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 characteristics 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 networkinternal characteristics, in particular packet loss rates. We develop statistically rigorous techniques for estimating loss rates on internal links based on losses observed by multicast receivers. These techniques exploit 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 validate these techniques through simulation and discuss possible extensions and applications of this work. The bandwidth efficiency of multicast traffic makes these techniques suitable for large-scale measurements of both end-toend and internal network dynamics.