A One-Step Network Traffic Prediction

Authors:
Xiangyang Mu;Nan Tang;Weixin Gao;Lin Li;Yatong Zhou
Affiliations:
School of Electrical Engineering, Xi'an Shiyou University, Xi'an, China 710065;School of Electrical Engineering, Xi'an Shiyou University, Xi'an, China 710065;School of Electrical Engineering, Xi'an Shiyou University, Xi'an, China 710065;School of Electrical Engineering, Xi'an Shiyou University, Xi'an, China 710065;School of Information Engineering, Hebei University of Technology, Tianjin, China 300401
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Year:
2008

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Abstract

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.