A new quantum particle swarm optimization algorithm for controller placement problem in software-defined networking. (October 2021)

• Record Type: Journal Article
• Authors: Zhang, Quanyuan; Li, Haolun; Liu, Yanli; Ouyang, Shangrong; Fang, Caiting; Mu, Wentao; Gao, Hao
• Is Part Of: Computers & electrical engineering. Volume 95(2021)
• Publication Date: 2021-10
• Subjects: Software-defined networking -- Controller placement problem -- Quantum particle swarm algorithm -- Convergence rate; 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.2021.107456
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: As a new network control and management method for network, software-defined networking (SDN) algorithms have attracted more attention to make networks agile and flexible. To meet the requirements of users and conquer the physical limitation of network, it is necessary to design an efficient controller placement mechanism of SDN, which is defined as an optimization problem to determine the proper positions and number of its controllers. As a modern optimization tool, Quantum-behavior particle swarm optimization (QPSO) algorithm demonstrates power fast convergence rate but limits in global search ability. In this paper, by introducing a full search history and excellent dimension update strategy into the traditional QPSO algorithm which enhances its performance, simulation results show that the proposed algorithm achieves better performance in dozens of different multi-controller placement problems.