Ant Colony Optimization for Distributed Routing Path Optimization in Optical Burst-Switched Networks

This work describes a distributed framework for routing path optimization in Optical Burst-Switched (OBS) networks that loosely mimics the foraging behaviour of ants observed in nature, which in the past has originated the Ant Colony Optimization (ACO) metaheuristic. The framework consists of additional data structures stored at the nodes and of special control packets that traverse the network, estimate the goodness of their paths and update accordingly the data structures of the nodes. The performance of the framework, which has been implemented on an event-driven OBS network simulator, is evaluated on several network topologies and compared with that obtained with centralized routing path optimization. The simulation results show that the distributed routing path selection framework significantly improves the performance of OBS networks by reducing data loss and that it attains a performance comparable to that of the centralized algorithm. The results also suggest that the framework is robust to changes in its parameters. Keywords—Ant colony optimization, optical burst switching, routing path optimization, swarm intelligence.