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|
{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"id": "18170001",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['build.gradle.kts',\n",
" '.github',\n",
" 'opendc-web',\n",
" 'gradle.properties',\n",
" 'gradlew.bat',\n",
" 'docker-compose.yml',\n",
" '.gitignore',\n",
" 'resources',\n",
" '.dockerignore',\n",
" 'opendc-simulator',\n",
" '.gitattributes',\n",
" 'traces',\n",
" 'codecov.yml',\n",
" 'opendc-experiments',\n",
" '.editorconfig',\n",
" 'gradlew',\n",
" '.gradle',\n",
" 'site',\n",
" 'opendc-compute',\n",
" 'opendc-workflow',\n",
" 'output',\n",
" 'CONTRIBUTING.md',\n",
" 'opendc-trace',\n",
" 'LICENSE.txt',\n",
" 'docker-compose.prod.yml',\n",
" 'CITATION.cff',\n",
" '.git',\n",
" 'buildSrc',\n",
" 'build',\n",
" 'README.md',\n",
" 'opendc-common',\n",
" 'opendc-faas',\n",
" 'docker-compose.override.yml',\n",
" '.idea',\n",
" 'gradle',\n",
" 'settings.gradle.kts']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from IPython.display import display, HTML\n",
"\n",
"base_folder = \"../../../..\""
]
},
{
"cell_type": "markdown",
"id": "422f4d05",
"metadata": {},
"source": [
"## Topologies"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a2d05361",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Topology name: multi\n"
]
},
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: '../resources/env/multi.txt'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[2], line 6\u001b[0m\n\u001b[1;32m 3\u001b[0m df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m../resources/env/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtopology_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.txt\u001b[39m\u001b[38;5;124m\"\u001b[39m, delimiter\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m;\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4\u001b[0m display(HTML(df\u001b[38;5;241m.\u001b[39mto_html()))\n\u001b[0;32m----> 6\u001b[0m \u001b[43mread_topology\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmulti\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m read_topology(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msingle\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"Cell \u001b[0;32mIn[2], line 3\u001b[0m, in \u001b[0;36mread_topology\u001b[0;34m(topology_name)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mread_topology\u001b[39m(topology_name):\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTopology name: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtopology_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../resources/env/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mtopology_name\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m.txt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdelimiter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m;\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m display(HTML(df\u001b[38;5;241m.\u001b[39mto_html()))\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 210\u001b[0m kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/util/_decorators.py:317\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m 312\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 313\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[1;32m 314\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m 315\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(inspect\u001b[38;5;241m.