Self-similarity is a ubiquitous character in traffic and has a great impact on network performance.

自相似性是网络通信量的普遍性质并且对网络性能有很大的影响。

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According to the character of non linear network traffic, the traffic time series is decomposed into trend component, period component, mutation component and random component.

文章考虑网络流量非线性的特点，通过不同的数学模型将流量时间序列分解成趋势成分、周期成分、突变成分和随机成分。

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Based on statistics character of traffic in a large-scale network, the steady metrics that can estimated network behavior are found and a sampling measurement model is presented in this paper.

基于大规模网络流量的统计特征，寻找能够评价网络行为的稳定测度，并建立抽样测量模型。

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Based on statistics character of traffic in a large-scale network, the steady metrics that can estimated network behavior are found and a sampling measurement model is presented in this paper.

基于大规模网络流量的统计特征，寻找能够评价网络行为的稳定测度，并建立抽样测量模型。

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