Unicast-based inference of network link delay distributions with finite mixture models

Providers of high quality-of-service over telecommunication networks require accurate methods for remote measurement of link-level performance. Recent research in network tomography has demonstrated that it is possible to estimate internal link characteristics, e.g., link delays and packet losses, using unicast probing schemes in which probes are exchanged between several pairs of sites in the network. In this paper, we present a new method for estimation of internal link delay distributions using the end-to-end packet pair delay statistics gathered by back-to-back packet-pair unicast probes. Our method is based on a variant of the penalized maximum likelihood expectationmaximization (PML-EM) algorithm applied to an additive finite mixture model for the link delay probability density functions. The mixture model incorporates a combination of discrete and continuous components, and we use a minimum message length (MML) penalty for selection of model order. We present results of matlab and ns 2 simulations to illustrate the promise of our network tomography algorithm for light cross-traffic scenarios.