\n", + "metrics/skydive/_quantile25_skydive.pkl\n", + "metrics/skydive/_kurtosis_skydive.pkl\n" + ] + } + ], + "metadata": {} + } + ], + "metadata": { + "interpreter": { + "hash": "58aa45bce36df36f251b8d9fdbbcb286026eab987cf9474d74d499c1befd119c" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + }, + "metadata": { + "interpreter": { + "hash": "58aa45bce36df36f251b8d9fdbbcb286026eab987cf9474d74d499c1befd119c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/made/Data_Scraping_Preparation.py b/made/Data_Scraping_Preparation.py new file mode 100644 index 0000000..efd60de --- /dev/null +++ b/made/Data_Scraping_Preparation.py @@ -0,0 +1,497 @@ +# Packages are imported. +import pandas as pd +import requests as req +import numpy as np +import datetime as dt +import time +import multiprocessing as mp +import os +import random +import sys +import matplotlib.pyplot as plt +import seaborn as sns +from scipy import stats +import statsmodels.stats.multitest as statsmodels +import pickle +np.random.seed(1) + +sys.setrecursionlimit(25000) + +# Function that requests data. Change IP to external IP of one of the VM's collecting data. +def request_data(job, startTime, endTime): + + print('Job request: ' + str(job) + ' started.') + #print(startTime) + #print(job) + #print(endTime) + request = req.get('http://35.246.188.180:30000/api/v1/query_range?query={job=~"' + job + '"}&start=' + startTime + 'Z&end=' + endTime + 'Z&step=1s') + #print(eval(request.content)) + metric_data = pd.DataFrame(eval(request.content)) + + return metric_data + +# Change IP to external IP of one of the VM's collecting data. +def get_jobs(): + # Cluster: Get list of jobs + x = req.get('http://35.246.188.180:30000/api/v1/label/job/values') + jobList = eval(x.content)['data'] + + return jobList + +# Get timestamps for next 15mins. +def get_timestamps(date): + + scrape_timeframes = [] + interval = dt.timedelta(minutes=14, seconds=59) + + scrape_timeframes.append([str(date).replace(" ", "T"), str(date + interval).replace(" ", "T"), pd.date_range(date, (date + interval), freq='1S')]) + + return scrape_timeframes + +# Info of features is saved in separate files. +def save_feature_info(job, dfList_info): + + dfFeatures_info = pd.concat(dfList_info, axis=1) + dfFeatures_info.columns = [str(column) + dfFeatures_info.columns[column] for column in range(0, len(dfFeatures_info.columns))] + list_features_info = [dirs for dirs in os.listdir('data_scraped/feature_info/') if job in dirs] + file_number = len(list_features_info) + 1 + dfFeatures_info.to_feather('data_scraped/feature_info/features_' + job + str(file_number) + '.ftr') + +# Function to load in data and structure it into one dataframe. +def structureData(job, date): + + scrape_timeframes = get_timestamps(date) + features_dict = {'Timestamp' : scrape_timeframes[0][2]} + + dfMetric = request_data(job, scrape_timeframes[0][0], scrape_timeframes[0][1]) + dfFeatures = pd.DataFrame(features_dict) + + print(job + ' metrics: ' + str(len(dfMetric['data'][0]))) + + dfList = [pd.DataFrame(metric['values'], columns=['Timestamp', '#'.join(list(metric['metric'].values()))]) for metric in dfMetric['data'][0]] + dfList_info = [pd.DataFrame(metric['metric'].keys(), columns=['#'.join(list(metric['metric'].keys()))]) for metric in dfMetric['data'][0]] + dfList.insert(0, dfFeatures) + + for df in dfList: + if len(df.columns) > 1: + df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='s') + + else: + df['Timestamp'] = pd.to_datetime(df['Timestamp']) + + dfList = [df.set_index('Timestamp', drop=True) for df in dfList] + + + save_feature_info(job, dfList_info) + + dfFeatures = pd.concat(dfList, axis=1) + + + + print('Done: ' + job) + return dfFeatures + +# Function that saves the data as feather files. +def save_data(job, date): + tic = time.clock() + df = structureData(job, date) + file_number = len(os.listdir('data_scraped/' + job + '/')) + 1 + df.reset_index(inplace=True) + + df.to_feather('data_scraped/' + job + '/' + job + str(file_number - 1) + '.ftr') + toc = time.clock() + print(job + ': ' + str(toc-tic)) + + return df + +# For each separate job, data is requested in a timeframe of 15mins, structured, and then saved. +job_list = ['envoy-stats', 'istiod', 'kube-state-metrics', 'kubernetes-apiservers', 'kubernetes-cadvisor', 'kubernetes-nodes', 'kubernetes-pods', 'kubernetes-service-endpoints', 'litmuschaos', 'skydive'] +def scrape_data(job_list): + dfDates = pd.date_range('11:00', '15:45', freq='15min') + interval = dt.timedelta(minutes=15, seconds=1) + now = dt.datetime.now() + + + for date in dfDates: + time_passed = False + + while time_passed is False: + + if now > (date + interval): + + time_passed = True + + + for job in job_list: + + df = save_data(job, date) + time.sleep(5) + + else: + print('Too early') + time.sleep(60) + + now = dt.datetime.now() + +# The function to collect data is run. +scrape_data(job_list) + +# The collected data is filtered. Litmus related and constant features are removed along with features containing too many missing values. + +# Litmus related features are removed. +def filter_chaos(dataset): + + filter_keywords_list = ['litmus', 'litmuschaos', 'chaos', 'chaos-runner', 'hog', 'iostress', 'helper'] + column_list = [] + iteration_list = [] + final_feature_list = [] + checked_shape = False + dataframes_list = [] + + df = pd.read_feather(dataset) + + for column in df.columns: + if any(keyword in column for keyword in filter_keywords_list) == False: + iteration_list.append(column) + + return iteration_list + +# Function to convert datatypes to numeric. +def convert_datatype(column_data): + + return column_data[0][column_data[1]].apply(pd.to_numeric, errors='coerce') + +# Parallelization function. +def modifyParallelized(function, df): + + + column_list = [[df, column] for column in df.columns] + + # All the available cores are used. + cores=mp.cpu_count() + + # Create the multiprocessing pool of cores. + pool = mp.Pool(cores) + + + columns_converted = pool.map(function, column_list) + + + # Close down the pool and join. + pool.close() + pool.join() + #pool.clear() + + return columns_converted + +# Scraped data for jobs are read, combined, filtered, and saved to pickle files. +job_list = ['envoy-stats', 'istiod', 'kube-state-metrics', 'kubernetes-apiservers', 'kubernetes-cadvisor', 'kubernetes-nodes', 'kubernetes-pods', 'kubernetes-service-endpoints', 'litmuschaos', 'skydive'] +filter_keywords_list = ['litmus', 'litmuschaos', 'chaos', 'chaos-runner', 'hog', 'iostress', 'helper'] + +for job in job_list: + + if os.path.isfile('data_combined_filtered/' + job + '/' + job + '_filtered.pkl') == True: + continue + + column_list = [] + iteration_list = [] + final_feature_list = [] + dataframes_list = [] + + # Paths for each file are generated. + dataset_dirs = ['data_scraped/' + job + '/' + file for file in os.listdir('data_scraped/' + job) if job in file] + dataset_dirs.sort() + + print('Start Job: ' + job) + + df = pd.read_feather(dataset_dirs[0]) + + print(df.shape) + + # Any non-litmus job is filtered on litmus related features. + if job != 'litmuschaos': + column_list = filter_chaos(dataset_dirs[0]) + df = df[column_list] + + # NA's are dropped, features having more than 5 NA's are dropped. + df.dropna(axis=1, inplace=True, thresh=(len(df) - 5)) + print(df.shape) + column_list = [column for column in df.columns if "Timestamp" not in column] + df[column_list] = pd.concat(modifyParallelized(convert_datatype, df[column_list]), axis=1) + + + print(df.shape) + + df.dropna(axis=1, inplace=True, thresh=(len(df) - 5)) + + + column_list = df.columns + df.set_index('Timestamp', drop=True, inplace=True) + + # All datasets are merged into one dataset. + for dataset in dataset_dirs[1:]: + print(dataset) + df_concat = pd.read_feather(dataset).set_index('Timestamp', drop=True) + concat_columns = list(set(column_list).intersection(df_concat.columns)) + df_concat = pd.concat(modifyParallelized(convert_datatype, df_concat[concat_columns]), axis=1) + + df = pd.concat([df, df_concat[concat_columns]], axis=0) + time.sleep(2) + + + print(df.shape) + + # For litmuschaos, only features showing which experiment is running are kept. + if job == 'litmuschaos': + + df.dropna(axis=0, inplace=True) + df = df[[column for column in df.columns if 'awaited_experiments' in column]] + + # Final filters are executed. + else: + column_list = filter_chaos(dataset_dirs[0]) + df.reset_index(drop=False, inplace=True) + df.dropna(axis=1, inplace=True, thresh=(len(df) - 5)) + df.dropna(axis=0, inplace=True) + df = df.loc[:, (df != df.iloc[0]).any()] + + + print(df.shape) + + df.to_pickle('data_combined_filtered/' + job + '/' + job + '_filtered.pkl') + +# Feature summary of all jobs. +for job in job_list: + df = pd.read_pickle('data_combined_filtered/' + job + '/' + job + '_filtered.pkl') + print(job) + print(df.shape) + +# Metrics are generated for each dataset based on a 45 second sliding window. They are also saved. +def generate_metrics(job): + + df = pd.read_pickle('data_combined_filtered/' + job + '/' + job + '_filtered.pkl').set_index('Timestamp', drop=True).sort_values(by='Timestamp') + + dfMetric = df.rolling('45s').mean() + dfMetric.columns = ['mean_' + column for column in dfMetric.columns] + dfMetric.to_pickle('metrics/' + job + '/' + '_mean_' + job + '.pkl') + + dfMetric = df.rolling('45s').quantile(0.05) + dfMetric.columns = ['quantile05_' + column for column in dfMetric.columns] + dfMetric.to_pickle('metrics/' + job + '/' + '_quantile05_' + job + '.pkl') + + dfMetric = df.rolling('45s').quantile(0.25) + dfMetric.columns = ['quantile25_' + column for column in dfMetric.columns] + dfMetric.to_pickle('metrics/' + job + '/' + '_quantile25_' + job + '.pkl') + + dfMetric = df.rolling('45s').quantile(0.50) + dfMetric.columns = ['quantile50_' + column for column in dfMetric.columns] + dfMetric.to_pickle('metrics/' + job + '/' + '_quantile50_' + job + '.pkl') + + dfMetric = df.rolling('45s').quantile(0.75) + dfMetric.columns = ['quantile75_' + column for column in dfMetric.columns] + dfMetric.to_pickle('metrics/' + job + '/' + '_quantile75_' + job + '.pkl') + + dfMetric = df.rolling('45s').quantile(0.95) + dfMetric.columns = ['quantile95_' + column for column in dfMetric.columns] + dfMetric.to_pickle('metrics/' + job + '/' + '_quantile95_' + job + '.pkl') + + dfMetric = df.rolling('45s').var() + dfMetric.columns = ['variance_' + column for column in dfMetric.columns] + dfMetric.to_pickle('metrics/' + job + '/' + '_variance_' + job + '.pkl') + + dfMetric = df.rolling('45s').skew() + dfMetric.columns = ['skewness_' + column for column in dfMetric.columns] + dfMetric.to_pickle('metrics/' + job + '/' + '_skewness_' + job + '.pkl') + + dfMetric = df.rolling('45s').min() + dfMetric.columns = ['minimum_' + column for column in dfMetric.columns] + dfMetric.to_pickle('metrics/' + job + '/' + '_minimum_' + job + '.pkl') + + dfMetric = df.rolling('45s').max() + dfMetric.columns = ['maximum_' + column for column in dfMetric.columns] + dfMetric.to_pickle('metrics/' + job + '/' + '_maximum_' + job + '.pkl') + + dfMetric = df.rolling('45s').kurt() + dfMetric.columns = ['kurtosis_' + column for column in dfMetric.columns] + dfMetric.to_pickle('metrics/' + job + '/' + '_kurtosis_' + job + '.pkl') + + return job + ' Done' + +def generateParallelized(function, job_list): + + # All the available cores are used. + cores=mp.cpu_count() + + # Create the multiprocessing pool of cores. + pool = mp.Pool(cores) + + + columns_converted = pool.map(function, job_list) + + + # Close down the pool and join. + pool.close() + pool.join() + #pool.clear() + + return columns_converted + +# Metrics are generated for each dataset. +generateParallelized(generate_metrics, [job for job in job_list if job != 'litmuschaos']) + +# Rated features are also saved. +df = pd.DataFrame() +for job in [job for job in job_list if job != 'litmuschaos']: + df_new = pd.read_pickle('data_combined_filtered/' + job + '/' + job + '_filtered.pkl').set_index('Timestamp', drop=True) + df = pd.concat([df, df_new[[column for column in df_new.columns if 'total' in column]]], axis=1) + print(job) + print(df_new.shape) + +df.sort_values(by='Timestamp', inplace=True) +df.columns = ['rated_' + column for column in df.columns] +df = df.diff() +df.reset_index(drop=False, inplace=True) +df.to_pickle('data_combined_filtered/totals-rated/totals-rated_filtered.pkl') + +# Metrics are generated for the rated metrics. +generateParallelized(generate_metrics, ['totals-rated']) + +# Read litmus data +df = pd.read_pickle('data_combined_filtered/litmuschaos/litmuschaos_filtered.pkl') + +# Rename litmus features to right experiment names. + +new_names_dict = {'litmuschaos_awaited_experiments#teastore-chaos#teastore-chaos-node-cpu-hog#default#35.246.222.195:31111#litmuschaos' : 'Node CPU hog', + 'litmuschaos_awaited_experiments#teastore-chaos#teastore-chaos-node-io-stress#default#35.246.222.195:31111#litmuschaos' : 'Node IO stress', + 'litmuschaos_awaited_experiments#teastore-chaos#teastore-chaos-node-memory-hog#default#35.246.222.195:31111#litmuschaos' : 'Node memory hog', + 'litmuschaos_awaited_experiments#teastore-chaos#teastore-chaos-pod-cpu-hog#default#35.246.222.195:31111#litmuschaos' : 'Pod CPU hog', + 'litmuschaos_awaited_experiments#teastore-chaos#teastore-chaos-pod-io-stress#default#35.246.222.195:31111#litmuschaos' : 'Pod IO stress', + 'litmuschaos_awaited_experiments#teastore-chaos#teastore-chaos-pod-memory-hog#default#35.246.222.195:31111#litmuschaos' : 'Pod memory hog', + 'litmuschaos_namespace_scoped_awaited_experiments#default#35.246.222.195:31111#litmuschaos' : 'Anomalous'} + +df.rename(columns=new_names_dict, inplace=True) + +# All experiments are combined into one multi-class feature. +conditions = [ + (df['Node CPU hog'] == 1), + (df['Node IO stress'] == 1), + (df['Node memory hog'] == 1), + (df['Pod CPU hog'] == 1), + (df['Pod IO stress'] == 1), + (df['Pod memory hog'] == 1), + (df['Anomalous'] == 0)] + +options = ['Node CPU hog', 'Node IO stress', 'Node memory hog', 'Pod CPU hog', 'Pod IO stress', 'Pod memory hog', 'Non-anomalous'] +options_numbers = [0, 1, 2, 3, 4, 5, 6] +df['Anomaly_name'] = np.select(conditions, options) +df['Anomaly_nr'] = np.select(conditions, options_numbers) + +# The changes are saved into a new file. +df.to_pickle('finalLitmus/litmusfinal.pkl') + +# Function to calculate p-value for given columns in a df. +def calcPValue(df, column): + + pvalue = stats.ks_2samp(df.loc[df['Anomalous']==0, column].values.tolist(), df.loc[df['Anomalous']==1, column].values.tolist())[1] + + return pvalue + +feature_pvalues = [] +job_list = ['envoy-stats', 'istiod', 'kube-state-metrics', 'kubernetes-apiservers', 'kubernetes-cadvisor', 'kubernetes-nodes', 'kubernetes-pods', 'kubernetes-service-endpoints', 'totals-rated', 'skydive'] +folders = ['data_combined_filtered/', 'metrics/'] + +# Get paths of files. +def getFilePaths(): + + + directories = [] + + + for root, dirs, files in os.walk('metrics/', topdown=False): + for name in files: + + if ('ipynb' not in name) and ('litmus' not in name): + + directories.append(os.path.join(root, name)) + + return directories + +def calc_p_value(directory): + + dfFeatures = pd.DataFrame(columns=['Feature', 'p_value']) + df_litmus = pd.read_pickle('finalLitmus/litmusfinal.pkl') + if 'data_combined_filtered' in directory: + df_metric = pd.read_pickle(directory).set_index('Timestamp', drop=True) + else: + df_metric = pd.read_pickle(directory) + + df_metric = pd.concat([df_metric, df_litmus['Anomalous']], axis=1) + df_metric.dropna(axis=1, inplace=True, thresh=(len(df_metric) - 20)) + df_metric.dropna(axis=0, inplace=True) + + dfFeatures = pd.DataFrame([(column, calcPValue(df_metric, column)) for column in df_metric.columns if ((column != 'anomaly') and (column != 'time') and (column != 'Anomalous'))], columns = ['Feature', 'p_value']) + + return dfFeatures + +# Parallelization of p-value calculation. +def pvalue_parallelized(function, directories): + + # All the available cores are used. + cores=mp.cpu_count() + + # Create the multiprocessing pool of cores. + pool = mp.Pool(cores) + + + #Schrijf hier vstack en pd.df + feature_list = pd.DataFrame(np.vstack(pool.map(function, directories)), columns=['Features', 'P_value']) + + + # Close down the pool and join. + pool.close() + pool.join() + #pool.clear() + + return feature_list + +df_pvalue = pvalue_parallelized(calc_p_value, getFilePaths()) + +df_pvalue.sort_values(by='P_value') + +# P-values are saved. +df_pvalue.to_pickle('feature selection/pvalues_onlymetrics.pkl') + +# Function for feature selection. +def select_features(alpha): + df_pvalue = pd.read_pickle('feature selection/pvalues_onlymetrics.pkl') + by_results = statsmodels.multipletests(df_pvalue['P_value'], alpha=alpha, method='fdr_by') + df_pvalue['Rejected'] = by_results[0] + df_pvalue['P_vals_corrected'] = by_results[1] + df_filtered = df_pvalue.loc[df_pvalue.Rejected==True] + + return df_filtered + +# Features selected, filtered, class imbalance removed by randomly removing 9000 non-anomalous instances. +directories = getFilePaths() +df = pd.read_pickle('finalLitmus/litmusfinal.pkl') + +df_selection = select_features(0.000000000000000000000000000000000000001) +df_selection.to_pickle('feature selection/features_selected7.pkl') + +df_processed = df[~df.index.isin(random.sample(list(df[df['Anomaly_name']=='Non-anomalous'].index), k=9000))] +column_list = df_selection['Features'] + +for file in directories: + print(file) + df_new = pd.read_pickle(file) + if file[:4] == 'data': + df_new.set_index('Timestamp', inplace=True) + + + concat_columns = list(set(column_list).intersection(df_new.columns)) + df_processed = pd.concat([df_processed, df_new[concat_columns]], axis=1) + +df_processed.dropna(axis=0, inplace=True) +df_processed.drop(['Node CPU hog', 'Node IO stress', 'Node memory hog', 'Pod CPU hog', 'Pod IO stress', 'Pod memory hog', 'Anomalous'], axis=1, inplace=True) +df_processed.drop([column for column in df_processed.columns if 'sd_discovered' in column], axis=1, inplace=True) +df_processed.to_pickle('feature selection/multi-class-final7.pkl') \ No newline at end of file diff --git a/made/Deployment.py b/made/Deployment.py new file mode 100644 index 0000000..ad96382 --- /dev/null +++ b/made/Deployment.py @@ -0,0 +1,260 @@ +# Packages are imported. Unusual packages are installed. + +import os + +os.system('pip3 install pyyaml') +os.system('pip3 install ruamel.yaml') + +import yaml +import ruamel.yaml +import copy +from ruamel.yaml.scalarstring import (DoubleQuotedScalarString as dq, + SingleQuotedScalarString as sq) + +# Create GCP firewall rules for ingress and egress directions +os.system('gcloud compute firewall-rules create ingressrule --allow tcp:30000-33000,tcp:8000-11000,tcp:443,tcp:80,tcp:24226 --direction INGRESS') +os.system('gcloud compute firewall-rules create egressrule --allow tcp:30000-33000,tcp:8000-11000,tcp:443,tcp:80 --direction EGRESS') + +# table of hostnames and external ip's is requested +nodeinfo_list = os.popen("gcloud compute instances list --format='table(NAME,EXTERNAL_IP)'").read().split() + + +# list of dictionaries for each gke cluster node is made +nodeinfo_list = [{'name' : nodeinfo_list[i],'ip' : nodeinfo_list[i+1]} for i in range(0, len(nodeinfo_list), 1) if 'gke' in nodeinfo_list[i]] + +# label nodes 1 to 6 +for id in range(1, len(nodeinfo_list)+1): + os.system('kubectl label nodes ' + nodeinfo_list[id-1]['name'] + ' nodeid=' + str(id)) + +# Get working directory +directory = os.getcwd() + +# Download Istio if necessary +if os.path.isdir((os.getcwd()[:-4]) + 'istio-1.9.2') == False: + os.system('curl -L https://istio.io/downloadIstio | ISTIO_VERSION=1.9.2 TARGET_ARCH=x86_64 sh -') + os.system('cd istio-1.9.2') + os.system('export PATH=$PWD/bin:$PATH') + os.system('cd') + +# install istio +os.system('istioctl install --set profile=demo -y') + +# annotate default namespace for sidecar injection +os.system('kubectl label namespace default istio-injection=enabled') + +# deploy teastore microservices and label them as teastore +os.system('kubectl apply -f Infrastructure/Teastore/teastore-clusterip.yaml') +os.system('kubectl label deploy --all app=teastore -n default') + +# create ingressgateway