Novel Traffic Classification Mechanism in Software Defined Networks with Experimental Analysis

Abstract

Recently, the usage of Internet through mobile devices has surpassed the access through wired network. It makes extremely difficult for the network operators to manage their network in resource efficient manner. Software Defined Networking (SDN), a key 5G enabling technology, makes network management easier for the network operators through logically centralized control plane. One of the benefits of SDN is specific treatment for different network traffic type, however it requires an efficient traffic classification solution. Comprehensive research has been done on deep packet inspection or machine learning based traffic classification methods which are unsuitable for delay intolerant networks due much overhead. This paper proposes a simple traffic classification mechanism using different IP addresses which are assigned to Mobile Node (MN). Different applications in MN uses one of the assigned IP addresses based on the type of network traffic they generate. Centralized SDN controller uses these IP addresses to classify the network traffic. This method is useful for various network functions such as load balancing, security and mobility. In this paper we take mobility as a use case and through emulation results establish that traffic classification significantly improves MN mobility performance.