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

Jia Liu, Peng Gao, Jian Yuan and Xuetao Du

Journal of Probability and Statistics, 2010, vol. 2010, 1-16

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.

Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnljps:375942

DOI: 10.1155/2010/375942

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