for service mesh +os.system('kubectl apply -f Infrastructure/Istio/istioGateway.yml') + +# install litmus operator +os.system('kubectl apply -f https://litmuschaos.github.io/litmus/litmus-operator-v1.13.6.yaml') + +# install litmus experiments +os.system('kubectl apply -f https://hub.litmuschaos.io/api/chaos/1.13.7?file=charts/generic/experiments.yaml -n default') + +# deploy litmus admin service account +os.system('kubectl apply -f Infrastructure/Litmus/adminrbac.yml -n default') + +# deploy litmus chaos exporter +os.system('kubectl apply -f Infrastructure/Litmus/chaos-exporter.yml -n litmus') + +# create monitoring namespace +os.system('kubectl create ns monitoring') + +# deploy skydive analyzer and agents +os.system('kubectl apply -f Infrastructure/Skydive/skydive_agents_analyzer.yml') + +# ip change in prom_skydive_con.yml to existing k8s cluster node ip (in 2 places) +file_path = 'Infrastructure/Skydive/prom_skydive_con.yml' +with open(file_path, 'r') as f: + deployment_dict = yaml.safe_load_all(f) + deployment_dict = list(deployment_dict) + deployment_dict[0]['spec']['template']['spec']['containers'][0]['env'][0]['value'] = str(nodeinfo_list[0]['ip']) + ":32505" + deployment_dict[0]['spec']['template']['spec']['containers'][0]['env'][2]['value'] = "ws://" + str(nodeinfo_list[0]['ip']) + ":32505/ws/subscriber/flow" + +with open(file_path, 'w') as f: + yaml.safe_dump_all(deployment_dict, f, default_flow_style=False) + +# deploy prometheus_skydive_connector +os.system('kubectl apply -f Infrastructure/Skydive/prom_skydive_con.yml -n monitoring') + +# run skydive gremlin query !!!TODO +#os.system('sudo docker run --net=host -e SKYDIVE_ANALYZERS=35.246.242.101:32505 skydive/skydive client capture create --type pcap --gremlin "G.V().Has(\'Type\', \'device\', \'Name\', \'eth0\')"') + +# deploy kube-state-metric +os.system('kubectl apply -f Infrastructure/Kubernetes/kube-state-metrics-configs/') + +# change ip in prometheus config file + +#change ip prom config for skydive connector and litmus export to right values. +testyaml = ruamel.yaml.YAML() +with open('Infrastructure/Prometheus/kubernetes-prometheus/config-map.yaml', 'r') as read_file: + prom_config = testyaml.load_all(read_file) + + prom_config = list(prom_config) + #print(prom_config[0]['data']['prometheus.yml']) + prom_config[0]['data']['prometheus.yml'] = prom_config[0]['data']['prometheus.yml'].replace('skydive_ip_port', str(nodeinfo_list[0]['ip']) + ':32222') + prom_config[0]['data']['prometheus.yml'] = prom_config[0]['data']['prometheus.yml'].replace('litmus_ip_port', str(nodeinfo_list[3]['ip']) + ':31111') + + +with open('Infrastructure/Prometheus/kubernetes-prometheus/config-map.yaml', 'w') as created_file: + testyaml.default_flow_style = False + testyaml.dump(prom_config[0], created_file) + +# deploy prometheus +os.system('kubectl apply -f Infrastructure/Prometheus/kubernetes-prometheus/ -n monitoring') + +# deploy prometheus node exporters +os.system('kubectl apply -f Infrastructure/Prometheus/kubernetes-node-exporter/ -n monitoring') + +# Function for defining unique parameters +# define the experiment parameters based on specific experiments +def define_exp_parameters(experiment, exp_doc, node_list): + + deployment_list = [] + # Node cpu hog parameters + if ('cpu' in experiment) and ('node' in experiment): + + for node in node_list: + for duration in range(50, 81, 1): + for cpu_cores in range(2, 5): + + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][0]['value'] = str(duration) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][1]['value'] = str(cpu_cores) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][2]['value'] = sq(node) + deployment_list.append(copy.deepcopy(exp_doc)) + + # Pod cpu hog parameters + elif ('cpu' in experiment) and ('pod' in experiment): + + for duration in range(50, 81, 1): + for cpu_cores in range(2, 5): + + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][0]['value'] = str(cpu_cores) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][1]['value'] = str(duration) + deployment_list.append(copy.deepcopy(exp_doc)) + + # Node memory hog parameters + elif ('mem' in experiment) and ('node' in experiment): + + for node in node_list: + for duration in range(50, 81, 1): + for ram_perc in range(20, 41): + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][0]['value'] = str(duration) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][1]['value'] = str(ram_perc) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][2]['value'] = sq(node) + deployment_list.append(copy.deepcopy(exp_doc)) + + # Pod memory hog parameters + elif ('mem' in experiment) and ('pod' in experiment): + + for duration in range(50, 81, 1): + for ram in range(1500, 3000, 50): + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][0]['value'] = str(ram) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][1]['value'] = str(duration) + + deployment_list.append(copy.deepcopy(exp_doc)) + + # Node io stress parameters + elif ('iostress' in experiment) and ('node' in experiment): + + for node in node_list: + for duration in range(50, 81, 1): + for fsu_perc in range(20, 41): + for nr_workers in range(3, 6): + for nr_cpu in [1, 2]: + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][0]['value'] = str(duration) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][1]['value'] = str(fsu_perc) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][2]['value'] = str(nr_cpu) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][3]['value'] = str(nr_workers) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][4]['value'] = sq(node) + deployment_list.append(copy.deepcopy(exp_doc)) + + # Pod io stress parameters + elif ('iostress' in experiment) and ('pod' in experiment): + + for duration in range(50, 81, 1): + for fsu_perc in range(20, 41): + for nr_workers in range(3, 6): + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][0]['value'] = str(duration) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][1]['value'] = str(fsu_perc) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][2]['value'] = str(nr_workers) + deployment_list.append(copy.deepcopy(exp_doc)) + + # Pod delete parameters + elif ('del' in experiment): + + for duration in range(12, 20, 1): + for interval in range(3, 7): + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][0]['value'] = str(interval) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][1]['value'] = str(duration) + + deployment_list.append(copy.deepcopy(exp_doc)) + + # Network latency parameters + elif ('netlat' in experiment): + + for duration in range(50, 81, 1): + for latency in range(1600, 2800, 50): + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][0]['value'] = sq(exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][0]['value']) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][1]['value'] = str(latency) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][2]['value'] = str(duration) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][3]['value'] = sq(exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][3]['value']) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][4]['value'] = sq(exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][4]['value']) + + deployment_list.append(copy.deepcopy(exp_doc)) + + # Network loss parameters + elif 'netloss' in experiment: + + for duration in range(50, 81, 1): + for packet_loss_perc in range(80, 101): + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][0]['value'] = sq(exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][0]['value']) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][1]['value'] = str(packet_loss_perc) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][2]['value'] = str(duration) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][3]['value'] = sq(exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][3]['value']) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][4]['value'] = sq(exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][4]['value']) + deployment_list.append(copy.deepcopy(exp_doc)) + + # Network corruption parameters + elif 'netcorr' in experiment: + for duration in range(50, 81, 1): + for packet_corr_perc in range(80, 101): + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][0]['value'] = sq(exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][0]['value']) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][1]['value'] = str(duration) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][2]['value'] = str(packet_corr_perc) + + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][3]['value'] = sq(exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][3]['value']) + exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][4]['value'] = sq(exp_doc[0]['spec']['experiments'][0]['spec']['components']['env'][4]['value']) + deployment_list.append(copy.deepcopy(exp_doc)) + + return deployment_list + + +# Loading template files, generating different parameters, and dumping them as seperate YAML deployment files. +#node related +template_dir = 'Experiments/templates' +base_dir = 'Experiments/' +experiment_list = os.listdir(template_dir) +node_list = [node['name'] for node in nodeinfo_list] +testyaml = ruamel.yaml.YAML() +for experiment in [experiment for experiment in experiment_list if 'disk' not in experiment]: + + with open(template_dir + "/" + experiment, 'r') as read_file: + deployments = testyaml.load_all(read_file) + deployments = list(deployments) + deployments = define_exp_parameters(experiment, deployments, node_list) + + + i = 1 + for deployment in deployments: + with open(base_dir + experiment.split('.')[0] + "/" + experiment.split('.')