Merge "Tools: Add monitoring analysis jupyter notebook"

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Metrics Analysis Notebook (local)\n",
+    "\n",
+    "#### Used to analyse / visualize the metrics when uploaded via csv file\n",
+    "\n",
+    "### Contributor:    Aditya Srivastava <adityasrivastava301199@gmail.com>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.dates as mdates\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import pandas as pd\n",
+    "from pprint import pprint\n",
+    "import re\n",
+    "import requests\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Helper Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DATETIME_FORMAT = \"%Y-%m-%d %H:%M:%S\"\n",
+    "\n",
+    "def convert_to_timestamp(s):\n",
+    "    global DATETIME_FORMAT\n",
+    "    return time.mktime(datetime.strptime(s, DATETIME_FORMAT).timetuple())\n",
+    "\n",
+    "def convert_to_time_string(epoch):\n",
+    "    global DATETIME_FORMAT\n",
+    "    t = datetime.fromtimestamp(float(epoch)/1000.)\n",
+    "    return t.strftime(DATETIME_FORMAT)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Note: \n",
+    "    \n",
+    "Path will be used as a parameter in almost every function\n",
+    "\n",
+    "path / rootdir / csv : (str) Path to the folder whose direct children are metric folders\n",
+    "\n",
+    "example: /path/to/folder\n",
+    "\n",
+    "When : \n",
+    "```sh\n",
+    "ls /path/to/folder\n",
+    "\n",
+    "# output should be directories such as\n",
+    "# cpu-0 cpu-1 cpu-2  ..........................\n",
+    "# processes-ovs-vswitchd ........processes-ovsdb-server\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analysis Function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### CPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rootdir = 'metrics_data/'\n",
+    "\n",
+    "def fetch_cpu_data(rootdir):\n",
+    "    df = pd.DataFrame()\n",
+    "    reg_compile = re.compile(\"cpu-\\d{1,2}\")\n",
+    "    for dirpath, dirnames, filenames in os.walk(rootdir):\n",
+    "        dirname = dirpath.split(os.sep)[-1] \n",
+    "        if reg_compile.match(dirname):\n",
+    "            # read 3 files from this folder...\n",
+    "            _df = pd.DataFrame()\n",
+    "            for file in filenames:\n",
+    "                if 'user' in file:\n",
+    "                    temp_df = pd.read_csv(dirpath + os.sep + file)\n",
+    "                    _df['user'] = temp_df['value']\n",
+    "                    _df['epoch'] = temp_df['epoch']\n",
+    "\n",
+    "                if 'system' in file:\n",
+    "                    temp_df = pd.read_csv(dirpath + os.sep + file)\n",
+    "                    _df['system'] = temp_df['value']\n",
+    "                    _df['epoch'] = temp_df['epoch']\n",
+    "\n",
+    "                if 'idle' in file:\n",
+    "                    temp_df = pd.read_csv(dirpath + os.sep + file)\n",
+    "                    _df['idle'] = temp_df['value']\n",
+    "                    _df['epoch'] = temp_df['epoch']\n",
+    "\n",
+    "            _df['cpu'] = dirname.split('-')[-1]\n",
+    "\n",
+    "            df = df.append(_df, ignore_index=True)\n",
+    "\n",
+    "    total = df['user'] + df['system'] + df['idle']\n",
+    "\n",
+    "    df['user_percentage'] = df['user']*100 / total\n",
+    "    df['system_percentage'] = df['system']*100 / total\n",
+    "    df['idle_percentage'] = df['idle']*100 / total\n",
