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Quick Start Guide - AI-Enhanced VMM

🚀 One-Command Startup

Windows

start_all_services.bat

Linux/macOS

chmod +x start_all_services.sh
./start_all_services.sh

Docker (All Platforms)

docker-compose up --build

🔍 Verify System is Running

Check Services

Run Tests

# Test connectivity
python test_connectivity.py

# Validate system
python validate_system.py

# Simulate workload
python simulate_workload.py

🎯 Key Endpoints

AI Predictor (Port 5000)

# Health check
curl http://localhost:5000/health

# Get predictions
curl -X POST http://localhost:5000/predict \
  -H "Content-Type: application/json" \
  -d '{"recent_accesses": [1,2,3,4,5], "top_k": 5}'

# API docs
open http://localhost:5000/docs

C++ Backend (Port 8080)

# Get metrics
curl http://localhost:8080/metrics

# Start simulation
curl -X POST http://localhost:8080/simulate/start

# Stop simulation  
curl -X POST http://localhost:8080/simulate/stop

# Stream events
curl -N http://localhost:8080/events/stream

React Frontend (Port 3000)

  • Dashboard: http://localhost:3000
  • Real-time metrics and logs
  • AI prediction visualization
  • Control panel for simulations

🛠️ Troubleshooting

Stop All Services

# Windows
# Press Ctrl+C in each terminal

# Linux/macOS
./stop_all_services.sh

# Docker
docker-compose down

Check Port Usage

# Windows
netstat -an | findstr :5000
netstat -an | findstr :8080
netstat -an | findstr :3000

# Linux/macOS
lsof -i :5000
lsof -i :8080
lsof -i :3000

View Logs

# Docker
docker-compose logs -f

# Individual services
docker-compose logs -f predictor
docker-compose logs -f backend
docker-compose logs -f frontend

📊 Demo Script

Run the comprehensive demo:

python demo_script.py

This will:

  • Test all three services
  • Demonstrate AI predictions
  • Show system integration
  • Validate performance

🎓 For Teachers

What to Show

  1. AI Predictor API: http://localhost:5000/docs
  2. React Dashboard: http://localhost:3000
  3. Demo Script: python demo_script.py
  4. System Architecture: Microservices with AI integration

Key Talking Points

  • AI-enhanced virtual memory management
  • Machine learning for page prediction
  • Real-time monitoring and visualization
  • Production-ready implementation
  • Microservices architecture

🔧 Development

Manual Service Startup

# 1. AI Predictor
cd predictor
python -m uvicorn service:app --host 0.0.0.0 --port 5000

# 2. C++ Backend  
cd backend
mkdir build && cd build
cmake .. && make
./vmm_simulator

# 3. React Frontend
cd frontend
npm install
npm run dev

Testing Commands

# Test AI predictions
curl -X POST http://localhost:5000/predict \
  -H "Content-Type: application/json" \
  -d '{"recent_accesses": [1,2,3,4,5], "top_k": 5}'

# Test backend metrics
curl http://localhost:8080/metrics

# Test SSE streaming
curl -N http://localhost:8080/events/stream

📈 Performance Monitoring

Real-time Metrics

  • Page fault rate
  • AI prediction accuracy
  • Processing latency
  • Memory usage
  • System throughput

Access via:

🚨 Common Issues

  1. Port already in use

    • Kill processes using ports 3000, 5000, 8080
    • Use netstat or lsof to find processes

  2. Services not starting

    • Check Docker is running
    • Verify all dependencies installed
    • Check logs for errors

  3. Frontend not loading

    • Ensure backend is running on port 8080
    • Check browser console for errors
    • Verify CORS settings

  4. AI predictions failing

    • Check predictor service is running
    • Verify model is loaded
    • Check network connectivity

📞 Support

If you encounter issues:

  1. Check the troubleshooting section above
  2. Run python test_connectivity.py
  3. Check service logs
  4. Verify all ports are available