计算机应用

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基于组合方法的网络业务流预测

刘渊 李小航 刘元珍   

  1. 江南大学 江南大学 江南大学
  • 收稿日期:2007-07-02 修回日期:1900-01-01 发布日期:2007-12-01 出版日期:2007-12-01
  • 通讯作者: 李小航

Network traffic prediction based on combination method

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  • Received:2007-07-02 Revised:1900-01-01 Online:2007-12-01 Published:2007-12-01
  • Contact: Xiao-Hang LI

摘要: 在总结了已有的流量预测方法基础上,提出了一种基于多种预测技术组合而成的网络流量预测方法。该方法根据小波多尺度的分解和重构思想,将网络流量通过小波分解成不同尺度下的逼近信号和细节信号, 然后分别单支重构成低频序列和高频序列。根据低频和高频序列的不同特性,分别采用自回归模型(AR)和线性最小均方误差估计(LMMSE)对未来网络流量进行预测,最后重新组合生成预测流量。通过对真实网络流量的仿真实验表明,该方法能比较准确地预测未来的网络流量。

关键词: 网络流量, 自回归模型, 小波分析, 流量预测

Abstract: After summarizing the existing traffic prediction methods, a combination method of network traffic prediction based on a variety of forecasting techniques was proposed. According to the theory of decomposition and reconstruction based on multi-scale of wavelet, the network traffic was decomposed into approximation signals and detailed signals of different scales. Then these signals were reconstructed into several low frequency and high frequency time serials by wavelet. These serials were predicted by Linear Minimum Mean Square Error (LMMSE) and Auto Regressive (AR) models respectively according to their different features, and the predicted results of all serials were combined into the final prediction traffic. Simulation results with the real traffic traces show that the method can more accurately predict the future of the network traffic.

Key words: network traffic, Auto Regressive (AR) model, wavelet analysis, traffic prediction

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