Detecting Events in the Dynamics of Ego-Centered Measurements of the Internet Topology
Detecting events such as major routing changes or congestions in the dynamics of the internet topology is an important but challenging task. The authors explore here a top-down approach based on a notion of statistically significant events. It consists in identifying statistics which exhibit a homogeneous distribution with outliers, which correspond to events. They apply this approach to ego-centerd measurements of the internet topology (views obtained from a single monitor) and show that it succeeds in detecting meaningful events. Finally, they give some hints for the interpretation of such events in terms of network events.