NDLI: A RMT and PCA-Based Method of Monitoring the Large-Scale Traffic Pattern

Content Provider	IEEE Xplore Digital Library
Author	Jia Liu Depeng Jin Jian Yuan Wenzhu Zhang Li Su Lieguang Zeng
Copyright Year	2009
Abstract	Mechanisms to extract the characteristics of network traffic play a significant role in the 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 pattern of Internet. Besides the analysis of the largest eigenvalue in RMT, useful information is also extracted from the small eigenvalue by the 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 shows that using about 10% nodes as observation points, our method can monitor and extract key information about Internet traffic pattern.
Starting Page	698
Ending Page	703
File Size	1048237
Page Count	6
File Format	PDF
ISBN	9781424441549
DOI	10.1109/AMS.2009.9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-25
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Peer to peer computing Information management Data mining principal components analysis Traffic control random matrix theory Communication system traffic control Eigenvalues and eigenfunctions Large-scale systems Internet traffic pattern Monitoring Principal component analysis
Content Type	Text
Resource Type	Article

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