Merge "Tools: Add monitoring analysis jupyter notebook"

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diff --git a/tools/lma/metrics/jupyter-notebooks/Analysis-Monitoring-K8S.ipynb b/tools/lma/metrics/jupyter-notebooks/Analysis-Monitoring-K8S.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Metrics Analysis Notebook (k8s)\n",
+    "\n",
+    "#### Used to analyse / visualize the metrics, data fetched from prometheus (monitoring cluster)\n",
+    "\n",
+    "### Contributor:    Aditya Srivastava <adityasrivastava301199@gmail.com>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "import numpy as np\n",
+    "\n",
+    "import datetime\n",
+    "import time\n",
+    "import requests\n",
+    "\n",
+    "from pprint import pprint\n",
+    "import json\n",
+    "from datetime import datetime"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PROMETHEUS = 'http://10.10.120.211:30902/' #do not change, unless sure"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Helper Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#function to make DF out of query json\n",
+    "\n",
+    "def convert_to_df(res_json):\n",
+    "\n",
+    "    data_list = res_json['data']['result']\n",
+    "    res_df = pd.DataFrame()\n",
+    "    if not data_list:\n",
+    "        return res_df\n",
+    "\n",
+    "    # making colums\n",
+    "    headers = data_list[0]\n",
+    "    for data in data_list:\n",
+    "        metrics = data['metric']\n",
+    "        for metric in metrics.keys():\n",
+    "            res_df[metric] = np.nan\n",
+    "        res_df['value'] = 0\n",
+    "    \n",
+    "    # filling the df\n",
+    "    for data in data_list:\n",
+    "        metrics = data['metric']\n",
+    "        metrics['value'] = data['value'][-1]\n",
+    "        res_df = res_df.append(metrics, ignore_index=True)      \n",
+    "\n",
+    "    return res_df\n",
+    "\n",
+    "def convert_to_df_range(res_json):\n",
+    "\n",
+    "    data_list = res_json['data']['result']\n",
+    "    res_df = pd.DataFrame()\n",
+    "    if not data_list:\n",
+    "        return res_df\n",
+    "\n",
+    "    # filling the df\n",
+    "    for data in data_list:\n",
+    "        metrics = data['metric']\n",
+    "        values = np.array(data['values'])\n",
+    "        for time, value in values:\n",
+    "            metrics['timestamp'] = time\n",
+    "            metrics['value'] = value\n",
+    "            res_df = res_df.append(metrics, ignore_index=True)      \n",
+    "\n",
+    "    return res_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# functions to query\n",
+    "\n",
+    "def convert_to_timestamp(s):\n",
+    "    return time.mktime(datetime.strptime(s, \"%Y-%m-%d %H:%M:%S\").timetuple())\n",
+    "\n",
+    "def query_current(params={}):\n",
+    "    # input: params\n",
+    "    # type: dict\n",
+    "    # Example: {'query': 'container_cpu_user_seconds_total'}\n",
+    "    \n",
+    "    # Output: dict, loaded json response of the query\n",
+    "\n",
+    "    res = requests.get(PROMETHEUS + '/api/v1/query', \n",
+    "                       params=params)\n",
+    "    return json.loads(res.text)\n",
+    "\n",
+    "\n",
+    "def query_range(start, end, params={}, steps = '30s'):\n",
+    "    # input: params\n",
+    "    # type: dict\n",
+    "    # Example: {'query': 'container_cpu_user_seconds_total'}\n",
+    "    \n",
+    "    # Output: dict, loaded json response of the query\n",
+    "    params[\"start\"] = convert_to_timestamp(start)\n",
+    "    params[\"end\"] = convert_to_timestamp(end)\n",
+    "    params[\"step\"] = steps\n",
