中国物理B ›› 2009, Vol. 18 ›› Issue (11): 4760-4768.doi: 10.1088/1674-1056/18/11/026

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Network traffic prediction by a wavelet-based combined model

孙韩林1, 金跃辉1, 程时端1, 崔毅东2   

  1. (1)State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; (2)State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China;School of Information and Telecommunication Engineering, Beijing University of Posts and Telecommunications, Beijing
  • 收稿日期:2009-02-23 修回日期:2009-03-20 出版日期:2009-11-20 发布日期:2009-11-20
  • 基金资助:
    Project supported by National Basic Research Program of China (Grant Nos 2009CB320505 and 2009CB320504) and National High Technology Research and Development Program of China (Grant Nos 2006AA01Z235, 2007AA01Z206 and 2009AA01Z210).

Network traffic prediction by a wavelet-based combined model

Sun Han-Lin(孙韩林)a)†, Jin Yue-Hui(金跃辉)a), Cui Yi-Dong(崔毅东)a)b),and Cheng Shi-Duan(程时端)a)   

  1. a State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; b School of Information and Telecommunication Engineering, Beijing University of Posts and Telecommunications, Beijing
  • Received:2009-02-23 Revised:2009-03-20 Online:2009-11-20 Published:2009-11-20
  • Supported by:
    Project supported by National Basic Research Program of China (Grant Nos 2009CB320505 and 2009CB320504) and National High Technology Research and Development Program of China (Grant Nos 2006AA01Z235, 2007AA01Z206 and 2009AA01Z210).

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摘要： Network traffic prediction models can be grouped into two types, single models and combined ones. Combined models integrate several single models and thus can improve prediction accuracy. Based on wavelet transform, grey theory, and chaos theory, this paper proposes a novel combined model, wavelet--grey--chaos (WGC), for network traffic prediction. In the WGC model, we develop a time series decomposition method without the boundary problem by modifying the standard \grave\rm a trous algorithm, decompose the network traffic into two parts, the residual part and the burst part to alleviate the accumulated error problem, and employ the grey model GM(1,1) and chaos model to predict the residual part and the burst part respectively. Simulation results on real network traffic show that the WGC model does improve prediction accuracy.

Abstract: Network traffic prediction models can be grouped into two types, single models and combined ones. Combined models integrate several single models and thus can improve prediction accuracy. Based on wavelet transform, grey theory, and chaos theory, this paper proposes a novel combined model, wavelet--grey--chaos (WGC), for network traffic prediction. In the WGC model, we develop a time series decomposition method without the boundary problem by modifying the standard à trous algorithm, decompose the network traffic into two parts, the residual part and the burst part to alleviate the accumulated error problem, and employ the grey model GM(1,1) and chaos model to predict the residual part and the burst part respectively. Simulation results on real network traffic show that the WGC model does improve prediction accuracy.

Key words: network traffic prediction, wavelet transform, grey model, chaos model

中图分类号:  (Nonlinear dynamics and chaos)

  • 05.45.-a
02.30.Uu (Integral transforms) 02.70.-c (Computational techniques; simulations)