Simulate a Cloud-Based Load Balancer
Build a project that demonstrates traffic distribution, load balancing strategies, and backend health checks across cloud-hosted server replicas.Load balancers are essential in distributed systems to ensure reliability, scalability, and fault tolerance. Simulating one helps understand how cloud services like AWS ELB or NGINX handle traffic distribution under various conditions.
Build a load balancer simulation that accepts client requests and distributes them across multiple backend servers based on algorithms like round-robin, least connections, or weighted load balancing. Include backend failure detection and traffic rerouting.
Traffic Routing Logic
Implement round-robin, weighted, and random load balancing strategies using simple logic or HAProxy/NGINX.
Multiple Backend Servers
Simulate a set of Node.js or Python servers running in Docker containers or local ports.
Health Checks & Failover
Ping backend servers periodically; if one fails, reroute requests to available instances.
Live Monitoring Dashboard
Visualize request distribution, active connections, and health status of servers using a custom or Grafana dashboard.
The load balancer component listens for client traffic and forwards it to backend servers using a selected routing policy. Health checks are run at intervals to detect failing instances. Results are logged and visualized in real time.
- Frontend: Load balancer interface with config options
- Backend Servers: Node.js / Flask APIs hosted on separate ports or containers
- Balancing Algorithms: Round-robin, least connections, weighted random
- Monitoring: Prometheus + Grafana or custom logging
- Simulated Failure: Kill a backend to test rerouting
Backend Servers
Node.js (Express) / Python (Flask) apps exposing basic endpoints
Load Balancer
Custom Node.js/Flask script, or HAProxy / NGINX with configurable strategy
Monitoring
Prometheus for scraping server metrics, Grafana for visualization
Deployment
Docker Compose or Kubernetes to orchestrate containers and simulate cloud traffic
1. Launch Backend Servers
Create several mock backend servers that return server ID and timestamp on every request.
2. Implement Load Balancer Logic
Route incoming traffic to the backends using a selected algorithm; rotate IPs/ports as needed.
3. Add Health Check Functionality
Periodically ping servers. If one fails, remove it from the active list until it recovers.
4. Visualize Metrics
Display how traffic is distributed and which servers are live using a dashboard or logs.
5. Test Under Load
Use tools like Apache Benchmark or JMeter to simulate concurrent client requests and measure efficiency.
Learn Traffic Distribution in Real Time
Gain hands-on experience with backend orchestration, routing strategies, and fault recovery by simulating a complete cloud load balancer environment.
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