+    "    \n",
+    "    return df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CPU Unused Cores\n",
+    "def unused_cores(rootdir, verbose=False):\n",
+    "    \n",
+    "    df = fetch_cpu_data(rootdir)\n",
+    "    groups = df.groupby(['cpu'])\n",
+    "    if verbose: print(\"Unused Cores :\")\n",
+    "\n",
+    "    unused_cores = []\n",
+    "    for key, item in groups:\n",
+    "        curr_df = item\n",
+    "        unused_cores.append(key)\n",
+    "        idle_values = curr_df.loc[curr_df['idle_percentage'] < 99.999]\n",
+    "        if np.any(idle_values):\n",
+    "            unused_cores.pop(-1)\n",
+    "\n",
+    "    unused_cores = set(unused_cores)\n",
+    "    for key, item in groups:\n",
+    "        if key not in unused_cores:\n",
+    "            continue\n",
+    "        fig = plt.figure(figsize=(24,6), facecolor='oldlace', edgecolor='red')\n",
+    "\n",
+    "        ax1 = fig.add_subplot(131)\n",
+    "        ax1.title.set_text(\"System\")\n",
+    "        ax1.plot(item['epoch'], item['system_percentage'])\n",
+    "    \n",
+    "        ax2 = fig.add_subplot(132)\n",
+    "        ax2.title.set_text(\"User\")\n",
+    "        ax2.plot(item['epoch'], item['user_percentage'])\n",
+    "            \n",
+    "        ax3 = fig.add_subplot(133)\n",
+    "        ax3.title.set_text(\"Idle\")\n",
+    "        ax3.plot(item['epoch'], item['idle_percentage'])\n",
+    "\n",
+    "        plt.suptitle('Used CPU Core {}'.format(key), fontsize=14)\n",
+    "        plt.show()\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(rootdir, verbose=False):\n",
+    "    \n",
+    "\n",
+    "    df = fetch_cpu_data(rootdir)\n",
+    "    groups = df.groupby(['cpu'])\n",
+    "    if verbose: print(\"Fully Used Cores :\")\n",
+    "\n",
+    "    fully_used_cores = []\n",
+    "    for key, item in groups:\n",
+    "        curr_df = item\n",
+    "        idle_values = curr_df.loc[curr_df['idle_percentage'] <= 10]\n",
+    "        if np.any(idle_values):\n",
+    "            fully_used_cores.append(key)\n",
+    "\n",
+    "    fully_used_cores = set(fully_used_cores)\n",
+    "    for key, item in groups:\n",
+    "        if key not in fully_used_cores:\n",
+    "            continue\n",
+    "        fig = plt.figure(figsize=(24,6), facecolor='oldlace', edgecolor='red')\n",
+    "\n",
+    "        ax1 = fig.add_subplot(131)\n",
+    "        ax1.title.set_text(\"System\")\n",
+    "        ax1.plot(item['epoch'], item['system_percentage'])\n",
+    "\n",
+    "        ax2 = fig.add_subplot(132)\n",
+    "        ax2.title.set_text(\"User\")\n",
+    "        ax2.plot(item['epoch'], item['user_percentage'])\n",
+    "\n",
+    "        ax3 = fig.add_subplot(133)\n",
+    "        ax3.title.set_text(\"Idle\")\n",
+    "        ax3.plot(item['epoch'], item['idle_percentage'])\n",
+    "\n",
+    "        plt.suptitle('Used CPU Core {}'.format(key), fontsize=14)\n",
+    "        plt.show()\n",
+    "\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 used_cores(rootdir, verbose=False):\n",
+    "\n",
+    "    df = fetch_cpu_data(rootdir)\n",
+    "    groups = df.groupby(['cpu'])\n",
+    "    if verbose: print(\"Used Cores :\")\n",
+    "\n",
+    "    used_cores = []\n",
+    "    for key, item in groups:\n",
+    "        curr_df = item\n",
+    "        idle_values = curr_df.loc[curr_df['idle_percentage'] < 99.999]\n",
+    "        if np.any(idle_values):\n",
