摘要
大规模的网络进行动态流量监测的一个优化目标是有效减少观测对象,传统的方法通常根据流在空间的相关性减少测量对象。本文提出了一种基于主成分分析的网络的关键路径发现算法PCAR,它通过分析网络流量的时间和空间的相关性来发现网络中的关键路径。我们用Totem公布的Abliene流量数据检验了PCAR算法的有效性。实验表明,该算法与其它算法相比具有计算复杂性小、误判率低等特点。
Reducing the objects to be measured is one of the optimization targets when monitoring the traffic flows on large-scale networks. Traditional methods often reduce the objects to be measured according to the flow＇s dependence in the space. The paper presents an algorithm of finding critical paths based on principal component analysis named PCAR, which finds the critical paths in the network by analysing the time-and-space dependence of the network traffic. We evaluate the algorithm using a large collection of real traffic flows collected in the Abliene network and our results demonstrate that the algorithm are effective and features a low error ratio compared with other algorithms.
出处
《计算机工程与科学》
CSCD
2008年第6期1-4,共4页
Computer Engineering & Science
基金
国家973计划资助项目(2003CB314802)
