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Maximum Likelihood Network Topology Identification from Edge-based Unicast Measurements by Mark Coates; Rui Castro; Robert Nowak; Manik Gadhiok; Ryan King; Yolanda Tsang is a scholarly article available to read on EtoBox.

What is Maximum Likelihood Network Topology Identification from Edge-based Unicast Measurements about?

Network tomography is a process for inferring "internal" link-level delay and loss performance information based on end-to-end (edge) network measurements. These methods require knowledge of the network topology; therefore a first crucial step in the tomography process is topology identification. This paper considers the problem of discovering network topology solely from host-based, unicast measurements, without internal network cooperation. First, we introduce a novel delay-based measurement scheme that does not require clock synchronization, making it more practical than other previous proposals. In contrast to methods that rely on network cooperation , our methodology has the potential to identify layer two elements (provided they are logical topology branching points and induce some measurable delay). Second, we propose a maximum penalized likelihood criterion for topology identification. This is a global optimality criterion, in contrast to other recent proposals for topology identification that employ suboptimal, pair-merging strategies. We develop a novel Markov Chain Monte Carlo (MCMC) procedure for rapid determination of the most likely topologies. The performance of our

Author

Mark Coates; Rui Castro; Robert Nowak; Manik Gadhiok; Ryan King; Yolanda Tsang

Publisher

ACM

Published

2002

Language

EN