基于混沌理论的局域网流量预测

Prediction in LAN Traffic Flow Based on Chaos Theory

  • 摘要: 局域网业务流中广泛存在自相似为特征的现象,并且自相似现象与混沌现象间存在紧密联系.通过采用局域网流量对应的时间序列分析的方法进行研究,基于相空间重构思想,通过C-C算法计算嵌入维和延迟时间;利用小数据量法计算局域网流量时间序列的最大Lyapunov指数来判断其混沌特性;针对基于最大Lyapunov指数的预测方法中只考虑中心点的最邻近点对预测的决定性作用,而忽略了其邻近点邻域内其他各点对预测结果的影响的特点,提出了基于最大Lyapunov指数的加权邻域预测法;最后通过实测局域网流量预测验证方法的有效性.

     

    Abstract: There exists widely the self-similarity in LAN traffic flow, and there is a close relationship between the self similarity characteristics and chaotic phenomena. The LAN time series of traffic flow were reconstructed in phase space theory. The embedding dimension and the delay time were computed by the C-C algorithm, and the largest Lyapunov exponent was then calculated via the small data method to determine its chaotic level. The weighted neighborhood prediction method was proposed and conducted considering the only decisive role of the nearest point on the center point based on the largest Lyapunov exponent while ignoring its neighborhood points on the predicting affection. The validation of the method was done by predicting the actual LAN traffic flow.

     

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