A Controller Placement Algorithm Using Ant Colony Optimization in Software-Defined Network

Software Defined Network (SDN) which is labeled as a new network archetype that decouples the data plane and control plane is capable to solve today’s network problems and improve network performance. Yet, among numerous challenges and research openings in software-defined networks, Controller Placement Problem (CPP) is supposed to be the most vital issue which can directly affect the whole network’s performance. In this thesis, we deliver a comprehensive review of various metaheuristic CPP-optimized models in SDNs. In this regard, we propose a methodology named Ant Colony Optimization Controller Placement (ACO-CP), to solve the optimal controller location. Ant Colony Optimization is a population-based meta-heuristic algorithm proposed for the optimal location of the controllers, which takes a precise set of the objective function and returns the best potentially location out of the possible set of locations. The objective function is defined by considering, the average and maximum controller-to-controller, switch-to-controller latency, and load balance. By comparing the network performance our proposed algorithm, provides better performance compared with Pareto Simulated Annealing and k-medoid method, Specially on its overall global latency and controller-to-controller latency.