Long-term traffic flow prediction using multivariate SSA forecasting in SDN based networks

Abstract

Accurate and real-time traffic flow forecasting plays an important role in optimizing traffic routing enabling adaptive and sophisticated applications on the network. Managing and routing enormous traffic flow with dynamic behavior is a highly challenging task. However, arriving at a precise model for traffic forecasting in a short interval of time is not trivial because of the dynamic nature of traffic flow. A novel multivariate time series framework is designed to analyze and forecast the dynamic traffic flow in SDN based networks. The proposed framework adapts the Multivariate Singular Spectrum Analysis (MSSA) forecasting model and incorporates the Randomized Singular Value Decomposition (RSVD) to improve the accuracy of flow prediction. Simulations are conducted to evaluate the effectiveness of the proposed MSSA method. The proposed method predicts the long-term traffic fluctuation from the observed traffic traces. The SDN controller is trained using the traffic traces and future traffic flows are forecasted. The performance evaluation of the proposed method predicts real-time traffic trends accurately with 2.2% MAPE, 9.44 MAE and 13.803 RMSE. The results show that the learning ability of MSSA helps to forecast future network traffic with low prediction errors.