enhancing quality of service in sdns through pareto-optimized controller placement using ns-mf algorithm

Software-defined networks (sdn) have emerged as a new paradigm to overcome rigidity in traditional networks. sdn controllers manage network switches through a centralized control plane. strategically placing controllers is vital for meeting performance needs. we model the np-hard controller placement problem (cpp) as multi-objective optimization reconciling switch-controller latency, resilience to failures, inter-controller coordination overhead and load balancing. a customized non-dominated sorting moth flame algorithm (ns-mf) with novel recombination and perturbation techniques is proposed to effectively approximate the pareto-optimal set of placements on large problem instances. ns-mf is benchmarked on a diverse corpus of 41 topologies against the exhaustive poco solver, assessing computational time and solution quality tradeoffs. compared to poco, the proposed algorithm attains over 20x speedup for the largest graphs with an average optimality gap within 0.8%. the proposed ns-mf demonstrates superior performance over state-of-the-art metaheuristics (nsga-ii and psa) in reconciling proximity and diversity objectives when estimating pareto-optimal fronts. experimental results substantiate ns-mf's efficacy in effectively navigating objectives pertinent to resilient sdn design.