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链路层分类包的网络流量自相似性研究 被引量：2

Study of self-similarity characteristic of network traffic based on classified packets of data link layer
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摘要 提出一种从链路层分类包流量的角度研究网络流量自相似性的方法。使用优化的R/S(rescaled range)法计算Hurst指数,发现分类包流量和总流量一样呈自相似性,并用ON/OFF网络流量模型解释分类包流量自相似的物理原因;并使用主成分分析法研究分类包流量对总流量自相似性的影响,得出大于512B的分类包(大象包)是影响总流量自相似性的主要原因。实验表明该方法是快速有效的。 A novel method of studying self-similarity of network traffic is presented.By measuring online the network traffic of classified packets of a backbone link layer in OC-48 POS in a metro area network in long-term,the Hurst exponents of elassi fied packets are been estimated by the method of R/S（rescaled range）.The network traffic of the classified packets is self-similarity and the reason is explained by the ON/OFF traffic model.Then the influence of the traffic of classified packets on the self-similarity of the total traffic is researched by the method of PCA （Primary Component Analysis） and find that the self-similarity characteristic of the total traffic is mainly caused by the classified packets whose lengths are under 512 byte.Finally,the result of the experiment shows that the method is effective.
作者 聂得欣 袁小坊 王东 谢高岗
机构地区 湖南大学计算机与通信学院 中国科学院计算技术研究所下一代互联网研究中心
出处 《计算机工程与应用》 CSCD 北大核心 2009年第5期129-131,146,共4页 Computer Engineering and Applications
基金 国家自然科学基金网络与信息安全重大专项(No.90604015) 国家重点基础研究发展规划(973)(No.2007CB310702) 湖南省自然科学基金(No.06JJ4078)~~
关键词 网络流量 自相似性 HURST指数 R/S法 主成分分析法 network traffic self-similarity Hurst exponent R/S Primary Component Analysis（PCA）
分类号 TP393.06 [自动化与计算机技术—计算机应用技术]
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参考文献19

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二级参考文献52

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计算机工程与应用
2009年 第5期

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