Predictive Control Plane Balancing in SD-IoT Networks Based on Elitism Genetic Algorithm and Non-Cooperative Game Theory

In the evolving landscape of Software Defined Internet of Thing (SD-IoT), the proliferation of IoT devices and applications has led to a drastic expansion of network traffic. Because of the overwhelming influx of control messages, the SDN controller may not have sufficient capacity to adequately address them. The main challenge is to properly deploy multiple controllers to enhance resource utilization and boost network performance, taking into account different factors for varying network scenarios. This paper presents a Predictive and Elitism genetic algorithm with Non-cooperative Game (PENG) strategy, tailored to address the control plane load imbalance. PENG incorporates a Gated Recurrent Units (GRU) based traffic prediction model, an improved elitism genetic algorithm, and the non-cooperative game theory to synergistically optimize the load balancing strategy. The study formulates a multi-objective optimization model that takes into account the degree of load balancing, control plane latency, and migration expenses as utility functions. This paper is structured around pivotal modules such as traffic prediction, controller overload identification, switch migration, and controller-switch mapping matrix reconfiguration. Moreover, an improved elitism genetic reallocation algorithm (IEGR) is designed, featuring a novel similarity factor to expedite convergence and improve the accuracy of identifying the optimal solution. Further, the detailed algorithm of PENG is present, outlining proactive and predictive switch migration to preemptively address potential load imbalance. The proposed methodology is simulated and the experimental results demonstrate that the proposal outperforms the comparisons in optimizing the load balancing degree, reducing average latency and migration cost.