+    "            used_cores.append(key)\n",
+    "\n",
+    "    used_cores = set(used_cores)\n",
+    "    for key, item in groups:\n",
+    "        if key not in used_cores:\n",
+    "            continue\n",
+    "        fig = plt.figure(figsize=(24,6), facecolor='oldlace', edgecolor='red')\n",
+    "\n",
+    "        ax1 = fig.add_subplot(131)\n",
+    "        ax1.title.set_text(\"System\")\n",
+    "        ax1.plot(item['epoch'], item['system_percentage'])\n",
+    "\n",
+    "        ax2 = fig.add_subplot(132)\n",
+    "        ax2.title.set_text(\"User\")\n",
+    "        ax2.plot(item['epoch'], item['user_percentage'])\n",
+    "\n",
+    "        ax3 = fig.add_subplot(133)\n",
+    "        ax3.title.set_text(\"Idle\")\n",
+    "        ax3.plot(item['epoch'], item['idle_percentage'])\n",
+    "\n",
+    "        plt.suptitle('Used CPU Core {}'.format(key), fontsize=14)\n",
+    "        plt.show()\n",
+    "\n",
+    "    print(\"Number of used cores:   \", len(used_cores))\n",
+    "    return used_cores\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Interface"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rootdir = 'metrics_data/'\n",
+    "\n",
+    "def fetch_interfaces_data(rootdir):\n",
+    "\n",
+    "    df = pd.DataFrame()\n",
+    "    reg_compile = re.compile(\"interface-.*\")\n",
+    "    for dirpath, dirnames, filenames in os.walk(rootdir):\n",
+    "        dirname = dirpath.split(os.sep)[-1] \n",
+    "        if reg_compile.match(dirname):\n",
+    "            # read 3 files from this folder...\n",
+    "            _df = pd.DataFrame()\n",
+    "            for file in filenames:\n",
+    "                if 'errors' in file:\n",
+    "                    temp_df = pd.read_csv(dirpath + os.sep + file)\n",
+    "                    _df['error_rx'] = temp_df['rx']\n",
+    "                    _df['error_tx'] = temp_df['tx']\n",
+    "                    _df['epoch'] = temp_df['epoch']\n",
+    "\n",
+    "                if 'dropped' in file:\n",
+    "                    temp_df = pd.read_csv(dirpath + os.sep + file)\n",
+    "                    _df['dropped_rx'] = temp_df['rx']\n",
+    "                    _df['dropped_tx'] = temp_df['tx']\n",
+    "                    _df['epoch'] = temp_df['epoch']\n",
+    "\n",
+    "            _df['interface'] = '-'.join(dirname.split('-')[1:])\n",
+    "            df = df.append(_df, ignore_index=True)\n",
+    "    return df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Interface Dropped (both type 1 and 2, i.e rx and tx)\n",
+    "def interface_dropped(rootdir, verbose=False):\n",
+    "        \n",
+    "    df = fetch_interfaces_data(rootdir)\n",
+    "    group = df.groupby(['interface'])\n",
+    "    color = ['oldlace', 'mistyrose']\n",
+    "\n",
+    "    dropped = {'rx':[], 'tx':[]}\n",
+    "\n",
+    "    itr = 0\n",
+    "    for key, item in group:\n",
+    "        curr_df = item\n",
+    "\n",
+    "        if np.any(curr_df['dropped_rx'] == 1):\n",
+    "            dropped_rows = curr_df[curr_df['dropped_rx'] == 1]\n",
+    "            dropped['rx'].append([key, dropped_row['epoch'].iloc[0]])\n",
+    "        if np.any(curr_df['dropped_tx'] == 1):\n",
+    "            dropped_rows = curr_df[curr_df['dropped_tx'] == 1]\n",
+    "            dropped['tx'].append([key, dropped_row['epoch'].iloc[0]])\n",
+    "\n",
+    "        fig = plt.figure(figsize=(24,6), facecolor=color[itr%2], edgecolor='red')\n",
