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+3.13
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+{
+ "cells": [
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "# Cloud Assess\n",
+ "\n",
+ "This notebook demonstrates Cloud Assess.\n",
+ "\n",
+ "## VM workload simulation\n",
+ "\n",
+ "We simulate a workload of virtual machines (VM) as follows.\n",
+ "- The period is 1 month, i.e. 30 days\n",
+ "- The granularity of 1 hour, i.e. 24*30 = 720 measure points per VM\n",
+ "- 10 clients, each client creates 1 VM\n",
+ "- Each VM starts and ends at a random time\n",
+ "- Each VM is specified by a random RAM, vCPU and storage usage"
+ ],
+ "id": "9739e5e77a0d1368"
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-31T10:11:34.107085Z",
+ "start_time": "2025-10-31T10:11:34.104488Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "from dataclasses import dataclass, asdict\n",
+ "from random import randint\n",
+ "\n",
+ "import pandas as pd\n",
+ "from requests import post\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns"
+ ],
+ "id": "b689166ceea8bb67",
+ "outputs": [],
+ "execution_count": 94
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-31T09:13:16.078631Z",
+ "start_time": "2025-10-31T09:13:16.074787Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "period = 30 # days\n",
+ "delta_t = 1 # hour\n",
+ "t_max = period * 24 - 1 # clock ends at last tick\n",
+ "\n",
+ "@dataclass\n",
+ "class VM:\n",
+ " uid : str\n",
+ " start: int\n",
+ " end: int\n",
+ " ram: int # GB\n",
+ " vcpu: int # dimensionless\n",
+ " storage: int # GB\n",
+ "\n",
+ "def gen_vm(uid: str) -> VM:\n",
+ " start = randint(0, t_max)\n",
+ " end = start + randint(0, t_max - start)\n",
+ " ram = randint(2, 64)\n",
+ " vcpu = randint(1, 8)\n",
+ " storage = randint(100, 2000)\n",
+ " return VM(uid, start, end, ram, vcpu, storage)\n",
+ "\n",
+ "def gen_usage(n_clients) -> list[VM]:\n",
+ " return [gen_vm(f\"client_{i}\") for i in range(n_clients)]\n"
+ ],
+ "id": "fdef91c6bc0de37d",
+ "outputs": [],
+ "execution_count": 26
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-31T09:47:18.097367Z",
+ "start_time": "2025-10-31T09:47:18.092986Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "@dataclass\n",
+ "class TimeSlice:\n",
+ " t: int\n",
+ " active_vms: list[VM]\n",
+ "\n",
+ " def total_ram(self) -> int:\n",
+ " return sum([vm.ram for vm in self.active_vms])\n",
+ "\n",
+ " def total_vcpu(self) -> int:\n",
+ " return sum([vm.vcpu for vm in self.active_vms])\n",
+ "\n",
+ " def total_storage(self) -> int:\n",
+ " return sum([vm.storage for vm in self.active_vms])\n",
+ "\n",
+ "@dataclass\n",
+ "class Workload:\n",
+ " slices: list[TimeSlice]\n",
+ "\n",
+ " def __init__(self, vms: list[VM]):\n",
+ " self.slices = []\n",
+ " for t in range(0, t_max):\n",
+ " active_vms = [vm for vm in vms if (vm.start <= t) and (vm.end >= t)]\n",
+ " self.slices.append(TimeSlice(t, active_vms))"
+ ],
+ "id": "14b8fe7893fb93e2",
+ "outputs": [],
+ "execution_count": 52
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-31T09:47:19.223101Z",
+ "start_time": "2025-10-31T09:47:19.220139Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "n_clients = 10\n",
+ "workload = Workload(gen_usage(n_clients))"
+ ],
+ "id": "81988e4d68361fbd",
+ "outputs": [],
+ "execution_count": 53
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "## Invoking Cloud Assess\n",
+ "\n",
+ "We push the workload to Cloud Assess API and collect the impacts for every client.\n",
+ "\n",
+ "### Mappers\n",
+ "\n",
+ "Some utility functions to prepare the API requests."
