Project Description

The problem of identifying topology and inferring link-level performance parameters such as packet drop rate or delay variance using only end-to-end measurements is commonly referred to as network tomography. Existing measurement techniques have used a single source actively sending probes to multiple receivers. In this project we have developed a collaborative framework for performing network tomography on topologies with multiple sources and multiple destinations, without assuming that the topology is known. Using multiple sources potentially provides a more accurate and refined characterization of the internal network. We extend our multiple source active measurement procedure to incorporate performance measurements. A decision-theoretic framework is developed enabling the joint characterization of topology and internal performance. We design a statistical test based on the Generalized Likelihood Ratio Test and Wilks' Theorem. The test quantifies the tradeoff between network topology complexity and performance estimation, and identifies when measurements made by two sources can be combined to achieve reduced variance performance estimates. The performance and efficacy of the algorithm are assessed through simulation and Internet experiments. For details on our method please see [1] and [2].