+    "        ax = fig.add_subplot(211)\n",
+    "        ax.title.set_text(\"Interface: {} Dropped (rx)\".format(key))\n",
+    "        ax.plot(item['epoch'], item['dropped_rx'])\n",
+    "\n",
+    "        ax1 = fig.add_subplot(212)\n",
+    "        ax1.title.set_text(\"Interface: {} Dropped (tx)\".format(key))\n",
+    "        ax1.plot(item['epoch'], item['dropped_tx'])\n",
+    "\n",
+    "        itr += 1\n",
+    "\n",
+    "    plt.suptitle('Interface Dropped', fontsize=14)\n",
+    "    plt.show()\n",
+    "\n",
+    "    return dropped\n",
+    "\n",
+    "\n",
+    "# Interface Errors (both type 1 and 2, i.e rx and tx)\n",
+    "def interface_errors(rootdir, verbose=False):\n",
+    "        \n",
+    "    df = fetch_interfaces_data(rootdir)\n",
+    "    group = df.groupby(['interface'])\n",
+    "    color = ['oldlace', 'mistyrose']\n",
+    "\n",
+    "    errors = {'rx':[], 'tx':[]}\n",
+    "\n",
+    "    itr = 0\n",
+    "    for key, item in group:\n",
+    "        curr_df = item\n",
+    "\n",
+    "        if np.any(curr_df['error_rx'] == 1):\n",
+    "            err_rows = curr_df[curr_df['error_rx'] == 1]\n",
+    "            errors['rx'].append([key, err_row['epoch'].iloc[0]])\n",
+    "        if np.any(curr_df['error_tx'] == 1):\n",
+    "            err_rows = curr_df[curr_df['error_tx'] == 1]\n",
+    "            errors['tx'].append([key, err_row['epoch'].iloc[0]])\n",
+    "\n",
+    "        fig = plt.figure(figsize=(24,6), facecolor=color[itr%2], edgecolor='red')\n",
+    "        ax = fig.add_subplot(211)\n",
+    "        ax.title.set_text(\"Interface: {} Errors (rx)\".format(key))\n",
+    "        ax.plot(item['epoch'], item['error_rx'])\n",
+    "\n",
+    "        ax1 = fig.add_subplot(212)\n",
+    "        ax1.title.set_text(\"Interface: {} Errors (tx)\".format(key))\n",
+    "        ax1.plot(item['epoch'], item['error_tx'])\n",
+    "\n",
+    "        itr += 1\n",
+    "\n",
+    "    plt.suptitle('Interface Erros', fontsize=14)\n",
+    "    plt.show()\n",
+    "\n",
+    "    return errors\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### OVS Stats (Non DPDK)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rootdir = 'metrics_data/'\n",
+    "\n",
+    "def fetch_ovs_stats_data(rootdir):\n",
+    "    df = pd.DataFrame()\n",
+    "    reg_compile = re.compile(\"ovs_stats-.*\")\n",
+    "    for dirpath, dirnames, filenames in os.walk(rootdir):\n",
+    "        dirname = dirpath.split(os.sep)[-1] \n",
+    "        if reg_compile.match(dirname):\n",
+    "            if 'dpdk' in dirname:\n",
+    "                continue #ignoring dpdk\n",
+    "\n",
+    "            _df = pd.DataFrame()\n",
+    "            for file in filenames:\n",
+    "                if 'errors' in file:\n",
+    "                    col_name = '-'.join(file.split('_')[1:])\n",
+    "                    temp_df = pd.read_csv(dirpath + os.sep + file)\n",
+    "\n",
+    "                    _df['epoch'] = temp_df['epoch']\n",
+    "                    temp_df = temp_df.drop(['epoch'], axis=1)\n",
+    "                    new_cols = [i + '_' + col_name for i in temp_df.columns]\n",
+    "                    _df[new_cols] = temp_df\n",
+    "\n",
+    "                if 'dropped' in file:\n",
+    "                    col_name = '-'.join(file.split('_')[1:])\n",
+    "                    temp_df = pd.read_csv(dirpath + os.sep + file)\n",
+    "                    _df['epoch'] = temp_df['epoch']\n",
