An Effective Method of Monitoring the Large-Scale Traffic Pattern Based on RMT and PCA. (20th May 2010)

• Record Type: Journal Article
• Authors: Liu, Jia; Gao, Peng; Yuan, Jian; Du, Xuetao
• Other Names: Ma Chunsheng Academic Editor.
• Is Part Of: Journal of probability and statistics. Volume 2010(2010)
• Publication Date: 2010-05-20
• Subjects: Probabilities -- Periodicals; Mathematical statistics -- Periodicals; Mathematical statistics; Probabilities; Periodicals; 519
• DOI: 10.1155/2010/375942
• Languages: English
• ISSNs: 1687-952X

Abstract

Abstract : Mechanisms to extract the characteristics of network traffic play a significant role in traffic monitoring, offering helpful information for network management and control. In this paper, a method based on Random Matrix Theory (RMT) and Principal Components Analysis (PCA) is proposed for monitoring and analyzing large-scale traffic patterns in the Internet. Besides the analysis of the largest eigenvalue in RMT, useful information is also extracted from small eigenvalues by a method based on PCA. And then an appropriate approach is put forward to select some observation points on the base of the eigen analysis. Finally, some experiments about peer-to-peer traffic pattern recognition and backbone aggregate flow estimation are constructed. The simulation results show that using about 10% of nodes as observation points, our method can monitor and extract key information about Internet traffic patterns.