Monitoring the spatial-temporal effect of internet traffic based on Random Matrix Theory

Monitoring traffic pattern is always an important approach to improve the performance of computer networks. In recent years, peer-to-peer (P2P) service gradually dominates the Internet traffic against traditional client-server (C-S) service such as Web. The changed situation of networks is mainly in two aspects. One is that popular P2P file-sharing applications require both board downstream and upstream bandwidth, which conflicts to asymmetry structure of the C-S service oriented networks, and another is that the topology of P2P overlay networks is varying with peers joining and leaving. The dramatic shift of Internet traffic may cause different patterns from that observed in early years. This leads a new round of Internet measurement, which requires effective monitoring of network-wide traffic within the region of ISP or autonomous system. Unfortunately, few of technologies can capture well the dynamics of C-S and P2P traffic in a macroscopic level. In this paper, we propose a method based on random matrix theory (RMT) for the detection of networkwide traffic pattern. Using only a few observation points, our method can monitor the macroscopic effect of the Internet traffic. We show that such macroscopic-level monitoring can be used to capture shifts in spatial-temporal patterns caused by P2P and C-S traffic, and inform where and when P2P and C-S traffic possibly arise in transit networks.