Load balancing for Software Defined Network using Machine learning

Abstract

Software-Defined Networking is one of the most revolutionary and prominent technology in the field of networking. It solves the problem that our traditional network faces. Still it can face a problem of bottleneck and can be overloaded. To overcome this issue, various researcher has it given various works but they are based on two or three-parameter to perform load balancing and also they are static or dynamic. We have proposed an intelligent technique that forwards the packet i.e. TCP/UDP packet traffic based on several parameters (based on 12 parameters discussed in the latter part of this section). Based on these parameters, we have applied the trained machine using KMeans [1] and DBSCAN [2] clustering algorithm and also determine the optimal number of clusters. We have tested it on the huge number of packet that are 5000, 10000, 20000, 50000, 100000, 10000000.We have also compared there results of the KMeans and DBSCAN algorithm and also discussed researchers view