Route optimization in Software Defined Network for Reliable data delivery using Sail fish optimizer, Wild Geese optimizer and Aquila optimizer

Abstract

The reliability of data delivery in Software-Defined Networks (SDN) is essential, particularly for real-time and large-scale applications. This research compares and contrasts three meta-heuristic algorithms for route optimization in an SDN environment: Sailfish Optimization (SFO), Aquila Optimization (AO), and Wild Geese Optimization (WGO). SFO uses the cooperative hunting behaviors of sailfish and aims to balance exploration and exploitation phases for determining optimal paths for multipath routing. WGO draws inspiration from the migrating geese that utilize their collective intelligence for obtaining the stability of paths. WGO aims to handle route congestions and ensure the stability of the routes. On the other hand, AO is inspired by the hunting behavior of the bird known as Aquila. AO achieves a balance between the exploration and exploitation phases because of its dynamic search process such that it enhances network efficiency through route optimization. Our study performs simulations process using Mininet simulator. The simulation test and compare alternatives based on key factors such as throughput, bandwidth utilization, delay, and computation time. The assessment result indicates that WGO outperforms both SFO and AO with a throughput of 272.11 Gbps, latency of 0.42 ms, processing time of 2.96 sec, and bandwidth use of 29.19 mbps. The simulation provides that WGO achieves better throughput and bandwidth utilization with reduced delay and computation time with regard to route optimization in an SDN environment. The paper also gives valuable understanding towards selecting the best meta-heuristic optimization algorithm that is suitable for robust and effective multipath routing in SDN and enhances SDN performance and its reliability.