A comprehensive, production-ready system demonstrating intelligent load balancing, real-time health monitoring, and interactive network visualization. Built with modern microservices architecture and container orchestration.
distributed-network-monitor/
├── load-balancer/ # Core load balancing logic
│ ├── main.py # Flask application
│ ├── algorithms.py # All load balancing algorithms
│ ├── health_check.py # Server health monitoring
│ └── config.py # Configuration management
├── server-nodes/ # Simulated backend servers
│ ├── shared/ # Common server code
│ └── node[1-3]/ # Individual server instances
├── monitoring-dashboard/ # React frontend
│ ├── src/
│ │ ├── components/ # Reusable UI components
│ │ ├── store/ # Redux state management
│ │ └── utils/ # API and helper functions
├── metrics-collector/ # Metrics aggregation
├── config/ # Service configurations
├── scripts/ # Utility scripts
└── docs/ # Documentation
# Clone and run
git clone https://github.com/yourusername/distributed-network-monitor.git
cd distributed-network-monitor
docker-compose up --build
# Access services:
# Dashboard: http://localhost:3000
# Load Balancer: http://localhost:5000
# Grafana: http://localhost:3001 (admin/admin)
# Prometheus: http://localhost:9090
Multiple Algorithms: Round Robin, Least Connections, Least Response Time, Weighted, IP Hash
Real-time Health Checks: Automatic server health monitoring
Adaptive Routing: Dynamic algorithm switching based on performance
Interactive Dashboard: Live network topology visualization
Server Metrics: CPU, memory, connections, response times
Traffic Analytics: Requests/sec, error rates, protocol distribution
Microservices: Docker containers with isolated concerns
Monitoring Stack: Prometheus + Grafana integration
High Availability: Auto-failover and health recovery
Hot Reloading: Automatic code reloading during development
Comprehensive Logging: Structured logs for debugging and auditing
API Documentation: Well-documented REST endpoints
Test Utilities: Traffic generation and load testing scripts
┌─────────────────────────────────────────────────────────────┐
│ Client Requests │
└───────────────────────┬─────────────────────────────────────┘
│
┌───────▼───────┐
│ Load │ ←─ Distributes traffic using
│ Balancer │ selected algorithm
└───────┬───────┘
│
┌───────────────┼───────────────┐
│ │ │
┌───▼───┐ ┌───▼───┐ ┌───▼───┐
│Server │ │Server │ │Server │ ←─ Simulated web
│ 1 │ │ 2 │ │ 3 │ servers with
└───────┘ └───────┘ └───────┘ varying loads
│ │ │
└───────────────┼───────────────┘
│
┌───────▼───────┐
│ Metrics │ ←─ Collects performance data
│ Collector │ from all components
└───────┬───────┘
│
┌───────────────┼───────────────┐
│ │ │
┌───▼───┐ ┌───▼───┐ ┌───▼───┐
│React │ │Prom- │ │Grafana│ ←─ Visualization
│Dash- │ │etheus │ │ │ and monitoring
│board │ └───────┘ └───────┘
└───────┘
- Load Balancer - Python (Flask), Redis
- Web Servers - Python (Flask), Gunicorn
- Dashboard - React, Redux, Socket.io
- Monitoring - Prometheus, Grafana
- Orchestration - Docker, Docker Compose
- Visualization - Recharts, D3.js
Request Ingestion: Client requests hit the load balancer
Algorithm Selection: LB selects server based on current algorithm
Health Verification: LB checks server health before forwarding
Request Processing: Server processes request and returns response
Metrics Collection: All components emit Prometheus metrics
Visualization: Dashboard updates in real-time via WebSocket
Alerting: System triggers alerts for anomalies
Request Rate: Requests per second across all servers
Response Times: 50th, 95th, 99th percentile latencies
Error Rates: HTTP error percentages by server
Resource Usage: CPU, memory, and connection counts
Algorithm Performance: Comparison of different strategies
Traffic Patterns: Geographic and protocol distributions
Network Overview: High-level system health
Server Performance: Individual server metrics
Load Balancer Analytics: Algorithm effectiveness
Traffic Analysis: Request patterns and distributions
GET / - API info
GET /request - Forward request through load balancer
GET /stats - Get load balancer statistics
GET /algorithm/{algo} - Change load balancing algorithm
GET /metrics - Prometheus metrics
GET / - Hello endpoint
GET /health - Health check
GET /metrics - Server metrics
GET /api/data - Sample data API
# Manual testing
curl http://localhost:5000/request
curl http://localhost:5000/stats
curl http://localhost:5000/algorithm/least_connections
# Generate test traffic
python scripts/generate_traffic.py --requests 1000 --concurrent 10
Requests/sec: Throughput monitoring
Response Time: 95th percentile tracking
Error Rate: HTTP error percentage
Server Health: CPU, memory, connection counts
Algorithm Efficiency: Load distribution quality
# Check logs
docker-compose logs -f
# Restart services
docker-compose restart
# Rebuild from scratch
docker-compose down -v
docker-compose up --build
# Check service health
curl http://localhost:5000/
curl http://localhost:5001/health
Server Unhealthy: When a server fails health checks
High Latency: Response time > 200ms
High CPU/Memory: Resource usage above thresholds
Traffic Spike: Sudden increase in request rate
Algorithm Inefficiency: Poor load distribution detected
Dashboard Alerts: Real-time notifications in the React UI
Grafana Alerts: Configurable alert rules with notifications
Prometheus Alerts: Rule-based alerting for critical issues
Throughput: ~800 RPS per server instance
Latency: < 50ms 95th percentile
Memory Usage: < 512MB per service
CPU Usage: < 30% under normal load
Load Balancing Algorithms: Theory and practical implementation
Microservices Architecture: Service decomposition and communication
Container Orchestration: Docker Compose for multi-service apps
Monitoring & Observability: Metrics collection and visualization
Real-time Web Applications: WebSocket communication
Production Best Practices: Health checks, logging, error handling