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

Abstract EN

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.