Multicast-Based Inference of Network-Internal Loss Characteristics

Robust measurements of network dynamics are increasingly important to the design and operation of large internetworks like the Internet. However, administrative diversity makes it impractical to monitor every link on an end-to-end path. At the same time, it is difficult to determine the performance character-istics of individual links from end-to-end measurements of unicast traffic. In this paper, we introduce the use of end-to-end measurements of multicast traffic to infer network-internal characteristics. The band-width efficiency of multicast traffic makes it suitable for large-scale measurements of both end-to-end and internal network dynamics. We develop a Maximum Likelihood Estimator for loss rates on internal links based on losses ob-served by multicast receivers. It exploits the inherent correlation between such observations to infer the performance of paths between branch points in the tree spanning a multicast source and its receivers. We derive its rate of convergence as the number of measurements increases, and we establish robustness with respect to certain generalizations of the underlying model. We validate these techniques through simulation and discuss possible extensions and applications of this work.