User traffic profiling in a Software Defined Networking Context

Traffic classification and statistical trend analysis are critical steps for workload characterization, capacity planning and network policy configuration in computer networks. Additionally application level traffic classification aids in profiling user traffic based on application usage trends. However, user traffic profiling integration in real-time network resource management remains challenging due to variation in user traffic behaviour, requiring repeated manual configuration updates in traditional fixed topology networks. Software defined networks (SDN) on the other hand, due to their centralized control and real-time programmability of network elements, may offer a potential avenue for application based user traffic profiles to effectively allocate and control network resources. In this paper we evaluate the accuracy of developing meaningful user traffic profiles from application usage trends based on traffic flow analysis using k-means clustering algorithm and explore their applicability to software defined networks for real-time traffic management. The results show a considerable variation in application usage trends and associated network statistics among user traffic profiles leading to further propose implementing per profile flow metering and re-routing of resource intensive traffic profiles via different links for effective real-time network resource management in software defined networks.