+    "\n",
+    "    print(params)\n",
+    "     \n",
+    "    res = requests.get(PROMETHEUS + '/api/v1/query_range', \n",
+    "                       params=params,\n",
+    "                       )\n",
+    "\n",
+    "    return json.loads(res.text)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analysis Function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CPU Unused Cores\n",
+    "def unused_cores(start=None, end=None, node=None, steps='15s', csv=None, verbose=False):\n",
+    "    \n",
+    "    if csv is not None:\n",
+    "        df = pd.read_csv(csv)\n",
+    "        return df\n",
+    "    else:\n",
+    "        if start is None or end is None or node is None:\n",
+    "            return \"Start, end and Node name required when fetching from prometheus\"\n",
+    "        \n",
+    "        params = {'query' : \"collectd_cpu_percent{exported_instance='\" + node + \"'}\"}\n",
+    "\n",
+    "        target_cpu_usage_range = query_range(start, end, params, steps)\n",
+    "        df = convert_to_df_range(target_cpu_usage_range)\n",
+    "\n",
+    "        df = df.drop(['__name__', 'instance', 'job'], axis = 1)\n",
+    "        groups = df.groupby(['cpu'])\n",
+    "        if verbose: print(\"Unused Cores :\")\n",
+    "        unused_cores = []\n",
+    "        for key, item in groups:\n",
+    "            curr_df = item\n",
+    "            idle_row = curr_df.loc[curr_df['type'] == 'idle']\n",
+    "            if idle_row['value'].iloc[0] == '100':\n",
+    "                if verbose: print(\"Core:   \",key)\n",
+    "                unused_cores.append(int(key))\n",
+    "\n",
+    "        print(\"Number of unused cores:   \", len(unused_cores))\n",
+    "    return unused_cores\n",
+    "\n",
+    "\n",
+    "#CPU fully used cores\n",
+    "def fully_used_cores(start=None, end=None, node=None, steps='15s', csv=None, verbose=False):\n",
+    "    \n",
+    "    if csv is not None:\n",
+    "        df = pd.read_csv(csv)\n",
+    "        return df\n",
+    "    else:\n",
+    "        if start is None or end is None or node is None:\n",
+    "            return \"Start, end and Node name required when fetching from prometheus\"\n",
+    "        \n",
+    "        params = {'query' : \"collectd_cpu_percent{exported_instance='\" + node + \"'}\"}\n",
+    "\n",
+    "        target_cpu_usage_range = query_range(start, end, params, steps)\n",
+    "        df = convert_to_df_range(target_cpu_usage_range)\n",
+    "\n",
+    "        df = df.drop(['__name__', 'instance', 'job'], axis = 1)\n",
+    "        groups = df.groupby(['cpu'])\n",
+    "        if verbose: print(\"Fully Used Cores :\")\n",
+    "        fully_used_cores = []\n",
+    "        for key, item in groups:\n",
+    "            curr_df = item\n",
+    "            idle_row = curr_df.loc[curr_df['type'] == 'idle']\n",
+    "            if idle_row['value'].iloc[0] == '0':\n",
+    "                if verbose: print(\"Core:   \",key)\n",
+    "                fully_used_cores.append(int(key))\n",
+    "        print(\"Number of fully used cores:   \", len(fully_used_cores))\n",
+    "    return fully_used_cores\n",
+    "\n",
+    "\n",
+    "# CPU used cores plots\n",
+    "def plot_used_cores(start=None, end=None, node=None, steps='15s', csv=None, verbose=False):\n",
+    "    \n",
+    "    if csv is not None:\n",
+    "        df = pd.read_csv(csv)\n",
+    "        \n",
+    "        # \n",
+    "        df['rate'] = df['value'].diff()\n",
+    "\n",
+    "        fig = plt.figure(figsize=(24,6), facecolor='oldlace', edgecolor='red')\n",
+    "        ax1 = fig.add_subplot(111)\n",
