Research Article
Load Balancing in Software-Defined Networks Based on Particle Swarm Optimization
@INPROCEEDINGS{10.1007/978-3-031-65126-7_39, author={Haiyan Zhang and Liren Zou and Yilong Xie}, title={Load Balancing in Software-Defined Networks Based on Particle Swarm Optimization}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 19th EAI International Conference, QShine 2023, Shenzhen, China, October 8 -- 9, 2023, Proceedings, Part I}, proceedings_a={QSHINE}, year={2024}, month={8}, keywords={SDN Load Balancing Particle Swarm Optimization}, doi={10.1007/978-3-031-65126-7_39} }- Haiyan Zhang
Liren Zou
Yilong Xie
Year: 2024
Load Balancing in Software-Defined Networks Based on Particle Swarm Optimization
QSHINE
Springer
DOI: 10.1007/978-3-031-65126-7_39
Abstract
Nowadays, as Software-Defined Networking (SDN) gains prominence, Load Balancing (LB) for SDN assumes significant importance. By allocating network traffic among resources efficiently, LB ensures that no individual resource is burdened, thereby optimizing the overall performance. Based on the analysis of SDN Flow network forwarding mechanism, this paper applies Particle Swarm Optimization (PSO) algorithm to the data center’s traffic scheduling based on load balancing. First, the SDN load balancing problem is abstracted as an integer Linear programming model, which maximizes the average link bandwidth utilization on the basis of ensuring network delay. PSO algorithm is applied to optimize the load balancing problem, and the optimization algorithm is run in the SDN controller. The simulation experiment by Mininet shows that the SDN load balancing algorithm based on Particle swarm optimization can effectively balance the network load and improve the network performance.
