A One-Step Network Traffic Prediction

In the information society today computer networks are an indispensable part of people's life. Network traffic prediction is important to network planning, performance evaluation and network management directly. A variety of machine learning models such as artificial neural networks (ANN) and support vector machine (SVM) have been applied in traffic prediction. In this paper, a novel network traffic one-step-ahead prediction technique is proposed based on a state-of-the-art learning model called minimax probability machine (MPM). The predictive performance is tested on traffic data of Ethernet, experimental results show that the predictions of MPM match the actual traffics accurately and the proposed methods can increases the computational efficiency. Furthermore, we compare the MPM-based prediction technique with the SVM-based techniques. The results show that the predictive performance of MPM is competitive with SVM.