+    "        ax1.title.set_text('CPU usage')\n",
+    "        ax1.plot(df['epoch'], df['rate'])\n",
+    "        return df\n",
+    "    else:\n",
+    "        if start is None or end is None or node is None:\n",
+    "            return \"Start, end and Node name required when fetching from prometheus\"\n",
+    "\n",
+    "        params = {'query' : \"collectd_cpu_percent{exported_instance='\" + node + \"'}\"}\n",
+    "\n",
+    "        target_cpu_usage_range = query_range(start, end, params, steps)\n",
+    "        df = convert_to_df_range(target_cpu_usage_range)\n",
+    "    \n",
+    "        df = df.drop(['__name__', 'instance', 'job'], axis = 1)\n",
+    "        groups = df.groupby(['cpu'])\n",
+    "        used_cores = []\n",
+    "\n",
+    "        for key, item in groups:\n",
+    "            curr_df = item\n",
+    "            idle_row = curr_df.loc[curr_df['type'] == 'idle']\n",
+    "\n",
+    "            if idle_row['value'].iloc[0] != '100':\n",
+    "                used_cores.append(key)\n",
+    "                type_grps = curr_df.groupby('type')\n",
+    "                fig = plt.figure(figsize=(24,6), facecolor='oldlace', edgecolor='red')\n",
+    "\n",
+    "                for type_key, new_item in type_grps:\n",
+    "\n",
+    "                    if type_key == 'system':\n",
+    "                        ax1 = fig.add_subplot(131)\n",
+    "                        ax1.title.set_text(type_key)\n",
+    "                        ax1.plot(new_item['timestamp'], new_item['value'])\n",
+    "                    elif type_key == 'user':\n",
+    "                        ax2 = fig.add_subplot(132)\n",
+    "                        ax2.title.set_text(type_key)\n",
+    "                        ax2.plot(new_item['timestamp'], new_item['value'])\n",
+    "                    elif type_key == 'wait':\n",
+    "                        ax3 = fig.add_subplot(133)\n",
+    "                        ax3.title.set_text(type_key)\n",
+    "                        ax3.plot(new_item['timestamp'], new_item['value'])\n",
+    "\n",
+    "                plt.suptitle('Used CPU Core {}'.format(key), fontsize=14)\n",
+    "                plt.show()\n",
+    "            print(\"Number of used cores:   \", len(used_cores))\n",
+    "    return used_cores"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Interface"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Interface Dropped (both type 1 and 2, i.e rx and tx)\n",
+    "#TODO: Change this to separate functions later\n",
+    "def interface_dropped(start=None, end=None, node=None, steps='15s', csv=None, verbose=False):\n",
+    "    \n",
+    "    if csv is not None:\n",
+    "        df = pd.read_csv(csv)\n",
+    "        df_0 = df #TODO: Change this\n",
+    "        df_1 = df #TODO: Change this\n",
+    "    else:\n",
+    "        if start is None or end is None or node is None:\n",
+    "            return \"Start, end and Node name required when fetching from prometheus\"\n",
+    "        \n",
+    "        params = {'query' : \"collectd_interface_if_dropped_0_total{exported_instance='\" + node + \"'}\"}\n",
+    "\n",
+    "        interface_dropped_0 = query_range(start, end, params, steps)\n",
+    "        df_0 = convert_to_df_range(interface_dropped_0)\n",
+    "        \n",
+    "        params = {'query' : \"collectd_interface_if_dropped_1_total{exported_instance='\" + node + \"'}\"}\n",
+    "        interface_dropped_1 = query_range(start, end, params, steps)\n",
+    "        df_1 = convert_to_df_range(interface_dropped_1)\n",
+    "\n",
+    "        \n",
