Controller placement in software-defined networks using reinforcement learning and metaheuristics

Controller placement in software-defined networks using reinforcement learning and metaheuristics

연구 분야: Artificial Intelligence

학회: Cluster Computing

Software-defined networking (SDN) has revolutionized network management by enabling dynamic control and optimization of network resources. A key challenge in SDN deployment is the strategic placement and assignment of controllers, which significantly affects network performance in terms of energy consumption, latency, and load balancing. This research addresses the controller placement problem by proposing a two-step method that combines enhanced reinforcement learning with an improved metaheuristic algorithm, termed Bedbug-GLA. In the first step, an irregular cellular learning automata model is developed to determine the optimal number of controllers required. In the second step, the Bedbug metaheuristic algorithm is employed to efficiently assign controllers to switches. Simulation results demonstrate that Bedbug-GLA achieves up to an 18% improvement in maximum controller load, a 69% reduction in congested controller overload, and a 20% decrease in energy consumption compared to state-of-the-art metaheuristic approaches, as evaluated on standard network topologies derived from real-world datasets.