A New Traffic Prediction Algorithm to Software Defined Networking

Abstract

Traffic prediction is significantly important for performance analysis and network planning in Software Defined Networking (SDN). However, to effectively predict network traffic in current networks is very difficult and nearly prohibitive. As a new cutting-edge network technology, SDN decouples the control and data planes of network switch devices to enable the flexibility of network measurements and managements. The SDN architecture of the flow-based forwarding idea brings forth a promising of network traffic capture and prediction. We propose a lightweight traffic prediction algorithm for SDN applications. Firstly, different from traditional network traffic measurements, our method uses the flow-based forwarding idea in SDN to extract traffic statistic from data plane. The statistical variable describes network flow information forwarded in SDN and enables more accurate measurements of flow traffic via a direct and low-overhead way compared with traditional traffic measurements. Secondly, based on the temporal nature of traffic, the time-correlation theory is utilized to model flow traffic, where the time-series analysis theory and regressive modeling approach are used to characterize network traffic in SDN. A fully new method is proposed to perform traffic prediction. Thirdly, we propose the flow-based forwarding traffic prediction algorithm to forecast to SDN traffic. The detailed algorithm process is discussed and analyze. Finally, sufficient experiments are presented and designed to validate the proposed method. Simulation results show that our method is feasible and effective.