+    "                    temp_df = temp_df.drop(['epoch'], axis=1)\n",
+    "                    new_cols = [i + '_' + col_name for i in temp_df.columns]\n",
+    "                    _df[new_cols] = temp_df            \n",
+    "            _df['interface'] = '-'.join(dirname.split('-')[1:])\n",
+    "            df = df.append(_df, ignore_index=True)\n",
+    "    return df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ovs_stats_dropped(rootdir, verbose=False):\n",
+    "    \n",
+    "    df = fetch_ovs_stats_data(rootdir)\n",
+    "    group = df.groupby(['interface'])\n",
+    "    color = ['oldlace', 'mistyrose']\n",
+    "\n",
+    "    i = 0\n",
+    "    for key, item in group:\n",
+    "        curr_df = item\n",
+    "        for col in curr_df:\n",
+    "            if 'dropped' in col:\n",
+    "                if item[col].isnull().all():\n",
+    "                    continue\n",
+    "                fig = plt.figure(figsize=(24,6), facecolor=color[i%2], edgecolor='red')\n",
+    "                plt.plot(item['epoch'], item[col])\n",
+    "                plt.title(\"Interface: {} Dropped {}\".format(key, col))\n",
+    "        i += 1\n",
+    "    plt.show()\n",
+    "    return\n",
+    "\n",
+    "\n",
+    "# Interface Errors (both type 1 and 2, i.e rx and tx)\n",
+    "def ovs_stats_errors(rootdir, verbose=False):\n",
+    "\n",
+    "\n",
+    "    df = fetch_ovs_stats_data(rootdir)\n",
+    "    group = df.groupby(['interface'])\n",
+    "    color = ['oldlace', 'mistyrose']\n",
+    "\n",
+    "    i = 0\n",
+    "    for key, item in group:\n",
+    "        curr_df = item\n",
+    "        for col in curr_df:\n",
+    "            if 'error' in col:\n",
+    "                if item[col].isnull().all():\n",
+    "                    continue\n",
+    "                fig = plt.figure(figsize=(24,6), facecolor=color[i%2], edgecolor='red')\n",
+    "                plt.plot(item['epoch'], item[col])\n",
+    "                plt.title(\"Interface: {} Errors {}\".format(key, col))\n",
+    "        i += 1\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### DPDK"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rootdir = 'metrics_data/'\n",
+    "\n",
+    "def fetch_dpdk_data(rootdir):\n",
+    "    df = pd.DataFrame()\n",
+    "    reg_compile = re.compile(\".*dpdk.*\")\n",
+    "    for dirpath, dirnames, filenames in os.walk(rootdir):\n",
+    "        dirname = dirpath.split(os.sep)[-1] \n",
+    "        if reg_compile.match(dirname):\n",
+    "            _df = pd.DataFrame()\n",
+    "            for file in filenames:\n",
+    "                if 'errors' in file:\n",
+    "                    col_name = '-'.join(file.split('_')[1:])\n",
+    "                    temp_df = pd.read_csv(dirpath + os.sep + file)\n",
+    "\n",
+    "                    _df['epoch'] = temp_df['epoch']\n",
+    "                    temp_df = temp_df.drop(['epoch'], axis=1)\n",
+    "                    new_cols = [i + '_' + col_name for i in temp_df.columns]\n",
+    "                    _df[new_cols] = temp_df\n",
+    "\n",
+    "                if 'dropped' in file:\n",
+    "                    col_name = '-'.join(file.split('_')[1:])\n",
+    "                    temp_df = pd.read_csv(dirpath + os.sep + file)\n",
+    "                    _df['epoch'] = temp_df['epoch']\n",
+    "                    temp_df = temp_df.drop(['epoch'], axis=1)\n",