+    "        #df_0 : interfaces_dropped_0_df\n",
+    "        df_0 = df_0.drop(['__name__', 'instance', 'job'], axis = 1)\n",
+    "\n",
+    "        #df_1 : interfaces_dropped_1_df\n",
+    "        df_1 = df_1.drop(['__name__', 'instance', 'job'], axis = 1)\n",
+    "\n",
+    "        groups_0 = df_0.groupby(['interface'])\n",
+    "        groups_1 = df_1.groupby(['interface'])\n",
+    "\n",
+    "        groups = [groups_0, groups_1]\n",
+    "        dropped_interfaces= []\n",
+    "        drop_type = 0\n",
+    "        color = ['oldlace', 'mistyrose']\n",
+    "        plot_iter = 111\n",
+    "        for group in groups:\n",
+    "            dropped = []\n",
+    "\n",
+    "            for key, item in group:\n",
+    "                curr_df = item\n",
+    "                if np.any(curr_df['value'] == '1'):\n",
+    "                    dropped_row = curr_df.loc[curr_df['value'] == '1']\n",
+    "                    dropped.append([key, dropped_row['timestamp'].iloc[0]])\n",
+    "                fig = plt.figure(figsize=(24,6), facecolor=color[drop_type], edgecolor='red')\n",
+    "                ax = fig.add_subplot(plot_iter)\n",
+    "                ax.title.set_text(\"Interface: {}\".format(key))\n",
+    "                ax.plot(item['timestamp'], item['value'])\n",
+    "            dropped_interfaces.append(dropped)\n",
+    "            plt.suptitle('Interfaces Drop type {}'.format(drop_type), fontsize=14)\n",
+    "            plt.show()\n",
+    "            drop_type += 1\n",
+    "    return dropped_interfaces\n",
+    "\n",
+    "\n",
+    "# Interface Errors (both type 1 and 2, i.e rx and tx)\n",
+    "#TODO: Change this to separate functions later\n",
+    "def interface_errors(start=None, end=None, node=None, steps='15s', csv=None, verbose=False):\n",
+    "    \n",
+    "    if csv is not None:\n",
+    "        df = pd.read_csv(csv)\n",
+    "        df_0 = df #TODO: Change this\n",
+    "        df_1 = df #TODO: Change this\n",
+    "    else:\n",
+    "        if start is None or end is None or node is None:\n",
+    "            return \"Start, end and Node name required when fetching from prometheus\"\n",
+    "        \n",
+    "        params = {'query' : \"collectd_interface_if_errors_0_total{exported_instance='\" + node + \"'}\"}\n",
+    "        interfaces_errors_0 = query_range(start, end, params, steps)\n",
+    "        df_0 = convert_to_df_range(interfaces_errors_0)\n",
+    "        \n",
+    "        params = {'query' : \"collectd_interface_if_errors_1_total{exported_instance='\" + node + \"'}\"}\n",
+    "        interface_errors_1 = query_range(start, end, params, steps)\n",
+    "        df_1 = convert_to_df_range(interface_errors_1)\n",
+    "\n",
+    "        \n",
+    "        #df_0 : interfaces_errors_0_df\n",
+    "        df_0 = df_0.drop(['__name__', 'instance', 'job'], axis = 1)\n",
+    "\n",
+    "        #df_1 : interfaces_dropped_1_df\n",
+    "        df_1 = df_1.drop(['__name__', 'instance', 'job'], axis = 1)\n",
+    "\n",
+    "        groups_0 = df_0.groupby(['interface'])\n",
+    "        groups_1 = df_1.groupby(['interface'])\n",
+    "\n",
+    "        groups = [groups_0, groups_1]\n",
+    "        err_interfaces= []\n",
+    "        err_type = 0\n",
+    "        color = ['oldlace', 'mistyrose']\n",
+    "        for group in groups:\n",
+    "            errors = []\n",
+    "\n",
+    "            for key, item in group:\n",
+    "                curr_df = item\n",
+    "\n",
+    "                if np.any(curr_df['value'] == '1'):\n",
+    "                    err_row = curr_df.loc[curr_df['value'] == '1']\n",
