Research of chaos theory and local support vector machine in effective prediction of VBR MPEG video traffic

Authors:
Heng-Chao Li;Wen Hong;Yi-Rong Wu;Si-Jie Xu
Affiliations:
Graduate School of Chinese Academy of Sciences, Beijing, P.R. China;National Key Laboratory of Microwave Imaging Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing, P.R. China;National Key Laboratory of Microwave Imaging Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing, P.R. China;Graduate School of Southwest Jiaotong University, Chengdu, P.R. China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

Citing 3
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An adaptable neural-network model for recursive nonlinear traffic prediction and modeling of MPEG video sources

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A Short-Term Forecasting Algorithm for Network Traffic Based on Chaos Theory and SVM

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Abstract

The highly bursty and time-variant characteristics of VBR MPEG video traffic make it more difficult to manage network resources, and lead to the significant reduction of network utilization. Dynamic bandwidth allocation scheme based on real-time prediction algorithms has been used to guarantee the Quality of Service (QoS). In this paper, chaos theory and local support vector machine in effective prediction of VBR MPEG video traffic is investigated. Experimental results show that our proposed scheme can effectively capture the dynamics and complexity of VBR MPEG video traffic.