Virtual Memory Manager

An AI-enhanced Virtual Memory Manager (VMM) simulator with real-time dashboard visualization.

Overview

This project implements a comprehensive Virtual Memory Management system with three main components:

1. C++ Backend Simulator - Core VMM logic with HTTP API
2. Python AI Predictor - Machine learning-based page prediction service
3. React Frontend Dashboard - Real-time visualization and control interface

Features

• Virtual Memory Management: Page tables, page replacement algorithms (FIFO, LRU, Clock)
• AI-Enhanced Predictions: Machine learning models for page prefetching and eviction hints
• Real-time Dashboard: Live metrics, charts, and simulation control
• Multiple Workload Types: Sequential, Random, Strided, DB-like access patterns
• Server-Sent Events: Real-time log streaming and event updates

Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   React Frontend│    │  C++ Backend    │    │ Python AI       │
│   (Port 3000)   │◄──►│  (Port 8080)    │◄──►│ Predictor       │
│                 │    │                 │    │ (Port 5000)     │
└─────────────────┘    └─────────────────┘    └─────────────────┘

Quick Start

Prerequisites

• C++ Compiler (MSVC on Windows, GCC/Clang on Linux/macOS)
• CMake (3.10 or higher)
• Python 3.8+ with pip
• Node.js 16+ with npm

1. Start AI Predictor

cd predictor
pip install -r requirements.txt
python -m uvicorn predictor.service:app --host 0.0.0.0 --port 5000

2. Build and Start C++ Backend

cd backend
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
cmake --build . --config Release
cd bin/Release
./vmm_simulator.exe  # Windows
# or
./vmm_simulator      # Linux/macOS

3. Start React Frontend

cd frontend
npm install
npm run dev

4. Access Dashboard

Open your browser and navigate to:

• Frontend Dashboard: http://localhost:3000
• Backend API: http://localhost:8080
• AI Predictor: http://localhost:5000

API Endpoints

Backend (Port 8080)

• GET /metrics - Get simulation metrics
• POST /simulate/start - Start simulation
• POST /simulate/stop - Stop simulation
• GET /events/stream - Server-Sent Events stream

AI Predictor (Port 5000)

• GET /health - Health check
• POST /predict - Get page predictions
• GET /model/info - Model information

Docker Deployment

For easy deployment, use Docker Compose:

docker-compose up --build

This will start all three services with proper networking and health checks.

Configuration

VMM Configuration

VMMConfig vmm_config{
    .total_frames = 256,           // Number of physical frames
    .page_size = 4096,             // Page size in bytes
    .total_pages = 1024,           // Total virtual pages
    .replacement_policy = ReplacementPolicy::CLOCK,
    .enable_ai_predictions = true,
    .ai_predictor_url = "http://localhost:5000/predict"
};

Workload Configuration

WorkloadConfig workload_config{
    .type = WorkloadType::RANDOM,   // Workload pattern
    .total_requests = 1000,        // Number of memory accesses
    .page_range = 1000,            // Range of page numbers
    .stride = 1,                   // Stride for strided access
    .zipf_alpha = 1.0,            // Zipf distribution parameter
    .locality_factor = 0.8,        // Locality factor for webserver workload
    .working_set_size = 100        // Working set size
};

Development

Building from Source

1. Backend:

   cd backend
   mkdir build && cd build
   cmake .. -DCMAKE_BUILD_TYPE=Release
   cmake --build . --config Release

2. Frontend:

   cd frontend
   npm install
   npm run build

3. AI Predictor:

   cd predictor
   pip install -r requirements.txt

Testing

Run the connectivity test:

python test_connectivity.py

Run the system validation:

python validate_system.py

Project Structure

Virtual-Memory-Manager/
├── backend/                 # C++ VMM Simulator
│   ├── src/               # Source code
│   ├── include/          # Headers
│   ├── build/            # Build output
│   └── CMakeLists.txt    # Build configuration
├── frontend/              # React Dashboard
│   ├── src/              # React components
│   ├── public/           # Static assets
│   └── package.json      # Dependencies
├── predictor/            # Python AI Service
│   ├── models/           # ML models
│   ├── service.py        # FastAPI app
│   └── requirements.txt  # Python dependencies
├── docker-compose.yml    # Container orchestration
└── README.md            # This file

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Authors

• Aditya Pandey - Initial work and development

Acknowledgments

• Virtual Memory Management concepts and algorithms
• React and FastAPI frameworks
• Machine Learning for page prediction
• Real-time web technologies (SSE, WebSockets)