+    "                    new_cols = [i + '_' + col_name for i in temp_df.columns]\n",
+    "                    _df[new_cols] = temp_df            \n",
+    "            _df['dpdk'] = '-'.join(dirname.split('-')[1:])\n",
+    "            df = df.append(_df, ignore_index=True)\n",
+    "    return df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fetch_dpdk_data(rootdir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dpdk_dropped(rootdir, verbose=False):\n",
+    "    \n",
+    "    df = fetch_dpdk_data(rootdir)\n",
+    "    group = df.groupby(['dpdk'])\n",
+    "    color = ['oldlace', 'mistyrose']\n",
+    "\n",
+    "    i = 0\n",
+    "    for key, item in group:\n",
+    "        curr_df = item\n",
+    "        for col in curr_df:\n",
+    "            if 'dropped' in col:\n",
+    "                if item[col].isnull().all():\n",
+    "                    continue\n",
+    "                fig = plt.figure(figsize=(24,6), facecolor=color[i%2], edgecolor='red')\n",
+    "                plt.plot(item['epoch'], item[col])\n",
+    "                plt.title(\"DpDK: {} Dropped {}\".format(key, col))\n",
+    "        i += 1\n",
+    "    plt.show()\n",
+    "    return\n",
+    "\n",
+    "\n",
+    "# Interface Errors (both type 1 and 2, i.e rx and tx)\n",
+    "def dpdk_errors(rootdir, verbose=False):\n",
+    "\n",
+    "\n",
+    "    df = fetch_dpdk_data(rootdir)\n",
+    "    group = df.groupby(['dpdk'])\n",
+    "    color = ['oldlace', 'mistyrose']\n",
+    "\n",
+    "    i = 0\n",
+    "    for key, item in group:\n",
+    "        curr_df = item\n",
+    "        for col in curr_df:\n",
+    "            if 'error' in col:\n",
+    "                if item[col].isnull().all():\n",
+    "                    continue\n",
+    "                fig = plt.figure(figsize=(24,6), facecolor=color[i%2], edgecolor='red')\n",
+    "                plt.plot(item['epoch'], item[col])\n",
+    "                plt.title(\"DpDK: {} Errors {}\".format(key, col))\n",
+    "        i += 1\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dpdk_dropped(rootdir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### RDT  (need to be testes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rootdir = 'metrics_data/'\n",
+    "\n",
+    "def fetch_rdt_data(rootdir):\n",
+    "    df = pd.DataFrame()\n",
+    "    reg_compile = re.compile(\".*rdt.*\")\n",
+    "    for dirpath, dirnames, filenames in os.walk(rootdir):\n",
+    "        dirname = dirpath.split(os.sep)[-1] \n",
+    "        if reg_compile.match(dirname):\n",
+    "            _df = pd.DataFrame()\n",
+    "            for file in filenames:\n",
+    "                if 'bytes' in file:\n",
+    "                    col_name = '-'.join(file.split('_')[1:])\n",
+    "                    temp_df = pd.read_csv(dirpath + os.sep + file)\n",
+    "\n",
+    "                    _df['epoch'] = temp_df['epoch']\n",
+    "                    temp_df = temp_df.drop(['epoch'], axis=1)\n",
+    "                    new_cols = [i + '_' + col_name for i in temp_df.columns]\n",
+    "                    _df[new_cols] = temp_df\n",
+    "                    \n",
+    "                if 'bandwidth' in file:\n",
+    "                    col_name = '-'.join(file.split('_')[1:])\n",
+    "                    temp_df = pd.read_csv(dirpath + os.sep + file)\n",
+    "\n",
+    "                    _df['epoch'] = temp_df['epoch']\n",
+    "                    temp_df = temp_df.drop(['epoch'], axis=1)\n",
