Network topology discovery using finite mixture models

We propose a network topology estimation strategy using unicast end-to-end packet pair delay measurements that is based on mixture models for the delay covariances. An unsupervised learning algorithm is applied to estimate the number of mixture components and delay covariances. The leaf pairs are clustered by a MAP criterion and passed to a hierarchical topology construction algorithm to rebuild the tree. Results from an ns simulation show that our algorithm can identify a network tree with 8 leaf nodes.