\u001b[39mcurrentframe()),\n\u001b[1;32m 316\u001b[0m )\n\u001b[0;32m--> 317\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/parsers/readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m 936\u001b[0m dialect,\n\u001b[1;32m 937\u001b[0m delimiter,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 946\u001b[0m defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m 947\u001b[0m )\n\u001b[1;32m 948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/parsers/readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 602\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m 604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m 608\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1439\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1729\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1727\u001b[0m is_text \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 1728\u001b[0m mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1729\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1730\u001b[0m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1731\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1732\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1733\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1734\u001b[0m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1735\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1736\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1737\u001b[0m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1738\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1739\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1740\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/pandas/io/common.py:857\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 852\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m 853\u001b[0m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m 854\u001b[0m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m 855\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m 856\u001b[0m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 857\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 858\u001b[0m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 859\u001b[0m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 860\u001b[0m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 862\u001b[0m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 863\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 864\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 865\u001b[0m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m 866\u001b[0m handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../resources/env/multi.txt'"
]
}
],
"source": [
"def read_topology(topology_name):\n",
" print(f\"Topology name: {topology_name}\")\n",
" df = pd.read_csv(f\"../resources/env/{topology_name}.txt\", delimiter=\";\")\n",
" display(HTML(df.to_html()))\n",
" \n",
"read_topology(\"multi\")\n",
"read_topology(\"single\")"
]
},
{
"cell_type": "markdown",
"id": "8f4fe54d",
"metadata": {},
"source": [
"## Traces"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "fd17d88a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>timestamp</th>\n",
" <th>duration</th>\n",
" <th>cpu_count</th>\n",
" <th>cpu_usage</th>\n",
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" <tbody>\n",
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" <th>1</th>\n",
" <td>1019</td>\n",
" <td>2013-08-12 13:45:46+00:00</td>\n",
" <td>300000</td>\n",
" <td>1</td>\n",
" <td>11.703998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1019</td>\n",
" <td>2013-08-12 13:55:46+00:00</td>\n",
" <td>600000</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1019</td>\n",
" <td>2013-08-12 14:00:46+00:00</td>\n",
" <td>300000</td>\n",
" <td>1</td>\n",
" <td>11.703998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1019</td>\n",
" <td>2013-08-12 14:15:46+00:00</td>\n",
" <td>900000</td>\n",
" <td>1</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" id timestamp duration cpu_count cpu_usage\n",
"0 1019 2013-08-12 13:40:46+00:00 300000 1 0.000000\n",
"1 1019 2013-08-12 13:45:46+00:00 300000 1 11.703998\n",
