SDN-based Intelligent Network Controller
Built a software‑defined networking (SDN) controller to explore control‑plane programmability and performance trade‑offs.
- Learning Switch: Proactive MAC learning + flow pre-installation achieving 18.5 Gbit/s (≈670× reactive flood baseline).
- Topology Awareness: LLDP-based link discovery feeding dynamic adjacency graph.
- Routing: Dijkstra shortest paths with link weights derived from active delay measurements; installs per‑path flow rules.
- Loop Avoidance: Implemented STP-like pruning to prevent broadcast storms in cyclic graphs.
- Monitoring: Periodic counters + congestion heuristic to trigger path recomputation.
Results: Significant latency reduction and throughput gains under synthetic multi-host workloads; validated scalability on fat-tree emulations.
Tech: Python, Ryu, Mininet, OpenFlow.
Controller exposes a metrics endpoint exporting flow install latency and path recomputation intervals for evaluation.