GAMR: Revolutionizing Multi-Objective Routing in SDN Networks With Dynamic Genetic Algorithms

Abstract

The growing complexity of modern network systems has increased the need for efficient multi-objective routing (MOR) algorithms. However, existing MOR methods face significant challenges, particularly in terms of computation time, which becomes problematic in networks with short-lived tasks where rapid decision-making is essential. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) offers a promising approach to addressing these challenges. Nevertheless, directly applying NSGA-II in dynamic network environments, where states frequently change, is impractical. This paper presents GAMR, an enhanced non-dominated sorting Genetic Algorithm II-based dynamic multi-objective QoS routing algorithm, which leverages QoS metrics for its multi-objective function. Introducing novel initialization and crossover strategies, our approach efficiently identifies optimal solutions within a brief runtime. Implemented within a Software-defined Network controller for routing, GAMR outperforms existing multi-objective algorithms, exhibiting notable improvements in performance indicators. Specifically, enhancements range from 3.4% to 22.8% on the Hypervolume metric and from 33% to 86% on the Inverted Generational Distance metric. In terms of network metrics, experimental results demonstrate significant reductions in forwarding delay and packet loss rate to 41.25 ms and 3.9%, respectively, even under challenging network configurations with only 2 servers and up to 100 requests.