A Multi-controller Load Balancing Strategy for Software Defined WiFi Networks

Software DefinedWiFi networks (SD-WiFi) support scalable network control functions, flexible resource allocation and changes in traffic. But the load balancing in SD-WiFi is challenging due to involvement of numerous users in the network. In this paper, we propose an efficient algorithm approach to achieve load balancing in SD-WiFi architecture. The user generated traffic arrives at WiFi access points (APs), which is classified into high prioritized (HP) flows and low prioritized (LP) flows, based on flow size and delay constraint values using support vector machine (SVM). Controllers are organized as two-tier: global controller (GC) and local controllers (LC). Markov Chain Model (MCM) is employed with two transition states as overloaded and underloaded in GC to predict future load of LCs based on the current load. The optimal underloaded LC for flow migration is selected by using Type-2 Fuzzy based Particle Swarm Optimization (TFPSO) algorithm. We conducted extensive simulation experiments to evaluate the performance of the proposed scheme using OMNeT++ simulator. The proposed scheme outperforms flow stealer scheme by a 33% increase in throughput and 70% in workload performance. In comparison to MPSO-CO scheme the proposed scheme exhibits better latency results.