+ ],
+ "id": "d430722d66bee8a7"
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-31T10:23:15.623117Z",
+ "start_time": "2025-10-31T10:23:15.619390Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "pool_id = \"worker\"\n",
+ "\n",
+ "def vm_dto(vm: VM):\n",
+ " return {\n",
+ " \"id\": vm.uid,\n",
+ " \"pool_id\": pool_id,\n",
+ " \"quantity\": {\n",
+ " \"amount\": 1.0,\n",
+ " \"unit\": \"u\"\n",
+ " },\n",
+ " \"ram\": {\n",
+ " \"amount\": vm.ram,\n",
+ " \"unit\": \"GB\"\n",
+ " },\n",
+ " \"vcpu\": {\n",
+ " \"amount\": vm.vcpu,\n",
+ " \"unit\": \"vCPU\",\n",
+ " },\n",
+ " \"storage\": {\n",
+ " \"amount\": vm.storage,\n",
+ " \"unit\": \"GB\",\n",
+ " },\n",
+ " }\n",
+ "\n",
+ "def request_dto(t_slice: TimeSlice):\n",
+ " return {\n",
+ " \"period\": {\n",
+ " \"amount\": delta_t,\n",
+ " \"unit\": \"hour\",\n",
+ " },\n",
+ " \"total_ram\": {\n",
+ " \"amount\": t_slice.total_ram(),\n",
+ " \"unit\": \"GB\",\n",
+ " },\n",
+ " \"total_vcpu\": {\n",
+ " \"amount\": t_slice.total_vcpu(),\n",
+ " \"unit\": \"vCPU\",\n",
+ " },\n",
+ " \"total_storage\": {\n",
+ " \"amount\": t_slice.total_storage(),\n",
+ " \"unit\": \"GB\",\n",
+ " },\n",
+ " \"virtual_machines\": [vm_dto(vm) for vm in t_slice.active_vms]\n",
+ " }"
+ ],
+ "id": "5710628e4ce72ecd",
+ "outputs": [],
+ "execution_count": 102
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": "### Collect impacts\n",
+ "id": "719a049484abb74d"
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-31T10:23:19.693181Z",
+ "start_time": "2025-10-31T10:23:19.688484Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "@dataclass\n",
+ "class Impact:\n",
+ " unit: str\n",
+ " total: float\n",
+ " manufacturing: float\n",
+ " transport: float\n",
+ " use: float\n",
+ " end_of_life: float\n",
+ "\n",
+ "@dataclass\n",
+ "class Measure:\n",
+ " uid: str # client id\n",
+ " t: int # time of measurement\n",
+ " gwp: Impact\n",
+ " wu: Impact\n",
+ "\n",
+ "def collect_measures(workload: Workload) -> list[Measure]:\n",
+ " measures = []\n",
+ " for t, t_slice in enumerate(workload.slices):\n",
+ " dto = request_dto(t_slice)\n",
+ " dto_response = post(\"http://localhost:8080/virtual_machines/assess\", json=dto).json()\n",
+ " for vm_response in dto_response[\"virtual_machines\"]:\n",
+ " uid = vm_response[\"request\"][\"id\"]\n",
+ " gwp = Impact(\n",
+ " vm_response[\"impacts\"][\"GWP\"][\"total\"][\"unit\"],\n",
+ " vm_response[\"impacts\"][\"GWP\"][\"total\"][\"amount\"],\n",
+ " vm_response[\"impacts\"][\"GWP\"][\"per_lc_step\"][\"manufacturing\"][\"amount\"],\n",
+ " vm_response[\"impacts\"][\"GWP\"][\"per_lc_step\"][\"transport\"][\"amount\"],\n",
+ " vm_response[\"impacts\"][\"GWP\"][\"per_lc_step\"][\"use\"][\"amount\"],\n",
+ " vm_response[\"impacts\"][\"GWP\"][\"per_lc_step\"][\"end_of_life\"][\"amount\"],\n",
+ " )\n",
+ " wu = Impact(\n",
+ " vm_response[\"impacts\"][\"WU\"][\"total\"][\"unit\"],\n",
+ " vm_response[\"impacts\"][\"WU\"][\"total\"][\"amount\"],\n",
+ " vm_response[\"impacts\"][\"WU\"][\"per_lc_step\"][\"manufacturing\"][\"amount\"],\n",
+ " vm_response[\"impacts\"][\"WU\"][\"per_lc_step\"][\"transport\"][\"amount\"],\n",
+ " vm_response[\"impacts\"][\"WU\"][\"per_lc_step\"][\"use\"][\"amount\"],\n",
+ " vm_response[\"impacts\"][\"WU\"][\"per_lc_step\"][\"end_of_life\"][\"amount\"],\n",
+ " )\n",
+ " measures.append(Measure(uid, t, gwp, wu))\n",
+ " return measures\n"
+ ],
+ "id": "b7f99613affca8e1",
+ "outputs": [],
+ "execution_count": 103
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-31T10:24:37.941084Z",
+ "start_time": "2025-10-31T10:23:22.421897Z"
+ }
+ },
+ "cell_type": "code",
+ "source": "measures = collect_measures(workload)",
+ "id": "68ba5fe658c83c6b",
+ "outputs": [],
+ "execution_count": 104
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-31T10:24:58.805918Z",
+ "start_time": "2025-10-31T10:24:58.797939Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "import pickle\n",
+ "with open(\"ignored/measures.pickle\", \"wb\") as f:\n",
+ " pickle.dump(measures, f)"
+ ],
+ "id": "590855291db7555e",
+ "outputs": [],
+ "execution_count": 105
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": "## Visualization\n",
+ "id": "b18c5e2c9aad6bf7"
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-10-31T10:27:07.904101Z",
+ "start_time": "2025-10-31T10:27:07.895941Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "gwp_measures = list(map(lambda m: {\n",
+ " \"uid\": m.uid,\n",
+ " \"time\": m.t,\n",
+ " \"total\": m.gwp.total,\n",
+ " \"manufacturing\": m.gwp.manufacturing,\n",
+ " \"transport\": m.gwp.transport,\n",
+ " \"use\": m.gwp.use,\n",
+ " \"end_of_life\": m.gwp.end_of_life,\n",
+ "}, measures))\n",
+ "gwp_df = pd.DataFrame.from_records(gwp_measures)\n",
+ "gwp_df.head()"
+ ],
+ "id": "3eeb59135161f290",
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ " uid time total manufacturing transport use end_of_life\n",
+ "0 client_0 51 27.701328 23.455029 -0.0 4.246298 -0.0\n",
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+ "2 client_0 53 27.701328 23.455029 -0.0 4.246298 -0.0\n",
+ "3 client_0 54 27.701328 23.455029 -0.0 4.246298 -0.0\n",
+ "4 client_0 55 27.701328 23.455029 -0.0 4.246298 -0.0"
+ ],
+ "text/html": [
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