摘要
在对等网络中,节点可以动态地进入和离开网络,增加了业务监测的难度。该文提出了采用主成分分析(PCA)方法检测并分析对等网络流量特征,解释了流量协方差矩阵最大特征值和最大特征矢量的物理意义,同时定义权重矢量作为流量观测指标,利用流量的协方差矩阵最大特征值和特征向量给出了全网的P2P流量时间和空间的动态特性。仿真结果表明,该方法能够很好地识别P2P流量的动态时间和空间特性,为互联网流量检测提供了一种有效方法。
Monitoring P2P traffic is difficult due to the peers in P2P networks joining and leaving dynamically. This paper presents a method based on principal components analysis (PCA) to monitor the spatial-and-temporal characteristics of P2P traffic. The meaning of the largest eigenvalue and the corresponding eigenvector is explained by the PCA theory,which are calculated from the cross-correlation matrix of the flow data. A weight vector is also defined to monitor traffic patterns. Simulation results show that the spatial-and-temporal characteristics are effectively captured using the developed method.
出处
《清华大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2010年第4期561-564,共4页
Journal of Tsinghua University(Science and Technology)
基金
国家自然科学基金资助项目(60674048
60672107)
国家"九七三"重点基础研究项目(2007CB307100)
关键词
对等网络
流量监测
主成分分析
协方差矩阵
网络测量
peer-to-peer
traffic monitoring
principal components analysis
cross-correlation matrix
network measurement