Automatic Delay-Sensitive Applications Quality of Service Improvement with Deep Flows Discrimination in Software Defined Networks

Abstract

Nowadays, Internet applications with different network resource requirements have been growing exponentially; therefore, the network equipment needs to be configured consistently based on application resource requirements. In this paper, Software Defined Network (SDN) is used to make the network more programmable, flexible and agile to develop the proposed model. Flow discrimination and optimized resource allocation are significant and challenging items in providing the required network resources for each flow. In this paper, we propose a model composed of (1) network flows' types discriminator and (2) the optimized resource allocation based on the flow classes. The applications are clustered into four groups according to the network resource requirements, and a deep network traffic discriminator is used for classification. The greedy algorithm is also used for optimized resource allocation. The proposed model is developed in Mininet with a Pox controller to prove Quality of Service (QoS) improvement paralleled with maximized utilization. The simulation results show that the proposed model has intelligent behaviour in network resource allocation based on flows' requirements compared to Spanning Tree Protocol (STP) and Dynamic and Adaptive Multipath Routing (DAMR) while maximizing the network utilization.