A Hybrid Bio-inspired Algorithm for Routing in Software Defined Networks

Abstract

Software-Defined Networking (SDN) is an emerging architecture that decouples the network control and forwarding functions for enabling programmability of the underlying infrastructure. In SDN, the control plane makes decisions, and the data plane forwards the packets as decided by the control plane. Traditional networks lack intelligence and are ineffective in providing QoS due to the tight coupling of planes. Thus, optimal route computation in these networks is challenging due to congestion on links, causing the inefficient utilization of resources. SDN helps in design of optimal routing algorithms compared to traditional networks due to the complete knowledge of topology and programmability. Recently, bio-inspired network systems have gained importance for solving challenging networking issues. Bio-inspired algorithms provide optimal routing solutions using nature's biological systems and organisms. In this work, we propose a dynamic routing mechanism to compute the optimal route using a hybrid bio-inspired optimization algorithm. A hybrid approach using Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) is used to compute an optimal path. Proposed algorithm is implemented in Mininet emulator using POX controller. The performance is evaluated with other algorithms using average throughput and delay for different scenarios. The results show that hybrid approach of route computation performs better than other approaches.