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dc.contributor.advisor Nowak, Robert D.
dc.creatorTsang, Yau-Yau Yolanda
dc.date.accessioned 2009-06-04T06:49:18Z
dc.date.available 2009-06-04T06:49:18Z
dc.date.issued 2001
dc.identifier.urihttps://hdl.handle.net/1911/17471
dc.description.abstract Network tomography is a promising technique for characterizing the internal behavior of large-scale networks based solely on end-to-end measurements. Despite the efficiency of active probing in most network loss tomography methods, these measurements impose an additional burden on the network in terms of bandwidth and network resources. They can therefore cause the estimated performance parameters to differ substantially from losses suffered by existing TCP traffic flows. In this thesis, we propose a promising passive measurement framework based on the sampling of existing TCP flows. We demonstrate its performance using extensive ns-2 simulations. We observe accurate estimates of link losses (with 2% mean absolute error). We also describe the Expectation-Maximization (EM) algorithm in solving the Maximum Likelihood (ML) Estimates in terms of individual link loss rates as an incomplete data problem. Finally, we present a new method for simultaneously visualizing the network connectivity and the network performance parameters.
dc.format.extent 43 p.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectElectronics
Electrical engineering
dc.title Loss inference in unicast network tomography based on TCP traffic monitoring
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Electrical and Computer Engineering
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Masters
thesis.degree.name Master of Science
dc.identifier.citation Tsang, Yau-Yau Yolanda. "Loss inference in unicast network tomography based on TCP traffic monitoring." (2001) Master’s Thesis, Rice University. https://hdl.handle.net/1911/17471.


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