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Journal of Probability and Statistics
Volume 2010, Article ID 375942, 16 pages
Research Article

An Effective Method of Monitoring the Large-Scale Traffic Pattern Based on RMT and PCA

1Research Division, China Mobile Group Design Institute Co. Ltd, Beijing 100080, China
2Department of Electronic Engineering, Tsinghua University, Beijing 10084, China

Received 2 September 2009; Revised 17 January 2010; Accepted 12 April 2010

Academic Editor: Chunsheng Ma

Copyright © 2010 Jia Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.