Multicast topology inference from measured end-to-end loss Academic Article

abstract

• The use of multicast inference on end-to-end measurement has recently been proposed as a means to infer network internal characteristics such as packet link loss rate and delay. In this paper, we propose three types of algorithm that use loss measurements to infer the underlying multicast topology: i) a grouping estimator that exploits the monotonicity of loss rates with increasing path length; ii) a maximum-likelihood (ML) estimator (MLE); and iii) a Bayesian estimator. We establish their consistency, compare their complexity and accuracy, and analyze the modes of failure and their asymptotic probabilities.

authors

• Duffield, Nick

published proceedings

• IEEE TRANSACTIONS ON INFORMATION THEORY

altmetric score

• 3

author list (cited authors)

• Duffield, N. G., Horowitz, J., Lo Presti, F., & Towsley, D.

citation count

• 143

complete list of authors

• Duffield, NG||Horowitz, J||Lo Presti, F||Towsley, D

publication date

• January 2002

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE TRANSACTIONS ON INFORMATION THEORY  Journal

keywords

• Communication Networks
• End-to-end Measurement
• Maximum-likelihood (ml) Estimation
• Multicast
• Statistical Inference
• Topology Discovery

Digital Object Identifier (DOI)

• 10.1109/18.971737

start page

• 26

end page

• 45

volume

• 48

issue

• 1

URL

• http://dx.doi.org/10.1109/18.971737