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.
['Jia Liu', 'Depeng Jin', 'Jian Yuan', 'Wenzhu Zhang', 'Li Su', 'Lieguang Zeng']
A RMT and PCA-Based Method of Monitoring the Large-Scale Traffic Pattern