+    "                    new_cols = [i + '_' + col_name for i in temp_df.columns]\n",
+    "                    _df[new_cols] = temp_df\n",
+    "\n",
+    "                if 'ipc' in file:\n",
+    "                    col_name = '-'.join(file.split('_')[1:])\n",
+    "                    temp_df = pd.read_csv(dirpath + os.sep + file)\n",
+    "                    _df['epoch'] = temp_df['epoch']\n",
+    "                    temp_df = temp_df.drop(['epoch'], axis=1)\n",
+    "                    new_cols = [i + '_' + col_name for i in temp_df.columns]\n",
+    "                    _df[new_cols] = temp_df            \n",
+    "            _df['intel_rdt'] = '-'.join(dirname.split('-')[1:])\n",
+    "            df = df.append(_df, ignore_index=True)\n",
+    "    return df\n"
+   ]
+  },
+  {
+   "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",
+    "    df = fetch_rdt_data(rootdir)\n",
+    "    group = df.groupby(['intel_rdt'])\n",
+    "    color = ['oldlace', 'mistyrose']\n",
+    "\n",
+    "    i = 0\n",
+    "    for key, item in group:\n",
+    "        curr_df = item\n",
+    "        for col in curr_df:\n",
+    "            if 'bytes' in col:\n",
+    "                if item[col].isnull().all():\n",
+    "                    continue\n",
+    "                fig = plt.figure(figsize=(24,6), facecolor=color[i%2], edgecolor='red')\n",
+    "                plt.plot(item['epoch'], item[col])\n",
+    "                plt.title(\"RDT BYTES, RDT: {}\".format(key, col))\n",
+    "        i += 1\n",
+    "    plt.show()\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",
+    "    \n",
+    "    df = fetch_rdt_data(rootdir)\n",
+    "    group = df.groupby(['intel_rdt'])\n",
+    "    color = ['oldlace', 'mistyrose']\n",
+    "\n",
+    "    i = 0\n",
+    "    for key, item in group:\n",
+    "        curr_df = item\n",
+    "        for col in curr_df:\n",
+    "            if 'ipc' in col:\n",
+    "                if item[col].isnull().all():\n",
+    "                    continue\n",
+    "                fig = plt.figure(figsize=(24,6), facecolor=color[i%2], edgecolor='red')\n",
+    "                plt.plot(item['epoch'], item[col])\n",
+    "                plt.title(\"RDT IPC, RDT: {}\".format(key, col))\n",
+    "        i += 1\n",
+    "    plt.show()\n",
+    "\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",
+    "        \n",
+    "    df = fetch_rdt_data(rootdir)\n",
+    "    group = df.groupby(['intel_rdt'])\n",
+    "    color = ['oldlace', 'mistyrose']\n",
+    "\n",
+    "    i = 0\n",
+    "    for key, item in group:\n",
+    "        curr_df = item\n",
+    "        for col in curr_df:\n",
+    "            if 'bandwidht' in col:\n",
+    "                if item[col].isnull().all():\n",
+    "                    continue\n",
+    "                fig = plt.figure(figsize=(24,6), facecolor=color[i%2], edgecolor='red')\n",
+    "                plt.plot(item['epoch'], item[col])\n",
+    "                plt.title(\"RDT Memory Bandwidht, RDT: {}\".format(key, col))\n",
+    "        i += 1\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Memory (following functions still need to written for csv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rootdir = 'metrics_data/'\n",
+    "\n",
+    "def fetch_memory_data(rootdir):\n",
+    "    df = pd.DataFrame()\n",
+    "    reg_compile = re.compile(\"memory\")\n",
+    "    for dirpath, dirnames, filenames in os.walk(rootdir):\n",
