An adaptive heuristic for multi-objective controller placement in software-defined networks

Software-defined networking paradigm faces many challenges, including reliability, resiliency, scalability, and availability. These challenges can be tackled by carefully selecting placements within the network. However, the evaluation of all placements is only practical for small networks. In this paper, a fast and efficient adaptation of evolutionary algorithms is presented to solve large-scale multi-objective controller placement problems. The presented algorithm requires reasonable memory resource and enjoys a greedy heuristic to generate a high-quality initial population, smart mechanisms to encourage the diversification and intensification, and a new fast Pareto finder. Moreover, a new variant of the problem is developed in which the capacities of controllers and loads of switches are added as constraints. A new constraint handling technique is applied to adapt our algorithm to solve the new problem. Finally, the results on several topologies from Internet Topology Zoo revealed that our presented algorithms outperformed some other efficient algorithms from the literature.