Optimization of Routing using Traffic Classification in Software Defined Networking

Efficient routing is an essential task for any network as it directly impacts the network's performance. In this paper, we have used traffic classification techniques to optimize routing in Software Defined Network (SDN) and provided a cost aware routing framework. Instead of classifying traffic based on a particular parameter, we perform the traffic classification using a hybrid approach that is based on machine learning techniques. Our framework uses supervised machine learning techniques to classify network flow and detect elephant flows which are too heavy in size and require significant bandwidth. As the requirements of each elephant flow vary with the application, we further perform Quality of Service (QoS) based traffic classification, which classifies these elephant flow into QoS classes as per their QoS requirements. For this purpose, we have used semi supervised machine learning algorithms. Further, we have proposed a routing algorithm that makes the use of Dijkstra algorithm to compute the best possible shortest path based on the QoS requirements of the elephant flow. The proposed method was implemented and tested on Mininet using a POX controller. The simulation results show that our framework successfully classifies elephant flows with an accuracy of 80% and also computes a low-cost path for each elephant flow based on the actual internet data set.