+    "        dirname = dirpath.split(os.sep)[-1] \n",
+    "        if reg_compile.match(dirname):\n",
+    "            print(dirname)\n",
+    "            _df = pd.DataFrame()\n",
+    "            for file in filenames:                \n",
+    "                col_name = file.split('-')[1]\n",
+    "                temp_df = pd.read_csv(dirpath + os.sep + file)\n",
+    "                _df['epoch'] = temp_df['epoch']\n",
+    "                temp_df = temp_df.drop(['epoch'], axis=1)\n",
+    "                new_cols = [col_name for i in temp_df.columns]\n",
+    "                _df[new_cols] = temp_df\n",
+    "            df = df.append(_df, ignore_index=True)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "def get_memory_usage(rootdir, verbose=False):\n",
+    "    df = fetch_memory_data(rootdir)\n",
+    "    color = ['oldlace', 'mistyrose']\n",
+    "    i = 0\n",
+    "    for col in df:\n",
+    "        if df[col].isnull().all():\n",
+    "            continue\n",
+    "        fig = plt.figure(figsize=(24,6), facecolor=color[i%2], edgecolor='red')\n",
+    "        plt.plot(df['epoch'], df[col])\n",
+    "        plt.title(\"{} Memory\".format(col))\n",
+    "        i += 1\n",
+    "    plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usage / Examples\n",
+    "\n",
+    "\n",
+    "##### CPU \n",
+    "\n",
+    "- For calling cpu unsued cores\n",
+    "\n",
+    "```py\n",
+    "cores = unused_cores(rootdir='metrics_data')\n",
+    "```\n",
+    "\n",
+    "- For finding fully used cores\n",
+    "\n",
+    "```py\n",
+    "fully_used = fully_used_cores('metrics_data')\n",
+    "```\n",
+    "\n",
+    "- Similarly for plotting used cores\n",
+    "\n",
+    "```py\n",
+    "plot_used_cores(csv='metrics_data')\n",
+    "```\n",
+    "\n",
+    "\n",
+    "##### Interface\n",
+    "\n",
+    "- Interface Dropped  \n",
+    "\n",
+    "```py\n",
+    "# Using CSV\n",
+    "dropped_interfaces = interface_dropped('metrics_data')\n",
+    "```\n",
+    "\n",
+    "- Interface Errors\n",
+    "\n",
+    "```py\n",
+    "# Using CSV\n",
+    "interface_errors('metrics_data')\n",
+    "```\n",
+    "\n",
+    "##### OVS Stats\n",
+    "\n",
+    "- OVS Stats Dropped  \n",
+    "\n",
+    "```py\n",
+    "# Using CSV\n",
+    "ovs_stats_dropped('metrics_data')\n",
+    "```\n",
+    "\n",
+    "- OVS Stats Errors\n",
+    "\n",
+    "```py\n",
+    "# Using CSV\n",
+    "ovs_stats_errors('metrics_data')\n",
+    "```\n",
+    "\n",
+    "##### DPDK \n",
+    "\n",
+    "- DPDK Dropped  \n",
+    "\n",
+    "```py\n",
+    "# Using CSV\n",
+    "dpdk_dropped('metrics_data')\n",
+    "```\n",
+    "\n",
+    "- DPDK Errors\n",
+    "\n",
+    "```py\n",
+    "# Using CSV\n",
+    "dpdk_errors('metrics_data')\n",
+    "```\n",
+    "\n",
+    "\n",
+    "\n",
+    "##### RDT (Do not run yet)\n",
+    "\n",
+    "- Plot bytes\n",
+    "\n",
+    "```py\n",
+    "#csv\n",
+    "plot_rdt_bytes('metrics_data')\n",
+    "```\n",
+    "\n",
+    "- Plot ipc values\n",
+    "\n",
+    "```py\n",
+    "#csv\n",
+    "plot_rdt_ipc('metrics_data')\n",
+    "```\n",
+    "\n",
+    "- Memory bandwidth\n",
+    "\n",
+    "```py\n",
+    "#csv\n",
+    "get_rdt_memory_bandwidth('metrics_data')\n",
+    "```\n",
+    "\n",
+    "##### Memory\n",
+    "\n",
+    "```py\n",
+    "#csv\n",
+    "get_memory_usage('metrics_data')\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
+}