摘要
A novel methodology for prediction of network traffic, WPANFIS, which relies on wavelet packet transform （WPT） for multi-resolution analysis and adaptive neuro-fuzzy inference system （ANFIS） is proposed in this article. The widespread existence of self-similarity in network traffic has been demonstrated in earlier studies, which exhibits both long range dependence （LRD） and short range dependence （SRD）. Also, it has been shown that wavelet decomposition is an effective tool for LRD decorrelation. The new method uses WPT as extension of wavelet transform which can decoorrelate LRD and make more precisely partition in the high-frequency section of the original traffic. Then ANFIS which can extract useful information from the original traffic is implemented in this study for better prediction performance of each decomposed non-stationary wavelet coefficients. Simulation results show that the proposed WPANFIS can achieve high prediction accuracy in real network traffic environment.
A novel methodology for prediction of network traffic, WPANFIS, which relies on wavelet packet transform （WPT） for multi-resolution analysis and adaptive neuro-fuzzy inference system （ANFIS） is proposed in this article. The widespread existence of self-similarity in network traffic has been demonstrated in earlier studies, which exhibits both long range dependence （LRD） and short range dependence （SRD）. Also, it has been shown that wavelet decomposition is an effective tool for LRD decorrelation. The new method uses WPT as extension of wavelet transform which can decoorrelate LRD and make more precisely partition in the high-frequency section of the original traffic. Then ANFIS which can extract useful information from the original traffic is implemented in this study for better prediction performance of each decomposed non-stationary wavelet coefficients. Simulation results show that the proposed WPANFIS can achieve high prediction accuracy in real network traffic environment.
基金
supported by the National Basic Research Program of China (2007CB310701)
Research Fund for University Doctor Subject (20070013013)
Chinese Universities Scientific Fund (2009RC0124)