"2 1019 2013-08-12 13:55:46+00:00 600000 1 0.000000\n",
"3 1019 2013-08-12 14:00:46+00:00 300000 1 11.703998\n",
"4 1019 2013-08-12 14:15:46+00:00 900000 1 0.000000"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trace = pd.read_parquet(f\"resources/bitbrains-small/trace/trace.parquet\")\n",
"trace.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "346f097f",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>start_time</th>\n",
" <th>stop_time</th>\n",
" <th>cpu_count</th>\n",
" <th>cpu_capacity</th>\n",
" <th>mem_capacity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1019</td>\n",
" <td>2013-08-12 13:35:46+00:00</td>\n",
" <td>2013-09-11 13:39:58+00:00</td>\n",
" <td>1</td>\n",
" <td>2926.000135</td>\n",
" <td>181352</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1023</td>\n",
" <td>2013-08-12 13:35:46+00:00</td>\n",
" <td>2013-09-11 13:39:58+00:00</td>\n",
" <td>1</td>\n",
" <td>2925.999560</td>\n",
" <td>260096</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1026</td>\n",
" <td>2013-08-12 13:35:46+00:00</td>\n",
" <td>2013-09-11 13:39:58+00:00</td>\n",
" <td>1</td>\n",
" <td>2925.999717</td>\n",
" <td>249972</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1052</td>\n",
" <td>2013-08-29 14:38:12+00:00</td>\n",
" <td>2013-09-05 07:09:07+00:00</td>\n",
" <td>1</td>\n",
" <td>2926.000107</td>\n",
" <td>131245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1073</td>\n",
" <td>2013-08-21 11:07:12+00:00</td>\n",
" <td>2013-09-11 13:39:58+00:00</td>\n",
" <td>1</td>\n",
" <td>2599.999649</td>\n",
" <td>179306</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id start_time stop_time cpu_count \\\n",
"0 1019 2013-08-12 13:35:46+00:00 2013-09-11 13:39:58+00:00 1 \n",
"1 1023 2013-08-12 13:35:46+00:00 2013-09-11 13:39:58+00:00 1 \n",
"2 1026 2013-08-12 13:35:46+00:00 2013-09-11 13:39:58+00:00 1 \n",
"3 1052 2013-08-29 14:38:12+00:00 2013-09-05 07:09:07+00:00 1 \n",
"4 1073 2013-08-21 11:07:12+00:00 2013-09-11 13:39:58+00:00 1 \n",
"\n",
" cpu_capacity mem_capacity \n",
"0 2926.000135 181352 \n",
"1 2925.999560 260096 \n",
"2 2925.999717 249972 \n",
"3 2926.000107 131245 \n",
"4 2599.999649 179306 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"meta = pd.read_parquet(f\"resources/bitbrains-small/trace/meta.parquet\")\n",
"meta.head(5)"
]
},
{
"cell_type": "markdown",
"id": "13bf9fdb",
"metadata": {},
"source": [
"# Lets run this in OpenDC!"
]
},
{
"cell_type": "markdown",
"id": "c9766446",
"metadata": {},
"source": [
"## Resulting Files"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "0d400ffd",
"metadata": {},
"outputs": [],
"source": [
"output_folder = \"output\"\n",
"workload = \"workload=bitbrains-small\"\n",
"seed = \"seed=0\"\n",
"\n",
"df_host_multi = pd.read_parquet(f\"{output_folder}/host/topology=multi/{workload}/{seed}/data.parquet\")\n",
"df_host_single = pd.read_parquet(f\"{output_folder}/host/topology=single/{workload}/{seed}/data.parquet\")\n",
"\n",
"df_server_multi = pd.read_parquet(f\"{output_folder}/server/topology=multi/{workload}/{seed}/data.parquet\")\n",
"df_server_single = pd.read_parquet(f\"{output_folder}/server/topology=single/{workload}/{seed}/data.parquet\")\n",
"\n",
"df_service_multi = pd.read_parquet(f\"{output_folder}/service/topology=multi/{workload}/{seed}/data.parquet\")\n",
"df_service_single = pd.read_parquet(f\"{output_folder}/service/topology=single/{workload}/{seed}/data.parquet\")"
]
},
{
"cell_type": "markdown",
"id": "6d494d6e",
"metadata": {},
"source": [
"### Host"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "48a1e1a6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['timestamp',\n",
" 'host_id',\n",
" 'cpu_count',\n",
" 'mem_capacity',\n",
" 'guests_terminated',\n",
" 'guests_running',\n",
