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Genetic Algorithms with Variant Particle Swarm Optimization Based Mutation for Generic Controller Placement in Software-Defined Networks

Authors :
Lingxia Liao
Victor C. M. Leung
Zhi Li
Han-Chieh Chao
Source :
Symmetry, Vol 13, Iss 7, p 1133 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

To enable learning-based network management and optimization, the 5th Generation Mobile Communication Technology and Internet of Things systems usually involve software-defined networking (SDN) architecture and multiple SDN controllers to efficiently collect the big volume of runtime statistics, define network-wide policies, and enforce the policies over the whole network. To better plan the placement of controllers over SDN systems, this article proposes a generic controller placement problem (GCP) that considers the organization and placement of controllers as well as the switch attachment to optimize the delay between controllers and switches, the delay among controllers, and the load imbalance among controllers. To solve this problem without losing generality, a novel multi-objective genetic algorithm (MOGA) with a mutation based on a variant Particle Swarm Optimization (PSO) is proposed. This PSO chooses a global best position for a particle according to a pre-computed global best position set to lead the mutation of the particle. It successfully handles multiple conflicting objectives, fits the scenario of mutation, and can apply in many other flavors of MOGAs. Evaluations over 12 real Internet service provider networks show the effectiveness of our MOGA in reducing convergence time and improving the diversity and accuracy of the Pareto frontiers. The proposed approaches in formulating and solving the GCP in this article are general and can be applied in many other optimization problems with minor modifications.

Details

Language :
English
ISSN :
20738994
Volume :
13
Issue :
7
Database :
Directory of Open Access Journals
Journal :
Symmetry
Publication Type :
Academic Journal
Accession number :
edsdoj.0b2913d6a06a42caa3c3651af4157691
Document Type :
article
Full Text :
https://doi.org/10.3390/sym13071133