Cloud Evo Unified

ACO-Driven Intelligent Cloud Resource Allocation — a Streamlit-based cloud infrastructure simulator combining Ant Colony Optimization (ACO) with Genetic Algorithm (GA) mutation, backed by a 7-table SQLite schema.

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Overview

Cloud Evo Unified simulates a full cloud environment — users, servers, tasks, network links, routing, and traffic — then layers an ACO+GA intelligence engine on top to make adaptive, real-time workload allocation decisions. The engine's predictions are evaluated against a deterministic ground truth using precision/recall/F1 and a confusion matrix.

Features

Cloud Infrastructure Simulator

• User, Server, and Task management (CRUD)
• Network topology with Dijkstra & Floyd-Warshall routing
• Live traffic monitoring and resource metrics dashboards
• Availability monitor, distance/hop calculator, API output viewer

ACO Intelligence Layer

• Ant Colony Optimization engine (pheromone-based path selection)
• Genetic Algorithm mutation layer to prevent premature convergence
• Resource allocation & load balancing driven by a weighted composite score
• Server ranking and pheromone trail visualization

Evaluation & Reporting

• OS Evolutionary Comparison across 5 OS types (Linux, Windows Server, FreeBSD, Ubuntu, CentOS)
• Parameter comparison across 3 experimental trials
• Confusion matrix / model evaluation (precision, recall, F1, accuracy)
• Dataset collection pipeline (CSV export) and consolidated ACO report

Tech Stack

Layer	Technology
Frontend/UI	Streamlit (multi-page app)
Visualization	Plotly Express
Data handling	Pandas, NumPy
Database	SQLite (7-table relational schema)
Algorithms	ACO, GA mutation, Dijkstra, Floyd-Warshall

Setup & Installation

Prerequisites

• Python 3.9+
• pip

Steps

# 1. Clone the repository
git clone https://github.com/verrolinajacob/aco-cloud-resource-allocator.git
cd aco-cloud-resource-allocator/cloud_evo_unified

# 2. (Recommended) Create a virtual environment
python -m venv venv
venv\Scripts\activate        # Windows
source venv/bin/activate     # macOS/Linux

# 3. Install dependencies
pip install -r requirements.txt

# 4. Run the app
streamlit run app.py