Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/56418
Citations
Scopus Web of Science® Altmetric
?
?
Type: Conference paper
Title: Network loss inference with second order statistics of end-to-end flows
Author: Nguyen, H.
Thiran, P.
Citation: Proceedings of the 7th ACM SIGCOMM Internet Measurement Conference, San Diego, USA, 24-26 October 2007: pp.227-239
Publisher: ACM
Publisher Place: New York
Issue Date: 2007
ISBN: 9781595939081
Conference Name: Internet Measurement Conference (2007 : San Diego, USA)
Department: Teletraffic Research Centre for Mathematical Modelling
Statement of
Responsibility: 
Hung X. Nguyen and Patrick Thiran
Abstract: We address the problem of calculating link loss rates from end-to-end measurements. Contrary to existing works that use only the average end-to-end loss rates or strict temporal correlations between probes, we exploit second-order moments of end-to-end flows. We first prove that the variances of link loss rates can be uniquely calculated from the covariances of the measured end-to-end loss rates in any realistic topology. After calculating the link variances, we remove the un-congested links with small variances from the first-order moment equations to obtain a full rank linear system of equations, from which we can calculate precisely the loss rates of the remaining congested links. This operation is possible because losses due to congestion occur in bursts and hence the loss rates of congested links have high variances. On the contrary, most links on the Internet are un-congested, and hence the averages and variances of their loss rates are virtually zero. Our proposed solution uses only regular unicast probes and thus is applicable in today’s Internet. It is accurate and scalable, as shown in our simulations and experiments on PlanetLab.
Keywords: Network Tomography; Identifiability; Inference
RMID: 0020093573
DOI: 10.1145/1298306.1298339
Description (link): http://portal.acm.org/citation.cfm?id=1298339
Appears in Collections:Mathematical Sciences publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.