+    "                    erros.append([key, err_row['timestamp'].iloc[0]])\n",
+    "\n",
+    "                fig = plt.figure(figsize=(24,6), facecolor=color[err_type], edgecolor='red')\n",
+    "                ax = fig.add_subplot(111)\n",
+    "                ax.title.set_text(\"Interface: {}\".format(key))\n",
+    "                ax.plot(item['timestamp'], item['value'])\n",
+    "\n",
+    "            err_interfaces.append(errors)\n",
+    "            plt.suptitle('Interfaces Error type {}'.format(err_type), fontsize=14)\n",
+    "            plt.show()\n",
+    "            err_type += 1\n",
+    "\n",
+    "    return err_interfaces"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### RDT "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# L3 cache bytes\n",
+    "def plot_rdt_bytes(start=None, end=None, node=None, steps='15s', csv=None, verbose=False):\n",
+    "    \n",
+    "    if csv is not None:\n",
+    "        df = pd.read_csv(csv)\n",
+    "    else:\n",
+    "        if start is None or end is None or node is None:\n",
+    "            return \"Start, end and Node name required when fetching from prometheus\"\n",
+    "\n",
+    "        params = {'query' : \"collectd_intel_rdt_bytes{exported_instance='\" + node + \"'}\"}\n",
+    "        intel_rdt_bytes = query_range(start, end, params, steps)\n",
+    "        df = convert_to_df_range(intel_rdt_bytes)\n",
+    "\n",
+    "        df = df.drop(['__name__', 'instance', 'job'], axis = 1)\n",
+    "        groups = df.groupby(['intel_rdt'])\n",
+    "        for key, item in groups:\n",
+    "            curr_df = item\n",
+    "            fig = plt.figure(figsize=(24,6), facecolor='oldlace', edgecolor='red')\n",
+    "            ax1 = fig.add_subplot(111)\n",
+    "            ax1.title.set_text(\"Intel RDT Number: {}\".format(key))\n",
+    "            ax1.plot(item['timestamp'], item['value'])\n",
+    "            plt.show()\n",
+    "    return\n",
+    "\n",
+    "\n",
+    "# L3 IPC values\n",
+    "def plot_rdt_ipc(start=None, end=None, node=None, steps='15s', csv=None, verbose=False):\n",
+    "    \n",
+    "    if csv is not None:\n",
+    "        df = pd.read_csv(csv)\n",
+    "    else:\n",
+    "        if start is None or end is None or node is None:\n",
+    "            return \"Start, end and Node name required when fetching from prometheus\"\n",
+    " \n",
+    "        params = {'query' : \"collectd_intel_rdt_ipc{exported_instance='\" + node + \"'}\"}\n",
+    "        intel_rdt_ipc = query_range(start, end, params, steps)\n",
+    "        df = convert_to_df_range(intel_rdt_ipc)\n",
+    "\n",
+    "        df = df.drop(['__name__', 'instance', 'job'], axis = 1)\n",
+    "        groups = df.groupby(['intel_rdt'])\n",
+    "        for key, item in groups:\n",
+    "            curr_df = item\n",
+    "            fig = plt.figure(figsize=(24,6), facecolor='oldlace', edgecolor='red')\n",
+    "            ax1 = fig.add_subplot(111)\n",
+    "            ax1.title.set_text(\"Intel RDT Number: {}, IPC value\".format(key))\n",
+    "            ax1.plot(item['timestamp'], item['value'])\n",
+    "            plt.show()\n",
+    "    return\n",
+    "\n",
+    "\n",
+    "# memeory bandwidtdh\n",
+    "def get_rdt_memory_bandwidth(start=None, end=None, node=None, steps='15s', csv=None, verbose=False):\n",
+    "    \n",
+    "    if csv is not None:\n",
+    "        df = pd.read_csv(csv)\n",
+    "    else:\n",
+    "\n",
+    "        if start is None or end is None or node is None:\n",