" 'guests_error',\n",
" 'guests_invalid',\n",
" 'cpu_limit',\n",
" 'cpu_usage',\n",
" 'cpu_demand',\n",
" 'cpu_utilization',\n",
" 'cpu_time_active',\n",
" 'cpu_time_idle',\n",
" 'cpu_time_steal',\n",
" 'cpu_time_lost',\n",
" 'power_total',\n",
" 'uptime',\n",
" 'downtime',\n",
" 'boot_time']"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of measurements: 77778\n"
]
}
],
"source": [
"display(list(df_host_multi.columns))\n",
"print(f\"number of measurements: {len(df_host_multi)}\")"
]
},
{
"cell_type": "markdown",
"id": "9eb9be2c",
"metadata": {},
"source": [
"### Server"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "57a2b148",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['timestamp',\n",
" 'server_id',\n",
" 'host_id',\n",
" 'uptime',\n",
" 'downtime',\n",
" 'provision_time',\n",
" 'boot_time',\n",
" 'cpu_count',\n",
" 'cpu_limit',\n",
" 'cpu_time_active',\n",
" 'cpu_time_idle',\n",
" 'cpu_time_steal',\n",
" 'cpu_time_lost',\n",
" 'mem_limit']"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of measurements: 408040\n"
]
}
],
"source": [
"display(list(df_server_multi.columns))\n",
"print(f\"number of measurements: {len(df_server_multi)}\")"
]
},
{
"cell_type": "markdown",
"id": "fbe5f439",
"metadata": {},
"source": [
"### Service"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "9ef468ed",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"['timestamp',\n",
" 'hosts_up',\n",
" 'hosts_down',\n",
" 'servers_pending',\n",
" 'servers_active',\n",
" 'attempts_success',\n",
" 'attempts_failure',\n",
" 'attempts_error']"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of measurements: 8642\n"
]
}
],
"source": [
"display(list(df_service_single.columns))\n",
"print(f\"number of measurements: {len(df_host_single)}\")"
]
},
{
"cell_type": "markdown",
"id": "09d31c91",
"metadata": {},
"source": [
"## Power Usage"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "82f0a24a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"big topology: 3400876.9739568997\n",
"small topology: 60000.000858306885\n"
]
}
],
"source": [
"print(f\"big topology: {df_host_multi.power_total.sum()}\")\n",
"print(f\"small topology: {df_host_single.power_total.sum()}\")"
]
},
{
"cell_type": "markdown",
"id": "7ab3357d",
"metadata": {},
"source": [
"## CPU usage"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "e94db3a6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"big topology: 2.352397194967523e-05\n",
"small topology: 0.00011571395510298542\n"
]
}
],
"source": [
"print(f\"big topology: {df_host_multi.cpu_utilization.mean()}\")\n",
"print(f\"small topology: {df_host_single.cpu_utilization.mean()}\")"
]
},
{
"cell_type": "markdown",
"id": "e000a260",
"metadata": {},
"source": [
"## CPU utilization"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "8d7daa45",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"big topology: 2.352397194967523e-05\n",
"small topology: 0.00011571395510298542\n"
]
}
],
"source": [
"print(f\"big topology: {df_host_multi.cpu_utilization.mean()}\")\n",
"print(f\"small topology: {df_host_single.cpu_utilization.mean()}\")"
]
},
{
"cell_type": "markdown",
"id": "ad97741c",
"metadata": {},
"source": [
"## Plotting Results"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "5df8f9aa",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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UCDcAAMBUCDcAAMBUXAo3J06ccHcdAAAAbuFSuGnTpo369eun//7v/9bFixfdXRMAAIDLXAo3+/fv10033aT4+HiFhYXp0Ucf1Z49e1wqICkpSREREfLz81NUVNQfbueXX37R+PHjFR4eLl9fX7Vr106bNm1yad8AAMB8XAo3kZGReuWVV3TmzBmtWLFCmZmZ6tOnjzp37qxFixYpJyenUttZs2aN4uPjlZCQoP3796tr166KjY3V2bNnK+xfVFSk/v3769SpU/rggw90+PBhLV++XE2bNnXlMAAAgAlZDMMwqrqRwsJCvfbaa5o6daqKiopktVr1wAMPaN68eQoPD7/iuKioKPXo0UNLly6VJJWVlclms+nxxx/XlClTyvVftmyZFixYoEOHDsnHx8elWvPy8hQUFKTc3FwFBga6tI2rSZ8X6/LYyMlb3FgJAADm4cz3d5Xultq7d6/GjRun8PBwLVq0SE899ZSOHz+urVu36syZMxo0aNAVxxYVFWnfvn2KiYn5VzFeXoqJiVFqamqFYzZs2KDo6GiNHz9eoaGh6ty5s+bMmaPS0tIr7qewsFB5eXkOLwAAYF51XBm0aNEirVy5UocPH9aAAQP09ttva8CAAfLyupSVWrZsqVWrVikiIuKK2zh37pxKS0sVGhrq0B4aGqpDhw5VOObEiRPavn27hg0bpk2bNunYsWMaN26ciouLlZCQUOGYxMREzZo1y5XDBAAAtZBL4eb111/Xww8/rJEjR17xtFNISIjefPPNKhX3e2VlZQoJCdEbb7whb29vdevWTT/88IMWLFhwxXAzdepUxcfH29/n5eXJZrO5tS4AAHDtcCncHD169A/7WK1WjRgx4oqfBwcHy9vbW9nZ2Q7t2dnZCgsLq3BMeHi4fHx85O3tbW/r2LGjsrKy7Nf6/J6vr698fX3/sF4AAGAOLl1zs3LlSq1du7Zc+9q1a/XWW29VahtWq1XdunVTcnKyva2srEzJycmKjo6ucEzv3r117NgxlZWV2duOHDmi8PDwCoMNAAC4/rgUbhITExUcHFyuPSQkRHPmzKn0duLj47V8+XK99dZbOnjwoP7yl7+ooKBAo0aNkiTFxcVp6tSp9v5/+ctf9NNPP2nixIk6cuSINm7cqDlz5mj8+PGuHAYAADAhl05LZWRkqGXLluXaW7RooYyMjEpvZ8iQIcrJydHMmTOVlZWlyMhIbd682X6RcUZGhv0iZUmy2WzasmWLJk2apJtuuklNmzbVxIkTNXnyZFcOAwAAmJBL4SYkJERff/11ubuhvvrqKzVq1MipbU2YMEETJkyo8LOUlJRybdHR0friiy+c2gcAALh+uHRa6sEHH9R//dd/6dNPP1VpaalKS0u1fft2TZw4Uf/5n//p7hoBAAAqzaWVm9mzZ+vUqVO64447VKfOpU2UlZUpLi7OqWtuAAAA3M2lcGO1WrVmzRrNnj1bX331lerWrasuXbqoRYsW7q4PAADAKS6Fm8vatWundu3auasWAACAKnMp3JSWlmrVqlVKTk7W2bNnHZ47I0nbt293S3EAAADOcincTJw4UatWrdI999yjzp07y2KxuLsuAAAAl7gUblavXq33339fAwYMcHc9AAAAVeLSreBWq1Vt2rRxdy0AAABV5lK4efLJJ/XKK6/IMAx31wMAAFAlLp2W2rFjhz799FN98sknuvHGG+Xj4+Pw+bp169xSHAAAgLNcCjf169fXfffd5+5aAAAAqsylcLNy5Up31wEAAOAWLl1zI0klJSXatm2b/vrXvyo/P1+SdObMGZ0/f95txQEAADjLpZWb7777TnfddZcyMjJUWFio/v37KyAgQPPmzVNhYaGWLVvm7joBAAAqxaWVm4kTJ6p79+76+eefVbduXXv7fffdp+TkZLcVBwAA4CyXVm4+//xz7dq1S1ar1aE9IiJCP/zwg1sKAwAAcIVLKzdlZWUqLS0t1/79998rICCgykUBAAC4yqVwc+edd2rx4sX29xaLRefPn1dCQgI/yQAAADzKpdNSCxcuVGxsrDp16qSLFy9q6NChOnr0qIKDg/Xee++5u0YAAIBKcyncNGvWTF999ZVWr16tr7/+WufPn9fo0aM1bNgwhwuMAQAAappL4UaS6tSpo4ceesidtQAAAFSZS+Hm7bffvurncXFxLhUDAABQVS6Fm4kTJzq8Ly4u1oULF2S1WuXv70+4AQAAHuPS3VI///yzw+v8+fM6fPiw+vTpwwXFAADAo1z+banfa9u2rebOnVtuVQcAAKAmuS3cSJcuMj5z5ow7NwkAAOAUl6652bBhg8N7wzCUmZmppUuXqnfv3m4pDAAAwBUuhZvBgwc7vLdYLGrcuLFuv/12LVy40B11AQAAuMSlcFNWVubuOgAAANzCrdfcAAAAeJpLKzfx8fGV7rto0SJXdgEAAOASl8JNWlqa0tLSVFxcrPbt20uSjhw5Im9vb91yyy32fhaLxT1VAgAAVJJL4WbgwIEKCAjQW2+9pQYNGki69GC/UaNGqW/fvnryySfdWiQAAEBluXTNzcKFC5WYmGgPNpLUoEEDvfDCC9wtBQAAPMqlcJOXl6ecnJxy7Tk5OcrPz69yUQAAAK5yKdzcd999GjVqlNatW6fvv/9e33//vT788EONHj1a999/v7trBAAAqDSXrrlZtmyZnnrqKQ0dOlTFxcWXNlSnjkaPHq0FCxa4tUAAAABnuBRu/P399dprr2nBggU6fvy4JKl169a64YYb3FocAACAs6r0EL/MzExlZmaqbdu2uuGGG2QYhrvqAgAAcIlL4ebHH3/UHXfcoXbt2mnAgAHKzMyUJI0ePZrbwAEAgEe5FG4mTZokHx8fZWRkyN/f394+ZMgQbd682W3FAQAAOMula27+8Y9/aMuWLWrWrJlDe9u2bfXdd9+5pTAAAABXuLRyU1BQ4LBic9lPP/0kX1/fKhcFAADgKpfCTd++ffX222/b31ssFpWVlWn+/Pnq16+f24oDAABwlkunpebPn6877rhDe/fuVVFRkZ555hl98803+umnn7Rz50531wgAAFBpLq3cdO7cWUeOHFGfPn00aNAgFRQU6P7771daWppat27t7hoBAAAqzemVm+LiYt11111atmyZpk2bVh01AQAAuMzplRsfHx99/fXX1VELAABAlbl0Wuqhhx7Sm2++6e5aAAAAqsylC4pLSkq0YsUKbdu2Td26dSv3m1KLFi1yS3EAAADOcircnDhxQhEREfrnP/+pW265RZJ05MgRhz4Wi8V91QEAADjJqXDTtm1bZWZm6tNPP5V06ecWlixZotDQ0GopDgAAwFlOXXPz+1/9/uSTT1RQUODWggAAAKrCpQuKL/t92AEAAPA0p8KNxWIpd00N19gAAIBriVPX3BiGoZEjR9p/HPPixYt67LHHyt0ttW7dOvdVCAAA4ASnws2IESMc3j/00ENuLQYAAKCqnAo3K1eurK46AAAA3KJKFxQDAABca66JcJOUlKSIiAj5+fkpKipKe/bsqdS41atXy2KxaPDgwdVbIAAAqDU8Hm7WrFmj+Ph4JSQkaP/+/eratatiY2N19uzZq447deqUnnrqKfXt27eGKgUAALWBx8PNokWLNGbMGI0aNUqdOnXSsmXL5O/vrxUrVlxxTGlpqYYNG6ZZs2apVatWNVgtAAC41nk03BQVFWnfvn2KiYmxt3l5eSkmJkapqalXHPf8888rJCREo0eP/sN9FBYWKi8vz+EFAADMy6Ph5ty5cyotLS3321ShoaHKysqqcMyOHTv05ptvavny5ZXaR2JiooKCguwvm81W5boBAMC1y+OnpZyRn5+v4cOHa/ny5QoODq7UmKlTpyo3N9f+On36dDVXCQAAPMmp59y4W3BwsLy9vZWdne3Qnp2drbCwsHL9jx8/rlOnTmngwIH2trKyMklSnTp1dPjwYbVu3dphjK+vr/2JygAAwPw8unJjtVrVrVs3JScn29vKysqUnJys6Ojocv07dOigAwcOKD093f6699571a9fP6Wnp3PKCQAAeHblRpLi4+M1YsQIde/eXT179tTixYtVUFCgUaNGSZLi4uLUtGlTJSYmys/PT507d3YYX79+fUkq1w4AAK5PHg83Q4YMUU5OjmbOnKmsrCxFRkZq8+bN9ouMMzIy5OVVqy4NAgAAHmQxDMPwdBE1KS8vT0FBQcrNzVVgYKDbt58+L9blsZGTt7ixEgAAzMOZ72+WRAAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKlcE+EmKSlJERER8vPzU1RUlPbs2XPFvsuXL1ffvn3VoEEDNWjQQDExMVftDwAAri8eDzdr1qxRfHy8EhIStH//fnXt2lWxsbE6e/Zshf1TUlL04IMP6tNPP1VqaqpsNpvuvPNO/fDDDzVcOQAAuBZZDMMwPFlAVFSUevTooaVLl0qSysrKZLPZ9Pjjj2vKlCl/OL60tFQNGjTQ0qVLFRcXV+7zwsJCFRYW2t/n5eXJZrMpNzdXgYGB7juQ/5M+L9blsZGTt7ixEgAAzCMvL09BQUGV+v726MpNUVGR9u3bp5iYGHubl5eXYmJilJqaWqltXLhwQcXFxWrYsGGFnycmJiooKMj+stlsbqkdAABcmzwabs6dO6fS0lKFhoY6tIeGhiorK6tS25g8ebKaNGniEJB+a+rUqcrNzbW/Tp8+XeW6AQDAtauOpwuoirlz52r16tVKSUmRn59fhX18fX3l6+tbw5UBAABP8Wi4CQ4Olre3t7Kzsx3as7OzFRYWdtWxL730kubOnatt27bppptuqs4yAQBALeLR01JWq1XdunVTcnKyva2srEzJycmKjo6+4rj58+dr9uzZ2rx5s7p3714TpQIAgFrC46el4uPjNWLECHXv3l09e/bU4sWLVVBQoFGjRkmS4uLi1LRpUyUmJkqS5s2bp5kzZ+rdd99VRESE/dqcevXqqV69eh47DgAAcG3weLgZMmSIcnJyNHPmTGVlZSkyMlKbN2+2X2SckZEhL69/LTC9/vrrKioq0p///GeH7SQkJOi5556rydIBAMA1yOPPualpztwn7wqecwMAgPvVmufcAAAAuBvhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmArhBgAAmMo1EW6SkpIUEREhPz8/RUVFac+ePVftv3btWnXo0EF+fn7q0qWLNm3aVEOVAgCAa53Hw82aNWsUHx+vhIQE7d+/X127dlVsbKzOnj1bYf9du3bpwQcf1OjRo5WWlqbBgwdr8ODB+uc//1nDlQMAgGuRxTAMw5MFREVFqUePHlq6dKkkqaysTDabTY8//rimTJlSrv+QIUNUUFCgjz/+2N72pz/9SZGRkVq2bNkf7i8vL09BQUHKzc1VYGCg+w7k/6TPi3V5bOTkLW6sBAAA83Dm+7tODdVUoaKiIu3bt09Tp061t3l5eSkmJkapqakVjklNTVV8fLxDW2xsrNavX19h/8LCQhUWFtrf5+bmSro0SdXh/MUSl8dWV00AANR2l78jK7Mm49Fwc+7cOZWWlio0NNShPTQ0VIcOHapwTFZWVoX9s7KyKuyfmJioWbNmlWu32WwuVl2NngvydAUAAFzT8vPzFRR09e9Lj4abmjB16lSHlZ6ysjL99NNPatSokSwWi1v3lZeXJ5vNptOnT1fLKS9cwjzXDOa5ZjDPNYe5rhnVNc+GYSg/P19NmjT5w74eDTfBwcHy9vZWdna2Q3t2drbCwsIqHBMWFuZUf19fX/n6+jq01a9f3/WiKyEwMJC/ODWAea4ZzHPNYJ5rDnNdM6pjnv9oxeYyj94tZbVa1a1bNyUnJ9vbysrKlJycrOjo6ArHREdHO/SXpK1bt16xPwAAuL54/LRUfHy8RowYoe7du6tnz55avHixCgoKNGrUKElSXFycmjZtqsTEREnSxIkTdeutt2rhwoW65557tHr1au3du1dvvPGGJw8DAABcIzweboYMGaKcnBzNnDlTWVlZioyM1ObNm+0XDWdkZMjL618LTL169dK7776r6dOn69lnn1Xbtm21fv16de7c2VOHYOfr66uEhIRyp8HgXsxzzWCeawbzXHOY65pxLcyzx59zAwAA4E4ef0IxAACAOxFuAACAqRBuAACAqRBuAACAqRBunJSUlKSIiAj5+fkpKipKe/bsuWr/tWvXqkOHDvLz81OXLl20adOmGqq0dnNmnpcvX66+ffuqQYMGatCggWJiYv7w3wsucfbP82WrV6+WxWLR4MGDq7dAk3B2nn/55ReNHz9e4eHh8vX1Vbt27fhvRyU4O8+LFy9W+/btVbduXdlsNk2aNEkXL16soWprp88++0wDBw5UkyZNZLFYrvi7jr+VkpKiW265Rb6+vmrTpo1WrVpV7XXKQKWtXr3asFqtxooVK4xvvvnGGDNmjFG/fn0jOzu7wv47d+40vL29jfnz5xvffvutMX36dMPHx8c4cOBADVdeuzg7z0OHDjWSkpKMtLQ04+DBg8bIkSONoKAg4/vvv6/hymsXZ+f5spMnTxpNmzY1+vbtawwaNKhmiq3FnJ3nwsJCo3v37saAAQOMHTt2GCdPnjRSUlKM9PT0Gq68dnF2nt955x3D19fXeOedd4yTJ08aW7ZsMcLDw41JkybVcOW1y6ZNm4xp06YZ69atMyQZH3300VX7nzhxwvD39zfi4+ONb7/91nj11VcNb29vY/PmzdVaJ+HGCT179jTGjx9vf19aWmo0adLESExMrLD/Aw88YNxzzz0ObVFRUcajjz5arXXWds7O8++VlJQYAQEBxltvvVVdJZqCK/NcUlJi9OrVy/jb3/5mjBgxgnBTCc7O8+uvv260atXKKCoqqqkSTcHZeR4/frxx++23O7TFx8cbvXv3rtY6zaQy4eaZZ54xbrzxRoe2IUOGGLGxsdVYmWFwWqqSioqKtG/fPsXExNjbvLy8FBMTo9TU1ArHpKamOvSXpNjY2Cv2h2vz/HsXLlxQcXGxGjZsWF1l1nquzvPzzz+vkJAQjR49uibKrPVcmecNGzYoOjpa48ePV2hoqDp37qw5c+aotLS0psqudVyZ5169emnfvn32U1cnTpzQpk2bNGDAgBqp+Xrhqe9Bjz+huLY4d+6cSktL7U9Oviw0NFSHDh2qcExWVlaF/bOysqqtztrOlXn+vcmTJ6tJkybl/kLhX1yZ5x07dujNN99Uenp6DVRoDq7M84kTJ7R9+3YNGzZMmzZt0rFjxzRu3DgVFxcrISGhJsqudVyZ56FDh+rcuXPq06ePDMNQSUmJHnvsMT377LM1UfJ140rfg3l5efr1119Vt27datkvKzcwlblz52r16tX66KOP5Ofn5+lyTCM/P1/Dhw/X8uXLFRwc7OlyTK2srEwhISF644031K1bNw0ZMkTTpk3TsmXLPF2aqaSkpGjOnDl67bXXtH//fq1bt04bN27U7NmzPV0a3ICVm0oKDg6Wt7e3srOzHdqzs7MVFhZW4ZiwsDCn+sO1eb7spZde0ty5c7Vt2zbddNNN1VlmrefsPB8/flynTp3SwIED7W1lZWWSpDp16ujw4cNq3bp19RZdC7ny5zk8PFw+Pj7y9va2t3Xs2FFZWVkqKiqS1Wqt1pprI1fmecaMGRo+fLgeeeQRSVKXLl1UUFCgsWPHatq0aQ6/aQjXXel7MDAwsNpWbSRWbirNarWqW7duSk5OtreVlZUpOTlZ0dHRFY6Jjo526C9JW7duvWJ/uDbPkjR//nzNnj1bmzdvVvfu3Wui1FrN2Xnu0KGDDhw4oPT0dPvr3nvvVb9+/ZSeni6bzVaT5dcarvx57t27t44dO2YPj5J05MgRhYeHE2yuwJV5vnDhQrkAczlQGvzkott47HuwWi9XNpnVq1cbvr6+xqpVq4xvv/3WGDt2rFG/fn0jKyvLMAzDGD58uDFlyhR7/507dxp16tQxXnrpJePgwYNGQkICt4JXgrPzPHfuXMNqtRoffPCBkZmZaX/l5+d76hBqBWfn+fe4W6pynJ3njIwMIyAgwJgwYYJx+PBh4+OPPzZCQkKMF154wVOHUCs4O88JCQlGQECA8d577xknTpww/vGPfxitW7c2HnjgAU8dQq2Qn59vpKWlGWlpaYYkY9GiRUZaWprx3XffGYZhGFOmTDGGDx9u73/5VvCnn37aOHjwoJGUlMSt4NeiV1991WjevLlhtVqNnj17Gl988YX9s1tvvdUYMWKEQ//333/faNeunWG1Wo0bb7zR2LhxYw1XXDs5M88tWrQwJJV7JSQk1HzhtYyzf55/i3BTec7O865du4yoqCjD19fXaNWqlfHiiy8aJSUlNVx17ePMPBcXFxvPPfec0bp1a8PPz8+w2WzGuHHjjJ9//rnmC69FPv300wr/e3t5bkeMGGHceuut5cZERkYaVqvVaNWqlbFy5cpqr9NiGKy/AQAA8+CaGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwAAYCqEGwCmEBERocWLF9vfWywWrV+/XpJ06tQpWSwWpaenV2sNt912m5544olq3QeAP0a4AeAgKytLjz/+uFq1aiVfX1/ZbDYNHDjQ4cfvIiIiZLFYZLFYdMMNN+iWW27R2rVr7Z+PHDlSgwcPLrftlJQUWSwW/fLLLy7Xt2rVKtWvX79c+5dffqmxY8dWOMZmsykzM1OdO3d2eb+/daXjWLdunWbPnu2WfQBwHeEGgN2pU6fUrVs3bd++XQsWLNCBAwe0efNm9evXT+PHj3fo+/zzzyszM1NpaWnq0aOHhgwZol27dnmocqlx48by9/ev8DNvb2+FhYWpTp061VpDw4YNFRAQUK37APDHCDcA7MaNGyeLxaI9e/bo3//939WuXTvdeOONio+P1xdffOHQNyAgQGFhYWrXrp2SkpJUt25d/c///E+V9l/Rikh6erosFotOnTqllJQUjRo1Srm5ufaVo+eee05S+dNSv/X701IjR460j//tKyUlRZL097//Xd27d7cf49ChQ3X27Fn7tvr16ydJatCggSwWi0aOHCmp/Gmpn3/+WXFxcWrQoIH8/f1199136+jRo/bPL69CbdmyRR07dlS9evV01113KTMzs0rzCFzvCDcAJEk//fSTNm/erPHjx+uGG24o93lFp4Iuq1Onjnx8fFRUVFSNFUq9evXS4sWLFRgYqMzMTGVmZuqpp55yejuvvPKKfXxmZqYmTpyokJAQdejQQZJUXFys2bNn66uvvtL69et16tQpe4Cx2Wz68MMPJUmHDx9WZmamXnnllQr3M3LkSO3du1cbNmxQamqqDMPQgAEDVFxcbO9z4cIFvfTSS/r73/+uzz77TBkZGS4dE4B/qd41WgC1xrFjx2QYhv0LvrKKioq0cOFC5ebm6vbbb6+m6i6xWq0KCgqSxWJRWFiYy9sJCgpSUFCQpEvXyfz1r3/Vtm3b7Nt8+OGH7X1btWqlJUuWqEePHjp//rzq1aunhg0bSpJCQkKuGPqOHj2qDRs2aOfOnerVq5ck6Z133pHNZtP69ev1H//xH5IuBally5apdevWkqQJEybo+eefd/nYALByA+D/GIbhVP/JkyerXr168vf317x58zR37lzdc8891VRd9UhLS9Pw4cO1dOlS9e7d296+b98+DRw4UM2bN1dAQIBuvfVWSVJGRkalt33w4EHVqVNHUVFR9rZGjRqpffv2OnjwoL3N39/fHmwkKTw83H4KDIBrWLkBIElq27atLBaLDh06VKn+Tz/9tEaOHKl69eopNDRUFovF/llgYKC+++67cmN++eUXeXt7V3jaS5K8vC79/9Zvg9ZvT+G4U1ZWlu6991498sgjGj16tL29oKBAsbGxio2N1TvvvKPGjRsrIyNDsbGx1XLazcfHx+G9xWJxOmgCcMTKDQBJl+70iY2NVVJSkgoKCsp9/vvbnoODg9WmTRuFhYU5BBtJat++vb755hsVFhY6tO/fv18tW7Ys94V+WePGjSXJ4YLa3z+bxmq1qrS0tLKHVaGLFy9q0KBB6tChgxYtWuTw2aFDh/Tjjz9q7ty56tu3rzp06FBuJcVqtUrSVevo2LGjSkpKtHv3bnvbjz/+qMOHD6tTp05Vqh/A1RFuANglJSWptLRUPXv21IcffqijR4/q4MGDWrJkiaKjoyu9nWHDhslisSguLk779u3TsWPHtGLFCi1evFhPPvnkFce1adNGNptNzz33nI4ePaqNGzdq4cKFDn0iIiJ0/vx5JScn69y5c7pw4YLTx/noo4/q9OnTWrJkiXJycpSVlaWsrCwVFRWpefPmslqtevXVV3XixAlt2LCh3LNrWrRoIYvFoo8//lg5OTk6f/58uX20bdtWgwYN0pgxY7Rjxw599dVXeuihh9S0aVMNGjTI6ZoBVB7hBoBdq1attH//fvXr109PPvmkOnfurP79+ys5OVmvv/56pbdTv359ff755youLta9996ryMhILVmyRIsWLdKjjz56xXE+Pj567733dOjQId10002aN2+eXnjhBYc+vXr10mOPPaYhQ4aocePGmj9/vtPH+b//+7/KzMxUp06dFB4ebn/t2rVLjRs31qpVq7R27Vp16tRJc+fO1UsvveQwvmnTppo1a5amTJmi0NBQTZgwocL9rFy5Ut26ddO//du/KTo6WoZhaNOmTVdcuQLgHhaDk7sAAMBEWLkBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACmQrgBAACm8v8BjCGIOvW4gUwAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = df_host_multi.cpu_utilization\n",
"plt.hist(data, weights=np.ones_like(data) / len(data),\n",
" alpha=0.7, label=\"big\", bins=30)\n",
"\n",
"\n",
"data = df_host_single.cpu_utilization\n",
"plt.hist(data, weights=np.ones_like(data) / len(data),\n",
" alpha=0.7, label=\"small\", bins=30)\n",
"\n",
"plt.xlabel(\"CPU utilization\")\n",
"plt.ylabel(\"Frequency\")\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "520e42a4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0 8641\n",
"1.0 1\n",
"Name: cpu_utilization, dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_host_single.cpu_utilization.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "a8c35267",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([b'\\xf8\\x8b\\xb8\\xa8rL\\x81\\xec\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x02',\n",
