An adaptive heuristic for multi-objective controller placement in software-defined networks. (February 2018)

• Record Type: Journal Article
• Authors: Ahmadi, Vahid; Khorramizadeh, Mostafa
• Is Part Of: Computers & electrical engineering. Volume 66(2018)
• Page Start: 204
• Page End: 228
• Publication Date: 2018-02
• Subjects: Software-defined network -- Controller placement -- Multi-objective combinatorial optimization -- Heuristic algorithms -- Pareto front -- Multi-start hybrid non-dominated sorting genetic algorithm; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.12.043
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: 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.