Feature/parallelism

.gitignore

@@ -14,3 +14,5 @@ local
 
 **/lcaac-tmp.yaml
 **/application-tmp.yaml
+**/.venv
+experiments/performance/.ipynb_checkpoints

README.md

@@ -94,6 +94,18 @@ The request takes the following form:
       "amount": 1.0,
       "unit": "hour"
    },
+   "total_vcpu": {
+      "amount": 100.0,
+      "unit": "vCPU"
+   },
+   "total_ram": {
+      "amount": 100.0,
+      "unit": "GB"
+   },
+   "total_storage": {
+      "amount": 100.0,
+      "unit": "TB"
+   },
    "virtual_machines": [
       {
          "id": "vm-01",

build.gradle.kts

@@ -11,7 +11,7 @@ plugins {
 }
 
 group = "org.cloud_assess"
-version = "1.8.2"
+version = "2.0.0"
 
 java {
     sourceCompatibility = JavaVersion.VERSION_17
@@ -30,7 +30,7 @@ repositories {
 }
 
 dependencies {
-    val lcaacVersion = "1.7.12"
+    val lcaacVersion = "1.8.0"
     implementation("ch.kleis.lcaac:core:$lcaacVersion")
     implementation("ch.kleis.lcaac:grammar:$lcaacVersion")
 

docker-compose.yaml

@@ -1,6 +1,6 @@
 services:
   app:
-    image: "ghcr.io/kleis-technology/cloud-assess/cloud-assess-app:v1.8.3"
+    image: "ghcr.io/kleis-technology/cloud-assess/cloud-assess-app:v2.0.0"
     container_name: cloud-assess-app
     ports:
     - "8080:8080"

experiments/performance/.gitignore

@@ -0,0 +1 @@
+*.png

experiments/performance/.python-version

@@ -0,0 +1 @@
+3.13

experiments/performance/README.md

@@ -0,0 +1,7 @@
+# README
+
+## Run Jupyter
+```
+uv run --with jupyter jupyter lab
+```
+

experiments/performance/pyproject.toml

@@ -0,0 +1,11 @@
+[project]
+name = "perfanalysis"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "matplotlib>=3.10.5",
+    "numpy>=2.3.2",
+    "requests>=2.32.4",
+]

experiments/performance/server_data_creator.ipynb

@@ -0,0 +1,101 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c35fd21b-7cc2-408f-84fc-f7f99de9c7d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CSV file 'workers.csv' created with 2000 workers.\n",
+      "CSV file 'workers_impacts.csv' created with 2000 hardware entries.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import csv\n",
+    "import random\n",
+    "\n",
+    "def generate_workers_csv(n, filename=\"workers.csv\"):\n",
+    "    header = [\n",
+    "        \"id\",\"pool_id\",\"dc_id\",\"power\",\"lifespan\",\"hdd_quantity\",\"hdd_unit_size\",\n",
+    "        \"ssd_quantity\",\"ssd_unit_size\",\"ram_quantity\",\"ram_unit_size\",\"gpu_quantity\",\n",
+    "        \"gpu_model_name\",\"fan_quantity\",\"psu_total_weight\",\"load_rate\",\"comment\",\"quantity\"\n",
+    "    ]\n",
+    "    \n",
+    "    with open(filename, mode=\"w\", newline=\"\") as file:\n",
+    "        writer = csv.writer(file)\n",
+    "        writer.writerow(header)\n",
+    "        \n",
+    "        for i in range(n):\n",
+    "            row = [\n",
+    "                f\"worker-{i}\",         # id\n",
+    "                \"worker\",              # pool_id\n",
+    "                \"fr01\",                # dc_id\n",
+    "                random.uniform(50, 70), # power (random float)\n",
+    "                1,                      # lifespan\n",
+    "                \"\", \"\",                  # hdd_quantity, hdd_unit_size\n",
+    "                \"\", \"\",                  # ssd_quantity, ssd_unit_size\n",
+    "                random.uniform(30, 64), # ram_quantity (GB)\n",
+    "                \"GB\",                   # ram_unit_size\n",
+    "                \"\", \"\",                  # gpu_quantity, gpu_model_name\n",
+    "                \"\", \"\",                  # fan_quantity, psu_total_weight\n",
+    "                \"\", \"\",                  # load_rate, comment\n",
+    "                1                        # quantity\n",
+    "            ]\n",
+    "            writer.writerow(row)\n",
+    "    \n",
+    "    print(f\"CSV file '{filename}' created with {n} workers.\")\n",
+    "\n",
+    "def generate_workers_impact_csv(n, filename=\"workers_impacts.csv\"):\n",
+    "    header = [\n",
+    "        \"hw_id\",\"lc_step\",\"ADPe\",\"ADPf\",\"AP\",\"CTUe\",\"CTUh_c\",\"CTUh_nc\",\n",
+    "        \"Epf\",\"Epm\",\"Ept\",\"GWP\",\"GWPf\",\"GWPlu\",\"IR\",\"LU\",\"ODP\",\"PM\",\n",
+    "        \"POCP\",\"WU\",\"TPE\",\"GWPb\"\n",
+    "    ]\n",
+    "    \n",
+    "    with open(filename, mode=\"w\", newline=\"\") as file:\n",
+    "        writer = csv.writer(file)\n",
+    "        writer.writerow(header)\n",
+    "        \n",
+    "        for i in range(n):\n",
+    "            row = [\n",
+    "                f\"worker-{i}\",              # hw_id\n",
+    "                \"manufacturing\",        # lc_step\n",
+    "                *[random.uniform(0, 100) for _ in range(len(header) - 2)]\n",
+    "            ]\n",
+    "            writer.writerow(row)\n",
+    "    \n",
+    "    print(f\"CSV file '{filename}' created with {n} hardware entries.\")\n",
+    "\n",
+    "# Example usage\n",
+    "generate_workers_csv(2000)\n",
+    "generate_workers_impact_csv(2000)"
+   ]
+  }
+ ],
+ "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": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}