[0] + "_" + str(i) + ".yml", 'w') as created_file: + + testyaml.default_flow_style = False + + testyaml.dump(deployment[0], created_file) + + i += 1 \ No newline at end of file diff --git a/made/Experiments/README.txt b/made/Experiments/README.txt new file mode 100644 index 0000000..acd2ff8 --- /dev/null +++ b/made/Experiments/README.txt @@ -0,0 +1,3 @@ +Randomly running experiments using runExperiments.py: + +python3 runExperiments.py \ No newline at end of file diff --git a/made/Experiments/runExperiments.py b/made/Experiments/runExperiments.py new file mode 100644 index 0000000..7bae2d6 --- /dev/null +++ b/made/Experiments/runExperiments.py @@ -0,0 +1,69 @@ +import os +import sys +import time +import random + +#os.system('kubectl apply -f adminrbac.yml -n default') +experimentCategories = [] +#random.shuffle(experimentCategories) + +#print(experimentCategories) +i=0 +limit = 10000 +while i < limit: + + + if not experimentCategories: + + + experimentCategories = [x[0] for x in os.walk(os.path.dirname(os.path.realpath(__file__))) if 'templates' not in x[0] if 'del' not in x[0]][1:] + print(experimentCategories) + #print(experimentCategories) + + #experimentCategories = [x[0] for x in os.walk(r'D:\Master thesis\Experimental procedure\LitmusExperiments\testex')][1:] + + i=i+1 + print("Iteration: " + str(i)) + + if i == limit: + break + + + + choiceExperiment = random.randint(0, len(experimentCategories)-1) + + #print(choiceExperiment) + print(experimentCategories[choiceExperiment]) + + + experiments = os.listdir(experimentCategories[choiceExperiment]) + + + + + + choiceExperimentConfig = random.randint(0, len(experiments)-1) + + #experimentConfig = experimentCategories[choiceExperiment] + "//" + experiments[choiceExperimentConfig] + experimentConfig = os.path.join(experimentCategories[choiceExperiment], experiments[choiceExperimentConfig]) + #print(experimentConfig) + + + + del experimentCategories[choiceExperiment] + + print("Choice: " + experiments[choiceExperimentConfig]) + + os.system('kubectl apply -f ' + experimentConfig + ' -n default') + + sleepTime = random.randint(90, 110) + + time.sleep(sleepTime) + + #print(i) + #os.system('kubectl apply -f pod-memhog.yml -n default') + #i=i+1 + #time.sleep(100) + #os.system('kubectl apply -f pod-cpuhog.yml -n default') + +print('Done') \ No newline at end of file diff --git a/made/Experiments/templates/node-cpuhog.yml b/made/Experiments/templates/node-cpuhog.yml new file mode 100644 index 0000000..437ce93 --- /dev/null +++ b/made/Experiments/templates/node-cpuhog.yml @@ -0,0 +1,42 @@ +apiVersion: litmuschaos.io/v1alpha1 +kind: ChaosEngine +metadata: + name: teastore-chaos + namespace: default +spec: + appinfo: + appns: "default" + applabel: "app=teastore" + # It can be true/false + annotationCheck: 'false' + # It can be active/stop + engineState: 'active' + #ex. values: ns1:name=percona,ns2:run=nginx + auxiliaryAppInfo: '' + chaosServiceAccount: litmus-admin +# monitoring: 'false' + components: + runner: + runnerAnnotations: + sidecar.istio.io/inject: 'false' + # It can be delete/retain + jobCleanUpPolicy: 'delete' + experiments: + - name: node-cpu-hog + spec: + components: + experimentAnnotations: + sidecar.istio.io/inject: 'false' + env: + # set chaos duration (in sec) as desired + - name: TOTAL_CHAOS_DURATION + value: '60' + + ## Specify the size as percent of total node capacity Ex: '30' + ## Note: For consuming memory in mebibytes change the variable to MEMORY_CONSUMPTION_MEBIBYTES + - name: NODE_CPU_CORE + value: '2' + + # ENTER THE COMMA SEPARATED TARGET NODES NAME + - name: TARGET_NODES + value: 'gke-testcluster-default-pool-01550e5a-2h29' \ No newline at end of file diff --git a/made/Experiments/templates/node-iostress.yml b/made/Experiments/templates/node-iostress.yml new file mode 100644 index 0000000..4d46c7f --- /dev/null +++ b/made/Experiments/templates/node-iostress.yml @@ -0,0 +1,45 @@ +apiVersion: litmuschaos.io/v1alpha1 +kind: ChaosEngine +metadata: + name: teastore-chaos + namespace: default +spec: + # It can be true/false + annotationCheck: 'false' + # It can be active/stop + engineState: 'active' + #ex. values: ns1:name=percona,ns2:run=nginx + auxiliaryAppInfo: '' + chaosServiceAccount: litmus-admin + components: + runner: + runnerAnnotations: + sidecar.istio.io/inject: 'false' + # It can be delete/retain + jobCleanUpPolicy: 'delete' + experiments: + - name: node-io-stress + spec: + components: + experimentAnnotations: + sidecar.istio.io/inject: 'false' + env: + # set chaos duration (in sec) as desired + - name: TOTAL_CHAOS_DURATION + value: '120' + + ## specify the size as percentage of free space on the file system + - name: FILESYSTEM_UTILIZATION_PERCENTAGE + value: '50' + + ## Number of core of CPU + - name: CPU + value: '1' + + ## Total number of workers default value is 4 + - name: NUMBER_OF_WORKERS + value: '5' + + ## enter the comma separated target nodes name + - name: TARGET_NODES + value: 'gke-testcluster-default-pool-01550e5a-5237' \ No newline at end of file diff --git a/made/Experiments/templates/node-memhog.yml b/made/Experiments/templates/node-memhog.yml new file mode 100644 index 0000000..2db0aba --- /dev/null +++ b/made/Experiments/templates/node-memhog.yml @@ -0,0 +1,42 @@ +apiVersion: litmuschaos.io/v1alpha1 +kind: ChaosEngine +metadata: + name: teastore-chaos + namespace: default +spec: + appinfo: + appns: "default" + applabel: "app=teastore" + # It can be true/false + annotationCheck: 'false' + # It can be active/stop + engineState: 'active' + #ex. values: ns1:name=percona,ns2:run=nginx + auxiliaryAppInfo: '' + chaosServiceAccount: litmus-admin +# monitoring: 'false' + components: + runner: + runnerAnnotations: + sidecar.istio.io/inject: 'false' + # It can be delete/retain + jobCleanUpPolicy: 'delete' + experiments: + - name: node-memory-hog + spec: + components: + experimentAnnotations: + sidecar.istio.io/inject: 'false' + env: + # set chaos duration (in sec) as desired + - name: TOTAL_CHAOS_DURATION + value: '80' + + ## Specify the size as percent of total node capacity Ex: '30' + ## Note: For consuming memory in mebibytes change the variable to MEMORY_CONSUMPTION_MEBIBYTES + - name: MEMORY_CONSUMPTION_PERCENTAGE + value: '80' + + # ENTER THE COMMA SEPARATED TARGET NODES NAME + - name: TARGET_NODES + value: 'gke-testcluster-default-pool-01550e5a-9rnt' \ No newline at end of file diff --git a/made/Experiments/templates/pod-cpuhog.yml b/made/Experiments/templates/pod-cpuhog.yml new file mode 100644 index 0000000..d9bafc2 --- /dev/null +++ b/made/Experiments/templates/pod-cpuhog.yml @@ -0,0 +1,36 @@ +apiVersion: litmuschaos.io/v1alpha1 +kind: ChaosEngine +metadata: + name: teastore-chaos + namespace: default +spec: + # It can be true/false + annotationCheck: 'false' + # It can be active/stop + engineState: 'active' + appinfo: + appns: 'default' + applabel: 'app=teastore' + appkind: 'deployment' + chaosServiceAccount: litmus-admin +# monitoring: 'false' + components: + runner: + runnerAnnotations: + sidecar.istio.io/inject: 'false' + # It can be delete/retain + jobCleanUpPolicy: 'delete' + experiments: + - name: pod-cpu-hog + spec: + components: + experimentAnnotations: + sidecar.istio.io/inject: 'false' + env: + # Enter the amount of memory in megabytes to be consumed by the application pod + - name: CPU_CORES + value: '2' + + - name: TOTAL_CHAOS_DURATION + value: '60' # in seconds + \ No newline at end of file diff --git a/made/Experiments/templates/pod-del.yml b/made/Experiments/templates/pod-del.yml new file mode 100644 index 0000000..e7fd43e --- /dev/null +++ b/made/Experiments/templates/pod-del.yml @@ -0,0 +1,39 @@ +apiVersion: litmuschaos.io/v1alpha1 +kind: ChaosEngine +metadata: + name: teastore-chaos + namespace: default +spec: + # It can be true/false + annotationCheck: 'false' + # It can be active/stop + engineState: 'active' + appinfo: + appns: 'default' + applabel: 'app=teastore' + appkind: 'deployment' + chaosServiceAccount: litmus-admin +# monitoring: 'false' + components: + runner: + runnerAnnotations: + sidecar.istio.io/inject: 'false' + # It can be delete/retain + jobCleanUpPolicy: 'delete' + experiments: + - name: pod-delete + spec: + components: + experimentAnnotations: + sidecar.istio.io/inject: 'false' + env: + # set chaos interval (in sec) as desired + - name: CHAOS_INTERVAL + value: '10' + + - name: TOTAL_CHAOS_DURATION + value: '15' # in seconds + + # pod failures without '--force' & default terminationGracePeriodSeconds + - name: FORCE + value: 'false' \ No newline at end of file diff --git a/made/Experiments/templates/pod-iostress.yml b/made/Experiments/templates/pod-iostress.yml new file mode 100644 index 0000000..6d6df5e --- /dev/null +++ b/made/Experiments/templates/pod-iostress.yml @@ -0,0 +1,37 @@ +apiVersion: litmuschaos.io/v1alpha1 +kind: ChaosEngine +metadata: + name: teastore-chaos + namespace: default +spec: + appinfo: + appns: 'default' + applabel: 'app=teastore' + appkind: 'deployment' + # It can be delete/retain + jobCleanUpPolicy: 'delete' + components: + runner: + runnerAnnotations: + sidecar.istio.io/inject: 'false' + # It can be active/stop + engineState: 'active' + chaosServiceAccount: litmus-admin + experiments: + - name: pod-io-stress + spec: + components: + experimentAnnotations: + sidecar.istio.io/inject: 'false' + env: + # set chaos duration (in sec) as desired + - name: TOTAL_CHAOS_DURATION + value: '120' + + ## specify the size as percentage of free space on the file system + - name: FILESYSTEM_UTILIZATION_PERCENTAGE + value: '10' + + ## Total number of workers default value is 4 + - name: NUMBER_OF_WORKERS + value: '5' diff --git a/made/Experiments/templates/pod-memhog.yml b/made/Experiments/templates/pod-memhog.yml new file mode 100644 index 0000000..cbafcc9 --- /dev/null +++ b/made/Experiments/templates/pod-memhog.yml @@ -0,0 +1,35 @@ +apiVersion: litmuschaos.io/v1alpha1 +kind: ChaosEngine +metadata: + name: teastore-chaos + namespace: default +spec: + # It can be true/false + annotationCheck: 'false' + # It can be active/stop + engineState: 'active' + appinfo: + appns: 'default' + applabel: 'app=teastore' + appkind: 'deployment' + chaosServiceAccount: litmus-admin +# monitoring: 'false' + components: + runner: + runnerAnnotations: + sidecar.istio.io/inject: 'false' + # It can be delete/retain + jobCleanUpPolicy: 'delete' + experiments: + - name: pod-memory-hog + spec: + components: + experimentAnnotations: + sidecar.istio.io/inject: 'false' + env: + # Enter the amount of memory in megabytes to be consumed by the application pod + - name: MEMORY_CONSUMPTION + value: '1400' + + - name: TOTAL_CHAOS_DURATION + value: '60' # in seconds diff --git a/made/Experiments/templates/pod-netcorr.yml b/made/Experiments/templates/pod-netcorr.yml new file mode 100644 index 0000000..e2fca65 --- /dev/null +++ b/made/Experiments/templates/pod-netcorr.yml @@ -0,0 +1,49 @@ +apiVersion: litmuschaos.io/v1alpha1 +kind: ChaosEngine +metadata: + name: teastore-chaos + namespace: default +spec: + # It can be delete/retain + jobCleanUpPolicy: 'delete' + # It can be true/false + annotationCheck: 'false' + # It can be active/stop + engineState: 'active' + appinfo: + appns: 'default' + # FYI, To see app label, apply kubectl get pods --show-labels + applabel: 'app=teastore' + appkind: 'deployment' + chaosServiceAccount: litmus-admin + components: + runner: + runnerAnnotations: + sidecar.istio.io/inject: 'false' + experiments: + - name: pod-network-corruption + spec: + components: + + experimentAnnotations: + sidecar.istio.io/inject: 'false' + env: + #Network interface inside target container + - name: NETWORK_INTERFACE + value: 'eth0' + + - name: TOTAL_CHAOS_DURATION + value: '60' # in seconds + + - name: NETWORK_PACKET_CORRUPTION_PERCENTAGE + value: '80' + + # provide the name of container runtime + # for litmus LIB, it supports docker, containerd, crio + # for pumba LIB, it supports docker only + - name: CONTAINER_RUNTIME + value: 'docker' + + # provide the socket file path + - name: SOCKET_PATH + value: '/var/run/docker.sock' \ No newline at end of file diff --git a/made/Experiments/templates/pod-netlat.yml b/made/Experiments/templates/pod-netlat.yml new file mode 100644 index 0000000..899c761 --- /dev/null +++ b/made/Experiments/templates/pod-netlat.yml @@ -0,0 +1,48 @@ +apiVersion: litmuschaos.io/v1alpha1 +kind: ChaosEngine +metadata: + name: teastore-chaos + namespace: default +spec: + # It can be delete/retain + jobCleanUpPolicy: 'delete' + # It can be true/false + annotationCheck: 'false' + # It can be active/stop + engineState: 'active' + appinfo: + appns: 'default' + # FYI, To see app label, apply kubectl get pods --show-labels + applabel: 'app=teastore' + appkind: 'deployment' + chaosServiceAccount: litmus-admin + components: + runner: + runnerAnnotations: + sidecar.istio.io/inject: 'false' + experiments: + - name: pod-network-latency + spec: + components: + experimentAnnotations: + sidecar.istio.io/inject: 'false' + env: + #Network interface inside target container + - name: NETWORK_INTERFACE + value: 'eth0' + + - name: NETWORK_LATENCY + value: '2000' + + - name: TOTAL_CHAOS_DURATION + value: '60' # in seconds + + # provide the name of container runtime + # for litmus LIB, it supports docker, containerd, crio + # for pumba LIB, it supports docker only + - name: CONTAINER_RUNTIME + value: 'docker' + + # provide the socket file path + - name: SOCKET_PATH + value: '/var/run/docker.sock' \ No newline at end of file diff --git a/made/Experiments/templates/pod-netloss.yml b/made/Experiments/templates/pod-netloss.yml new file mode 100644 index 0000000..2a4f2b8 --- /dev/null +++ b/made/Experiments/templates/pod-netloss.yml @@ -0,0 +1,49 @@ +apiVersion: litmuschaos.io/v1alpha1 +kind: ChaosEngine +metadata: + name: teastore-chaos + namespace: default +spec: + # It can be delete/retain + jobCleanUpPolicy: 'delete' + # It can be true/false + annotationCheck: 'false' + # It can be active/stop + engineState: 'active' + appinfo: + appns: 'default' + # FYI, To see app label, apply kubectl get pods --show-labels + applabel: 'app=teastore' + appkind: 'deployment' + chaosServiceAccount: litmus-admin + components: + runner: + runnerAnnotations: + sidecar.istio.io/inject: 'false' + experiments: + - name: pod-network-loss + spec: + components: + experimentAnnotations: + sidecar.istio.io/inject: 'false' + env: + + #Network interface inside target container + - name: NETWORK_INTERFACE + value: 'eth0' + + - name: NETWORK_PACKET_LOSS_PERCENTAGE + value: '100' + + - name: TOTAL_CHAOS_DURATION + value: '60' # in seconds + + # provide the name of container runtime + # for litmus LIB, it supports docker, containerd, crio + # for pumba LIB, it supports docker only + - name: CONTAINER_RUNTIME + value: 'containerd' + + # provide the socket file path + - name: SOCKET_PATH + value: '/run/containerd/containerd.sock' \ No newline at end of file diff --git a/made/Infrastructure/Istio/istioGateway.yml b/made/Infrastructure/Istio/istioGateway.yml new file mode 100644 index 0000000..98bcd84 --- /dev/null +++ b/made/Infrastructure/Istio/istioGateway.yml @@ -0,0 +1,35 @@ +apiVersion: networking.istio.io/v1alpha3 +kind: Gateway +metadata: + name: teastore-gateway +spec: + selector: + istio: ingressgateway # use istio default controller + servers: + - port: + number: 80 + name: TCP + protocol: TCP + hosts: + - "*" +--- +apiVersion: networking.istio.io/v1alpha3 +kind: VirtualService +metadata: + name: teastorevs +spec: + hosts: + - "*" + gateways: + - teastore-gateway + http: + - match: + - uri: + exact: /productpage + - uri: + prefix: /products + route: + - destination: + host: teastore-webui + port: + number: 8080 \ No newline at end of file diff --git a/made/Infrastructure/Kubernetes/kube-state-metrics-configs/README.md b/made/Infrastructure/Kubernetes/kube-state-metrics-configs/README.md new file mode 100644 index 0000000..4e3152e --- /dev/null +++ b/made/Infrastructure/Kubernetes/kube-state-metrics-configs/README.md @@ -0,0 +1,2 @@ +# kube-state-metrics-configs +Kube state metrics kubernetes deployment configs diff --git a/made/Infrastructure/Kubernetes/kube-state-metrics-configs/cluster-role-binding.yaml b/made/Infrastructure/Kubernetes/kube-state-metrics-configs/cluster-role-binding.yaml new file mode 100644 index 0000000..b5534ca --- /dev/null +++ b/made/Infrastructure/Kubernetes/kube-state-metrics-configs/cluster-role-binding.yaml @@ -0,0 +1,15 @@ +apiVersion: rbac.authorization.k8s.io/v1 +kind: ClusterRoleBinding +metadata: + labels: + app.kubernetes.io/name: kube-state-metrics + app.kubernetes.io/version: v1.8.0 + name: kube-state-metrics +roleRef: + apiGroup: rbac.authorization.k8s.io + kind: ClusterRole + name: kube-state-metrics +subjects: +- kind: ServiceAccount + name: kube-state-metrics + namespace: kube-system diff --git a/made/Infrastructure/Kubernetes/kube-state-metrics-configs/cluster-role.yaml b/made/Infrastructure/Kubernetes/kube-state-metrics-configs/cluster-role.yaml new file mode 100644 index 0000000..930738d --- /dev/null +++ b/made/Infrastructure/Kubernetes/kube-state-metrics-configs/cluster-role.yaml @@ -0,0 +1,110 @@ +apiVersion: rbac.authorization.k8s.io/v1 +kind: ClusterRole +metadata: + labels: + app.kubernetes.io/name: kube-state-metrics + app.kubernetes.io/version: v1.8.0 + name: kube-state-metrics +rules: +- apiGroups: + - "" + resources: + - configmaps + - secrets + - nodes + - pods + - services + - resourcequotas + - replicationcontrollers + - limitranges + - persistentvolumeclaims + - persistentvolumes + - namespaces + - endpoints + verbs: + - list + - watch +- apiGroups: + - extensions + resources: + - daemonsets + - deployments + - replicasets + - ingresses + verbs: + - list + - watch +- apiGroups: + - apps + resources: + - statefulsets + - daemonsets + - deployments + - replicasets + verbs: + - list + - watch +- apiGroups: + - batch + resources: + - cronjobs + - jobs + verbs: + - list + - watch +- apiGroups: + - autoscaling + resources: + - horizontalpodautoscalers + verbs: + - list + - watch +- apiGroups: + - authentication.k8s.io + resources: + - tokenreviews + verbs: + - create +- apiGroups: + - authorization.k8s.io + resources: + - subjectaccessreviews + verbs: + - create +- apiGroups: + - policy + resources: + - poddisruptionbudgets + verbs: + - list + - watch +- apiGroups: + - certificates.k8s.io + resources: + - certificatesigningrequests + verbs: + - list + - watch +- apiGroups: + - storage.k8s.io + resources: + - storageclasses + - volumeattachments + verbs: + - list + - watch +- apiGroups: + - admissionregistration.k8s.io + resources: + - mutatingwebhookconfigurations + - validatingwebhookconfigurations + verbs: + - list + - watch +- apiGroups: + - networking.k8s.io + resources: + - networkpolicies + verbs: + - list + - watch diff --git a/made/Infrastructure/Kubernetes/kube-state-metrics-configs/deployment.yaml b/made/Infrastructure/Kubernetes/kube-state-metrics-configs/deployment.yaml new file mode 100644 index 0000000..a85d003 --- /dev/null +++ b/made/Infrastructure/Kubernetes/kube-state-metrics-configs/deployment.yaml @@ -0,0 +1,42 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + labels: + app.kubernetes.io/name: kube-state-metrics + app.kubernetes.io/version: v1.8.0 + name: kube-state-metrics + namespace: kube-system +spec: + replicas: 1 + selector: + matchLabels: + app.kubernetes.io/name: kube-state-metrics + template: + metadata: + labels: + app.kubernetes.io/name: kube-state-metrics + app.kubernetes.io/version: v1.8.0 + spec: + containers: + - image: quay.io/coreos/kube-state-metrics:v1.8.0 + livenessProbe: + httpGet: + path: /healthz + port: 8080 + initialDelaySeconds: 1 + timeoutSeconds: 5 + name: kube-state-metrics + ports: + - containerPort: 8080 + name: http-metrics + - containerPort: 8081 + name: telemetry + readinessProbe: + httpGet: + path: / + port: 8081 + initialDelaySeconds: 1 + timeoutSeconds: 5 + nodeSelector: + kubernetes.io/os: linux + serviceAccountName: kube-state-metrics diff --git a/made/Infrastructure/Kubernetes/kube-state-metrics-configs/service-account.yaml b/made/Infrastructure/Kubernetes/kube-state-metrics-configs/service-account.yaml new file mode 100644 index 0000000..b543ede --- /dev/null +++ b/made/Infrastructure/Kubernetes/kube-state-metrics-configs/service-account.yaml @@ -0,0 +1,8 @@ +apiVersion: v1 +kind: ServiceAccount +metadata: + labels: + app.kubernetes.io/name: kube-state-metrics + app.kubernetes.io/version: v1.8.0 + name: kube-state-metrics + namespace: kube-system diff --git a/made/Infrastructure/Kubernetes/kube-state-metrics-configs/service.yaml b/made/Infrastructure/Kubernetes/kube-state-metrics-configs/service.yaml new file mode 100644 index 0000000..0d951d8 --- /dev/null +++ b/made/Infrastructure/Kubernetes/kube-state-metrics-configs/service.yaml @@ -0,0 +1,19 @@ +apiVersion: v1 +kind: Service +metadata: + labels: + app.kubernetes.io/name: kube-state-metrics + app.kubernetes.io/version: v1.8.0 + name: kube-state-metrics + namespace: kube-system +spec: + clusterIP: None + ports: + - name: http-metrics + port: 8080 + targetPort: http-metrics + - name: telemetry + port: 8081 + targetPort: telemetry + selector: + app.kubernetes.io/name: kube-state-metrics diff --git a/made/Infrastructure/Litmus/adminrbac.yml b/made/Infrastructure/Litmus/adminrbac.yml new file mode 100644 index 0000000..afb0d85 --- /dev/null +++ b/made/Infrastructure/Litmus/adminrbac.yml @@ -0,0 +1,61 @@ +apiVersion: v1 +kind: ServiceAccount +metadata: + name: litmus-admin + namespace: default + labels: + name: litmus-admin +--- +# Source: openebs/templates/clusterrole.yaml +apiVersion: rbac.authorization.k8s.io/v1 +kind: ClusterRole +metadata: + name: litmus-admin + labels: + name: litmus-admin +rules: +- apiGroups: [""] + resources: ["pods","events","configmaps","secrets","services"] + verbs: ["create","delete","get","list","patch","update", "deletecollection"] +- apiGroups: [""] + resources: ["pods/exec","pods/log","pods/eviction","replicationcontrollers"] + verbs: ["get","list","create"] +- apiGroups: ["batch"] + resources: ["jobs"] + verbs: ["create","list","get","delete","deletecollection"] +- apiGroups: ["apps"] + resources: ["deployments","statefulsets"] + verbs: ["list","get","patch","update"] +- apiGroups: ["apps"] + resources: ["replicasets"] + verbs: ["list","get"] +- apiGroups: ["apps"] + resources: ["daemonsets"] + verbs: ["list","get","delete"] +- apiGroups: ["apps.openshift.io"] + resources: ["deploymentconfigs"] + verbs: ["list","get"] +- apiGroups: ["argoproj.io"] + resources: ["rollouts"] + verbs: ["list","get"] +- apiGroups: ["litmuschaos.io"] + resources: ["chaosengines","chaosexperiments","chaosresults"] + verbs: ["create","list","get","patch","update","delete"] +- apiGroups: [""] + resources: ["nodes"] + verbs: ["patch","get","list","update"] +--- +apiVersion: rbac.authorization.k8s.io/v1 +kind: ClusterRoleBinding +metadata: + name: litmus-admin + labels: + name: litmus-admin +roleRef: + apiGroup: rbac.authorization.k8s.io + kind: ClusterRole + name: litmus-admin +subjects: +- kind: ServiceAccount + name: litmus-admin + namespace: default \ No newline at end of file diff --git a/made/Infrastructure/Litmus/chaos-exporter.yml b/made/Infrastructure/Litmus/chaos-exporter.yml new file mode 100644 index 0000000..3bbe143 --- /dev/null +++ b/made/Infrastructure/Litmus/chaos-exporter.yml @@ -0,0 +1,45 @@ +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + labels: + app: chaos-monitor + name: chaos-monitor +spec: + replicas: 1 + selector: + matchLabels: + app: chaos-monitor + template: + metadata: + labels: + app: chaos-monitor + spec: + containers: + - image: shubh214/chaos-exporter:ci + imagePullPolicy: Always + name: chaos-exporter + env: + - name: WATCH_NAMESPACE + value: 'default' + - name: TSDB_SCRAPE_INTERVAL + value: '1s' + - name : INJECTION_TIME_FILTER + value: "disable" + serviceAccountName: litmus +--- +apiVersion: v1 +kind: Service +metadata: + labels: + app: chaos-monitor + name: chaos-monitor +spec: + ports: + - port: 8080 + protocol: TCP + targetPort: 8080 + nodePort: 31111 + selector: + app: chaos-monitor + type: NodePort diff --git a/made/Infrastructure/Prometheus/kubernetes-node-exporter/README.md b/made/Infrastructure/Prometheus/kubernetes-node-exporter/README.md new file mode 100644 index 0000000..1b93240 --- /dev/null +++ b/made/Infrastructure/Prometheus/kubernetes-node-exporter/README.md @@ -0,0 +1 @@ +# kubernetes-node-exporter \ No newline at end of file diff --git a/made/Infrastructure/Prometheus/kubernetes-node-exporter/daemonset.yaml b/made/Infrastructure/Prometheus/kubernetes-node-exporter/daemonset.yaml new file mode 100644 index 0000000..052d74d --- /dev/null +++ b/made/Infrastructure/Prometheus/kubernetes-node-exporter/daemonset.yaml @@ -0,0 +1,55 @@ +apiVersion: apps/v1 +kind: DaemonSet +metadata: + labels: + app.kubernetes.io/component: exporter + app.kubernetes.io/name: node-exporter + name: node-exporter + namespace: monitoring +spec: + selector: + matchLabels: + app.kubernetes.io/component: exporter + app.kubernetes.io/name: node-exporter + template: + metadata: + labels: + app.kubernetes.io/component: exporter + app.kubernetes.io/name: node-exporter + spec: + containers: + - args: + - --path.sysfs=/host/sys + - --path.rootfs=/host/root + - --no-collector.wifi + - --no-collector.hwmon + - --collector.filesystem.ignored-mount-points=^/(dev|proc|sys|var/lib/docker/.+|var/lib/kubelet/pods/.+)($|/) + - --collector.netclass.ignored-devices=^(veth.*)$ + name: node-exporter + image: prom/node-exporter + ports: + - containerPort: 9100 + protocol: TCP + resources: + limits: + cpu: 250m + memory: 180Mi + requests: + cpu: 102m + memory: 180Mi + volumeMounts: + - mountPath: /host/sys + mountPropagation: HostToContainer + name: sys + readOnly: true + - mountPath: /host/root + mountPropagation: HostToContainer + name: root + readOnly: true + volumes: + - hostPath: + path: /sys + name: sys + - hostPath: + path: / + name: root diff --git a/made/Infrastructure/Prometheus/kubernetes-node-exporter/service.yaml b/made/Infrastructure/Prometheus/kubernetes-node-exporter/service.yaml new file mode 100644 index 0000000..f29d3e3 --- /dev/null +++ b/made/Infrastructure/Prometheus/kubernetes-node-exporter/service.yaml @@ -0,0 +1,18 @@ +--- +kind: Service +apiVersion: v1 +metadata: + name: node-exporter + namespace: monitoring + annotations: + prometheus.io/scrape: 'true' + prometheus.io/port: '9100' +spec: + selector: + app.kubernetes.io/component: exporter + app.kubernetes.io/name: node-exporter + ports: + - name: node-exporter + protocol: TCP + port: 9100 + targetPort: 9100 \ No newline at end of file diff --git a/made/Infrastructure/Prometheus/kubernetes-prometheus/README.md b/made/Infrastructure/Prometheus/kubernetes-prometheus/README.md new file mode 100644 index 0000000..cb462d2 --- /dev/null +++ b/made/Infrastructure/Prometheus/kubernetes-prometheus/README.md @@ -0,0 +1,19 @@ +# kubernetes prometheus Setup + +Complete prometheus monitoring stack setup on Kubernetes. + +You can find the full tutorial from here https://devopscube.com/setup-prometheus-monitoring-on-kubernetes/ + +Idea of this repo to understand all the components involved in prometheus setup. + +# Other Manifest repos + +Kube State metrics manifests: https://github.com/bibinwilson/kube-state-metrics + +Alert manager Manifests: https://github.com/bibinwilson/kubernetes-alert-manager + +Grafana manifests: https://github.com/bibinwilson/kubernetes-grafana + +Node Exporter manifests: https://github.com/bibinwilson/kubernetes-node-exporter + + diff --git a/made/Infrastructure/Prometheus/kubernetes-prometheus/clusterRole.yaml b/made/Infrastructure/Prometheus/kubernetes-prometheus/clusterRole.yaml new file mode 100644 index 0000000..d666b69 --- /dev/null +++ b/made/Infrastructure/Prometheus/kubernetes-prometheus/clusterRole.yaml @@ -0,0 +1,33 @@ +apiVersion: rbac.authorization.k8s.io/v1 +kind: ClusterRole +metadata: + name: prometheus +rules: +- apiGroups: [""] + resources: + - nodes + - nodes/proxy + - services + - endpoints + - pods + verbs: ["get", "list", "watch"] +- apiGroups: + - extensions + resources: + - ingresses + verbs: ["get", "list", "watch"] +- nonResourceURLs: ["/metrics"] + verbs: ["get"] +--- +apiVersion: rbac.authorization.k8s.io/v1 +kind: ClusterRoleBinding +metadata: + name: prometheus +roleRef: + apiGroup: rbac.authorization.k8s.io + kind: ClusterRole + name: prometheus +subjects: +- kind: ServiceAccount + name: default + namespace: monitoring diff --git a/made/Infrastructure/Prometheus/kubernetes-prometheus/config-map.yaml b/made/Infrastructure/Prometheus/kubernetes-prometheus/config-map.yaml new file mode 100644 index 0000000..30ad988 --- /dev/null +++ b/made/Infrastructure/Prometheus/kubernetes-prometheus/config-map.yaml @@ -0,0 +1,183 @@ +apiVersion: v1 +kind: ConfigMap +metadata: + name: prometheus-server-conf + labels: + name: prometheus-server-conf + namespace: monitoring +data: + prometheus.rules: |- + groups: + - name: devopscube demo alert + rules: + - alert: High Pod Memory + expr: sum(container_memory_usage_bytes) > 1 + for: 1m + labels: + severity: slack + annotations: + summary: High Memory Usage + prometheus.yml: |- + global: + scrape_interval: 1s + evaluation_interval: 5s + rule_files: + - /etc/prometheus/prometheus.rules + alerting: + alertmanagers: + - scheme: http + static_configs: + - targets: + - "alertmanager.monitoring.svc:9093" + + scrape_configs: + + - job_name: 'skydive' + static_configs: + - targets: ['skydive_ip_port'] + + - job_name: 'litmuschaos' + + static_configs: + - targets: ['litmus_ip_port',] + + - job_name: 'istiod' + kubernetes_sd_configs: + - role: endpoints + namespaces: + names: + - istio-system + relabel_configs: + - source_labels: [__meta_kubernetes_service_name, __meta_kubernetes_endpoint_port_name] + action: keep + regex: istiod;http-monitoring + + - job_name: 'envoy-stats' + metrics_path: /stats/prometheus + kubernetes_sd_configs: + - role: pod + + relabel_configs: + - source_labels: [__meta_kubernetes_pod_container_port_name] + action: keep + regex: '.*-envoy-prom' + + - job_name: 'kubernetes-apiservers' + + kubernetes_sd_configs: + - role: endpoints + scheme: https + + tls_config: + ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt + bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token + + relabel_configs: + - source_labels: [__meta_kubernetes_namespace, __meta_kubernetes_service_name, __meta_kubernetes_endpoint_port_name] + action: keep + regex: default;kubernetes;https + + - job_name: 'kubernetes-nodes' + + scheme: https + + tls_config: + ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt + bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token + + kubernetes_sd_configs: + - role: node + + relabel_configs: + - action: labelmap + regex: __meta_kubernetes_node_label_(.