" b'\\x1b9\\x89jQ\\xa8t\\x9b\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x03',\n",
" b'\\xc5\\x84\\x13:\\xc9\\x16\\xab<\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00',\n",
" b'S\\xcb\\x9f\\x0ct~\\xa2\\xea\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x04',\n",
" b'\\xe2 \\xa89{\\x1d\\xcd\\xaf\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00',\n",
" b'\\x06\\xc4]\\x18\\x80\\tEO\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x01',\n",
" b',\\x82\\x9a\\xbe\\x1fE2\\xe1\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x05',\n",
" b'>\\xe5x\\x90A\\xc9\\x8a\\xc3\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x01',\n",
" b'nx\\x9ej\\xa1\\xb9e\\xf4\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00'],\n",
" dtype=object)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_host_multi.host_id.unique()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "68546b09",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>timestamp</th>\n",
" <th>hosts_up</th>\n",
" <th>hosts_down</th>\n",
" <th>servers_pending</th>\n",
" <th>servers_active</th>\n",
" <th>attempts_success</th>\n",
" <th>attempts_failure</th>\n",
" <th>attempts_error</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1970-01-01 00:05:00+00:00</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>44</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1970-01-01 00:10:00+00:00</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>29</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1970-01-01 00:15:00+00:00</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>29</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1970-01-01 00:20:00+00:00</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>29</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1970-01-01 00:25:00+00:00</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>29</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8637</th>\n",
" <td>1970-01-30 23:50:00+00:00</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8638</th>\n",
" <td>1970-01-30 23:55:00+00:00</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8639</th>\n",
" <td>1970-01-31 00:00:00+00:00</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8640</th>\n",
" <td>1970-01-31 00:05:00+00:00</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8641</th>\n",
" <td>1970-01-31 00:10:00+00:00</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>49</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8642 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp hosts_up hosts_down servers_pending \\\n",
"0 1970-01-01 00:05:00+00:00 1 0 44 \n",
"1 1970-01-01 00:10:00+00:00 1 0 29 \n",
"2 1970-01-01 00:15:00+00:00 1 0 29 \n",
"3 1970-01-01 00:20:00+00:00 1 0 29 \n",
"4 1970-01-01 00:25:00+00:00 1 0 29 \n",
"... ... ... ... ... \n",
"8637 1970-01-30 23:50:00+00:00 0 1 0 \n",
"8638 1970-01-30 23:55:00+00:00 0 1 0 \n",
"8639 1970-01-31 00:00:00+00:00 0 1 0 \n",
"8640 1970-01-31 00:05:00+00:00 0 1 0 \n",
"8641 1970-01-31 00:10:00+00:00 0 1 0 \n",
"\n",
" servers_active attempts_success attempts_failure attempts_error \n",
"0 0 0 0 0 \n",
"1 15 15 0 0 \n",
"2 15 15 0 0 \n",
"3 15 15 0 0 \n",
"4 15 15 0 0 \n",
"... ... ... ... ... \n",
"8637 0 49 1 0 \n",
"8638 0 49 1 0 \n",
"8639 0 49 1 0 \n",
"8640 0 49 1 0 \n",
"8641 0 49 1 0 \n",
"\n",
"[8642 rows x 8 columns]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_service_single"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "5b9ccf81",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 6189\n",
"1 1828\n",
"2 612\n",
"44 13\n",
"Name: servers_active, dtype: int64"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_service_multi.servers_active.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9e9bd9b9",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|