+    "            return \"Start, end and Node name required when fetching from prometheus\"\n",
+    "        \n",
+    "        params = {'query' : \"collectd_intel_rdt_memory_bandwidth_total{exported_instance='\" + node + \"'}\"}\n",
+    "        intel_rdt_mem_bw = query_range(start, end, params, steps)\n",
+    "        df = convert_to_df_range(intel_rdt_mem_bw)\n",
+    "\n",
+    "        df = df.drop(['__name__', 'instance', 'job'], axis = 1)\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "def get_memory_usage(start=None, end=None, node=None, steps='15s', csv=None, verbose=False):\n",
+    "    \n",
+    "    if csv is not None:\n",
+    "        df = pd.read_csv(csv)\n",
+    "    else:\n",
+    "        if start is None or end is None or node is None:\n",
+    "            return \"Start, end and Node name required when fetching from prometheus\"\n",
+    "        \n",
+    "        params = {'query' : \"collectd_memory{exported_instance='\" + node + \"'} / (1024*1024*1024) \"}        \n",
+    "        target_memory_usage_range = query_range(start, end, params, steps)\n",
+    "        df = convert_to_df_range(target_memory_usage_range)\n",
+    "\n",
+    "        df = df.drop(['instance', 'job'], axis = 1)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Testing Zone"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# prom fetch\n",
+    "cores = unused_cores('2020-07-31 08:00:12', '2020-07-31 08:01:12', 'pod12-node4')\n",
+    "print(cores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usage / Examples\n",
+    "\n",
+    "\n",
+    "##### CPU \n",
+    "\n",
+    "- For calling cpu unsued cores\n",
+    "\n",
+    "```py\n",
+    "# Fetching from prometheus\n",
+    "cores = unused_cores('2020-07-31 08:00:12', '2020-07-31 08:01:12', 'pod12-node4')\n",
+    "\n",
+    "```\n",
+    "\n",
+    "- For finding fully used cores\n",
+    "\n",
+    "```py\n",
+    "# Fetching from prometheus\n",
+    "fully_used = fully_used_cores('2020-07-31 08:00:12', '2020-07-31 08:01:12', 'pod12-node4')\n",
+    "\n",
+    "```\n",
+    "\n",
+    "- Similarly for plotting used cores\n",
+    "\n",
+    "```py\n",
+    "# Fetching\n",
+    "plot_used_cores('2020-07-31 08:00:12', '2020-07-31 08:01:12', 'pod12-node4')\n",
+    "\n",
+    "#csv\n",
+    "# use Analysis-Monitoring-Local Notebook for correct analysis \n",
+    "plot_used_cores(csv='metrics_data/cpu-0/cpu-user-2020-06-02')\n",
+    "\n",
+    "```\n",
+    "\n",
+    "\n",
+    "##### Interface\n",
+    "\n",
+    "- Interface Dropped  \n",
+    "\n",
+    "```py\n",
+    "# Fetching from prom\n",
+    "dropped_interfaces = interface_dropped('2020-07-31 08:00:12', '2020-07-31 08:01:12', 'pod12-node4')\n",
+    "\n",
+    "```\n",
+    "\n",
+    "- Interface Errors\n",
+    "\n",
+    "```py\n",
+    "# Fetching from prom\n",
+    "interface_errors('2020-07-31 08:00:12', '2020-07-31 08:01:12', 'pod12-node4')\n",
+    "```\n",
+    "\n",
+    "##### RDT\n",
+    "\n",
+    "- Plot bytes\n",
+    "\n",
+    "```py\n",
+    "# fetch\n",
+    "plot_rdt_bytes('2020-07-31 08:00:12', '2020-07-31 08:01:12','pod12-node4')\n",
+    "```\n",
+    "\n",
+    "- Plot ipc values\n",
+    "\n",
+    "```py\n",
+    "#fetch\n",
+    "plot_rdt_ipc('2020-07-31 08:00:12', '2020-07-31 08:01:12', 'pod12-node4')\n",
+    "```\n",
+    "\n",
+    "- Memory bandwidth\n",
+    "\n",
+    "```py\n",
+    "#fetch\n",
+    "get_rdt_memory_bandwidth('2020-07-31 08:00:12', '2020-07-31 08:01:12', 'pod12-node4')\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "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": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}