+) + - target_label: __address__ + replacement: kubernetes.default.svc:443 + - source_labels: [__meta_kubernetes_node_name] + regex: (.+) + target_label: __metrics_path__ + replacement: /api/v1/nodes/${1}/proxy/metrics + + - job_name: 'kubernetes-pods' + + kubernetes_sd_configs: + - role: pod + + relabel_configs: + - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape] + action: keep + regex: true + - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path] + action: replace + target_label: __metrics_path__ + regex: (.+) + - source_labels: [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port] + action: replace + regex: ([^:]+)(?::\d+)?;(\d+) + replacement: $1:$2 + target_label: __address__ + - action: labelmap + regex: __meta_kubernetes_pod_label_(.+) + - source_labels: [__meta_kubernetes_namespace] + action: replace + target_label: kubernetes_namespace + - source_labels: [__meta_kubernetes_pod_name] + action: replace + target_label: kubernetes_pod_name + + - job_name: 'kube-state-metrics' + static_configs: + - targets: ['kube-state-metrics.kube-system.svc.cluster.local:8080'] + + - job_name: 'kubernetes-cadvisor' + + scheme: https + + tls_config: + ca_file: /var/run/secrets/kubernetes.io/serviceaccount/ca.crt + bearer_token_file: /var/run/secrets/kubernetes.io/serviceaccount/token + + kubernetes_sd_configs: + - role: node + + relabel_configs: + - action: labelmap + regex: __meta_kubernetes_node_label_(.+) + - target_label: __address__ + replacement: kubernetes.default.svc:443 + - source_labels: [__meta_kubernetes_node_name] + regex: (.+) + target_label: __metrics_path__ + replacement: /api/v1/nodes/${1}/proxy/metrics/cadvisor + + + - job_name: 'kubernetes-service-endpoints' + + kubernetes_sd_configs: + - role: endpoints + + relabel_configs: + - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scrape] + action: keep + regex: true + - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_scheme] + action: replace + target_label: __scheme__ + regex: (https?) + - source_labels: [__meta_kubernetes_service_annotation_prometheus_io_path] + action: replace + target_label: __metrics_path__ + regex: (.+) + - source_labels: [__address__, __meta_kubernetes_service_annotation_prometheus_io_port] + action: replace + target_label: __address__ + regex: ([^:]+)(?::\d+)?;(\d+) + replacement: $1:$2 + - action: labelmap + regex: __meta_kubernetes_service_label_(.+) + - source_labels: [__meta_kubernetes_namespace] + action: replace + target_label: kubernetes_namespace + - source_labels: [__meta_kubernetes_service_name] + action: replace + target_label: kubernetes_name diff --git a/made/Infrastructure/Prometheus/kubernetes-prometheus/prometheus-deployment.yaml b/made/Infrastructure/Prometheus/kubernetes-prometheus/prometheus-deployment.yaml new file mode 100644 index 0000000..2ccb852 --- /dev/null +++ b/made/Infrastructure/Prometheus/kubernetes-prometheus/prometheus-deployment.yaml @@ -0,0 +1,38 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: prometheus-deployment + namespace: monitoring + labels: + app: prometheus-server +spec: + replicas: 1 + selector: + matchLabels: + app: prometheus-server + template: + metadata: + labels: + app: prometheus-server + spec: + containers: + - name: prometheus + image: prom/prometheus + args: + - "--config.file=/etc/prometheus/prometheus.yml" + - "--storage.tsdb.path=/prometheus/" + ports: + - containerPort: 9090 + volumeMounts: + - name: prometheus-config-volume + mountPath: /etc/prometheus/ + - name: prometheus-storage-volume + mountPath: /prometheus/ + volumes: + - name: prometheus-config-volume + configMap: + defaultMode: 420 + name: prometheus-server-conf + + - name: prometheus-storage-volume + emptyDir: {} diff --git a/made/Infrastructure/Prometheus/kubernetes-prometheus/prometheus-ingress.yaml b/made/Infrastructure/Prometheus/kubernetes-prometheus/prometheus-ingress.yaml new file mode 100644 index 0000000..202f2e6 --- /dev/null +++ b/made/Infrastructure/Prometheus/kubernetes-prometheus/prometheus-ingress.yaml @@ -0,0 +1,33 @@ +## Nginx Ingress +## Follow https://devopscube.com/setup-ingress-kubernetes-nginx-controller/ + +apiVersion: extensions/v1beta1 +kind: Ingress +metadata: + name: prometheus-ui + namespace: monitoring + annotations: + kubernetes.io/ingress.class: nginx +spec: + rules: + # Use the host you used in your kubernetes Ingress Configurations + - host: prometheus.example.com + http: + paths: + - backend: + serviceName: prometheus-service + servicePort: 8080 + tls: + - hosts: + - prometheus.apps.shaker242.lab + secretName: prometheus-secret +--- +apiVersion: v1 +kind: Secret +metadata: + name: prometheus-secret + namespace: monitoring +data: +# USe base64 in the certs + tls.crt: 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 + tls.key: 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 diff --git a/made/Infrastructure/Prometheus/kubernetes-prometheus/prometheus-service.yaml b/made/Infrastructure/Prometheus/kubernetes-prometheus/prometheus-service.yaml new file mode 100644 index 0000000..420a586 --- /dev/null +++ b/made/Infrastructure/Prometheus/kubernetes-prometheus/prometheus-service.yaml @@ -0,0 +1,17 @@ +apiVersion: v1 +kind: Service +metadata: + name: prometheus-service + namespace: monitoring + annotations: + prometheus.io/scrape: 'true' + prometheus.io/port: '9090' + +spec: + selector: + app: prometheus-server + type: NodePort + ports: + - port: 8080 + targetPort: 9090 + nodePort: 30000 diff --git a/made/Infrastructure/Skydive/prom_skydive_con.yml b/made/Infrastructure/Skydive/prom_skydive_con.yml new file mode 100644 index 0000000..1d43ec3 --- /dev/null +++ b/made/Infrastructure/Skydive/prom_skydive_con.yml @@ -0,0 +1,54 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: skydive-prometheus-connector +spec: + replicas: 1 + selector: + matchLabels: + app: skydive-prometheus-connector + template: + metadata: + labels: + app: skydive-prometheus-connector + spec: + containers: + - env: + - name: SKYDIVE_ANALYZERS + value: 35.242.242.25:32505 + - name: SKYDIVE_LOGGING_LEVEL + value: INFO + - name: SKYDIVE_PIPELINE_SUBSCRIBER_URL + value: ws://35.242.242.25:32505/ws/subscriber/flow + - name: SKYDIVE_PIPELINE_MANGLE + value: none + - name: SKYDIVE_PIPELINE_TRANSFORM_TYPE + value: none + - name: SKYDIVE_PIPELINE_STORE_TYPE + value: prom_sky_con + - name: SKYDIVE_PIPELINE_STORE_PROM_SKY_CON_PORT + value: '9100' + image: sodaliteh2020/prometheus-skydive-connector:2 + imagePullPolicy: Always + name: skydive-prometheus-connector + ports: + - containerPort: 9100 + protocol: TCP +--- +apiVersion: v1 +kind: Service +metadata: + labels: + app: skydive-prometheus-connector + name: skydive-prometheus-connector +spec: + ports: + - name: api + nodePort: 32222 + port: 9100 + protocol: TCP + targetPort: 9100 + selector: + app: skydive-prometheus-connector + sessionAffinity: None + type: NodePort diff --git a/made/Infrastructure/Skydive/skydive_agents_analyzer.yml b/made/Infrastructure/Skydive/skydive_agents_analyzer.yml new file mode 100644 index 0000000..b40ea57 --- /dev/null +++ b/made/Infrastructure/Skydive/skydive_agents_analyzer.yml @@ -0,0 +1,213 @@ +apiVersion: v1 +kind: ServiceAccount +metadata: + name: skydive-service-account +--- +apiVersion: rbac.authorization.k8s.io/v1 +kind: ClusterRoleBinding +metadata: + name: skydive-cluster-role +roleRef: + apiGroup: rbac.authorization.k8s.io + kind: ClusterRole + name: cluster-admin +subjects: + - kind: ServiceAccount + name: skydive-service-account + namespace: default +--- +apiVersion: v1 +kind: Service +metadata: + name: skydive-analyzer + labels: + app: skydive-analyzer +spec: + type: NodePort + ports: + - port: 8082 + name: api + - port: 8082 + name: protobuf + protocol: UDP + nodePort: 32505 + - port: 12379 + name: etcd + - port: 12380 + name: etcd-cluster + - port: 9200 + name: es + selector: + app: skydive + tier: analyzer +--- +apiVersion: v1 +kind: ConfigMap +metadata: + labels: + app: skydive-analyzer + name: skydive-analyzer-config +data: + SKYDIVE_ANALYZER_FLOW_BACKEND: elasticsearch + SKYDIVE_ANALYZER_TOPOLOGY_BACKEND: elasticsearch + SKYDIVE_ANALYZER_TOPOLOGY_PROBES: k8s +--- +apiVersion: v1 +kind: ConfigMap +metadata: + labels: + app: skydive-agent + name: skydive-agent-config +data: + SKYDIVE_AGENT_TOPOLOGY_PROBES: runc docker lldp libvirt socketinfo + SKYDIVE_AGENT_LISTEN: 127.0.0.1:8081 +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: skydive-analyzer +spec: + replicas: 1 + selector: + matchLabels: + app: skydive + tier: analyzer + template: + metadata: + labels: + app: skydive + tier: analyzer + spec: + serviceAccountName: skydive-service-account + containers: + - name: skydive-analyzer + image: skydive/skydive:0.27.0 + args: + - analyzer + ports: + - containerPort: 8082 + - containerPort: 8082 + protocol: UDP + - containerPort: 12379 + - containerPort: 12380 + readinessProbe: + httpGet: + port: 8082 + path: /api/status + initialDelaySeconds: 20 + periodSeconds: 10 + livenessProbe: + httpGet: + port: 8082 + path: /api/status + initialDelaySeconds: 20 + periodSeconds: 10 + failureThreshold: 10 + env: + - name: SKYDIVE_ANALYZER_LISTEN + value: "0.0.0.0:$(SKYDIVE_ANALYZER_SERVICE_PORT_API)" + - name: SKYDIVE_ETCD_LISTEN + value: "0.0.0.0:$(SKYDIVE_ANALYZER_SERVICE_PORT_ETCD)" + envFrom: + - configMapRef: + name: skydive-analyzer-config + - name: skydive-elasticsearch + image: elasticsearch:7.8.0 + env: + - name: http.port + value: "9200" + - name: discovery.type + value: "single-node" + ports: + - containerPort: 9200 +--- +apiVersion: apps/v1 +kind: DaemonSet +metadata: + name: skydive-agent +spec: + selector: + matchLabels: + app: skydive + tier: agent + template: + metadata: + labels: + app: skydive + tier: agent + spec: + hostNetwork: true + hostPID: true + containers: + - name: skydive-agent + image: skydive/skydive:0.27.0 + args: + - agent + - --listen=0.0.0.0:8081 + ports: + - containerPort: 8081 + readinessProbe: + httpGet: + port: 8081 + path: /api/status + initialDelaySeconds: 20 + periodSeconds: 10 + livenessProbe: + httpGet: + port: 8081 + path: /api/status + initialDelaySeconds: 20 + periodSeconds: 10 + failureThreshold: 10 + env: + - name: SKYDIVE_ANALYZERS + value: "$(SKYDIVE_ANALYZER_SERVICE_HOST):$(SKYDIVE_ANALYZER_SERVICE_PORT_API)" + - name: SKYDIVE_AGENT_TOPOLOGY_RUNC_RUN_PATH + value: /var/run/runc /var/run/runc-ctrs /var/run/containerd/runc + envFrom: + - configMapRef: + name: skydive-agent-config + securityContext: + privileged: true + volumeMounts: + - name: docker + mountPath: /var/run/docker.sock + - name: run + mountPath: /host/run + - name: ovsdb + mountPath: /var/run/openvswitch/db.sock + - name: runc + mountPath: /var/run/runc + - name: runc-ctrs + mountPath: /var/run/runc-ctrs + - name: containerd-runc + mountPath: /var/run/containerd/runc + - name: data-kubelet + mountPath: /var/data/kubelet + - name: lib-kubelet + mountPath: /var/lib/kubelet + volumes: + - name: docker + hostPath: + path: /var/run/docker.sock + - name: run + hostPath: + path: /var/run/netns + - name: ovsdb + hostPath: + path: /var/run/openvswitch/db.sock + - name: runc + hostPath: + path: /var/run/runc + - name: runc-ctrs + hostPath: + path: /var/run/runc-ctrs + - name: containerd-runc + hostPath: + path: /var/run/containerd/runc + - name: data-kubelet + hostPath: + path: /var/data/kubelet + - name: lib-kubelet + hostPath: + path: /var/lib/kubelet diff --git a/made/Infrastructure/Teastore/teastore-clusterip.yaml b/made/Infrastructure/Teastore/teastore-clusterip.yaml new file mode 100644 index 0000000..4c5a94f --- /dev/null +++ b/made/Infrastructure/Teastore/teastore-clusterip.yaml @@ -0,0 +1,289 @@ +apiVersion: apps/v1 +kind: Deployment +metadata: + name: teastore-db +spec: + selector: + matchLabels: + app: teastore + run: teastore-db + template: + metadata: + labels: + app: teastore + run: teastore-db + spec: + containers: + - name: teastore-db + image: descartesresearch/teastore-db + ports: + - containerPort: 3306 + nodeSelector: + nodeid: "1" +--- +apiVersion: v1 +kind: Service +metadata: + name: teastore-db + labels: + app: teastore + run: teastore-db +spec: + ports: + - port: 3306 + protocol: TCP + selector: + run: teastore-db +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: teastore-registry +spec: + selector: + matchLabels: + app: teastore + run: teastore-registry + template: + metadata: + labels: + app: teastore + run: teastore-registry + spec: + containers: + - name: teastore-registry + image: descartesresearch/teastore-registry + ports: + - containerPort: 8080 + nodeSelector: + nodeid: "2" +--- +apiVersion: v1 +kind: Service +metadata: + name: teastore-registry + labels: + app: teastore + run: teastore-registry +spec: + ports: + - port: 8080 + protocol: TCP + selector: + run: teastore-registry +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: teastore-persistence +spec: + selector: + matchLabels: + app: teastore + run: teastore-persistence + template: + metadata: + labels: + app: teastore + run: teastore-persistence + spec: + containers: + - name: teastore-persistence + image: descartesresearch/teastore-persistence + ports: + - containerPort: 8080 + env: + - name: HOST_NAME + value: "teastore-persistence" + - name: REGISTRY_HOST + value: "teastore-registry" + - name: DB_HOST + value: "teastore-db" + - name: DB_PORT + value: "3306" + nodeSelector: + nodeid: "1" +--- +apiVersion: v1 +kind: Service +metadata: + name: teastore-persistence + labels: + app: teastore + run: teastore-persistence +spec: + ports: + - port: 8080 + protocol: TCP + selector: + run: teastore-persistence +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: teastore-auth +spec: + selector: + matchLabels: + app: teastore + run: teastore-auth + template: + metadata: + labels: + app: teastore + run: teastore-auth + spec: + containers: + - name: teastore-auth + image: descartesresearch/teastore-auth + ports: + - containerPort: 8080 + env: + - name: HOST_NAME + value: "teastore-auth" + - name: REGISTRY_HOST + value: "teastore-registry" + nodeSelector: + nodeid: "3" +--- +apiVersion: v1 +kind: Service +metadata: + name: teastore-auth + labels: + app: teastore + run: teastore-auth +spec: + ports: + - port: 8080 + protocol: TCP + selector: + run: teastore-auth +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: teastore-image +spec: + selector: + matchLabels: + app: teastore + run: teastore-image + template: + metadata: + labels: + app: teastore + run: teastore-image + spec: + containers: + - name: teastore-image + image: descartesresearch/teastore-image + ports: + - containerPort: 8080 + env: + - name: HOST_NAME + value: "teastore-image" + - name: REGISTRY_HOST + value: "teastore-registry" + nodeSelector: + nodeid: "4" +--- +apiVersion: v1 +kind: Service +metadata: + name: teastore-image + labels: + app: teastore + run: teastore-image +spec: + ports: + - port: 8080 + protocol: TCP + selector: + run: teastore-image +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: teastore-recommender +spec: + selector: + matchLabels: + app: teastore + run: teastore-recommender + template: + metadata: + labels: + app: teastore + run: teastore-recommender + spec: + containers: + - name: teastore-recommender + image: descartesresearch/teastore-recommender + ports: + - containerPort: 8080 + env: + - name: HOST_NAME + value: "teastore-recommender" + - name: REGISTRY_HOST + value: "teastore-registry" + nodeSelector: + nodeid: "5" +--- +apiVersion: v1 +kind: Service +metadata: + name: teastore-recommender + labels: + app: teastore + run: teastore-recommender +spec: + ports: + - port: 8080 + protocol: TCP + selector: + run: teastore-recommender +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: teastore-webui +spec: + selector: + matchLabels: + app: teastore + run: teastore-webui + template: + metadata: + labels: + app: teastore + run: teastore-webui + spec: + containers: + - name: teastore-webui + image: descartesresearch/teastore-webui + ports: + - containerPort: 8080 + env: + - name: HOST_NAME + value: "teastore-webui" + - name: REGISTRY_HOST + value: "teastore-registry" + nodeSelector: + nodeid: "6" +--- +apiVersion: v1 +kind: Service +metadata: + name: teastore-webui + labels: + app: teastore + run: teastore-webui +spec: + type: NodePort + ports: + - port: 8080 + nodePort: 30080 + protocol: TCP + selector: + run: teastore-webui diff --git a/made/Load Generation/README.txt b/made/Load Generation/README.txt new file mode 100644 index 0000000..335fa0d --- /dev/null +++ b/made/Load Generation/README.txt @@ -0,0 +1,13 @@ +LIMBO: https://github.com/joakimkistowski/HTTP-Load-Generator + +1. Change IP in teastore_browse to ingress gateway IP of service mesh. +2. On one machine (i.e., VM), load generator machine needs to be ran (running the java program): + +java -jar httploadgenerator.jar loadgenerator + +3. On another machine run the load director: + +java -jar httploadgenerator.jar director -s 34.142.76.38 -a intensity.csv -l teastore_browse.lua -o results.csv -t 50 + + +Load should be generated now. \ No newline at end of file diff --git a/made/Load Generation/httploadgenerator.jar b/made/Load Generation/httploadgenerator.jar new file mode 100644 index 0000000000000000000000000000000000000000..f57e3d4b32a4cb0bea0b8df048369968f927ac1e GIT binary patch literal 1658907 zcmagFV~}j^vbNi{?VfGhwr$(CZQGu0+jjSC+qP|gb2Ii@5$`!8qN+wj<&PQ}SK_I> zQ(g)f1Pb6kE@lH)+5f!x?>ET5pE4rK0yGk`qIB~AZ3Y3r^w*3I^OP+4@7KM5Unu|A zOh!OfLR3UanN~(rBw7G2kRCzgMaDBs*B{Jv9BwruzRl5!)H&HQCov=7^a?b_;pN_U z`zBx?R3r+yVMw~U(%V92a1aCiu$PzAdwSnDhy|C3JhZ@PsU|}oWA3T$m&U?0!`bLg z{B^=%rJw`E%az~Bgi~{E)fs`}90@{s&T;k-cru{6Loo`zuX2uJsJ)0F*wm#*3F?=s zrqLAk|2zc%z*cdq^xq%;zZb;cJ9`TwJ0ok0|MRT>VRc_ZHo6N40FVp}0D$v>GjLwRq2YZQt6a5bzvSGB{3BEAW!MtxfG=uC+bkAh}ghjY}s%Xa*J~p_umff$s2g_DFrRNnU5{| zm@H24blaQGCUp@>_ai+82%i 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Generation/teastore_browse.lua new file mode 100644 index 0000000..1e4c48e --- /dev/null +++ b/made/Load Generation/teastore_browse.lua @@ -0,0 +1,93 @@ +--[[ + teastore_browse profile. Sends cyclical requests to webui instance or front-end loadbalancer. + Browses store and updates shopping carts. + Does not perform any actions that would change the database. +--]] + +--[[ + Global Variables. Initialized at load driver startup. +--]] +prefix = "http://34.89.211.29:30080/tools.descartes.teastore.webui/" +productviewcount = 30 +postIndex = {3, 11} + + +--[[ + Gets called at the beginning of each "call cycle", perform as much work as possible here. + Initialize all global variables here. + Note that math.random is already initialized using a fixed seed (5) for reproducibility. +--]] +function onCycle() + userpostfix = 1 + math.random(90) + calls = { + "", + "login", + --[[[POST]--]]"loginAction?username=user"..userpostfix.."&password=password", + --[[[POST]--]]"category?page=1&category=", + "product?id=", + --[[[POST]--]]"cartAction?addToCart=&productid=", + "category?page=1&category=", + "category?page=", + --[[[POST]--]]"cartAction?addToCart=&productid=", + "profile", + --[[[POST]--]]"loginAction?logout=", + } +end + +--[[ + Gets called with ever increasing callnums for each http call until it returns nil. + Once it returns nil, onCycle() is called again and callnum is reset to 1 (Lua convention). + + Here, you can use our HTML helper functions for conditional calls on returned texts (usually HTML, thus the name). + We offer: + - html.getMatches( regex ) + Returns all lines in the returned text stream that match a provided regex. + - html.extractMatches( prefixRegex, postfixRegex ) + Returns all matches that are preceeded by a prefixRegex match and followed by a postfixRegex match. + The regexes must have one unique match for each line in which they apply. + - html.extractMatches( prefixRegex, matchingRegex, postfixRegex ) + Variant of extractMatches with a matching regex defining the string that is to be extracted. +--]] +function onCall(callnum) + if callnum == 2 then + local categoryids = html.extractMatches("href=.*category.*?category=","\\d+","&page=1.") + categoryid = categoryids[math.random(#categoryids)] + elseif callnum == 5 then + local productids = html.extractMatches("href=.*product.*?id=","\\d+",". >\\d+    .*") + page = math.random(pagecount) 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