By Topic

Bayesian inference of network loss characteristics with applications to TCP performance prediction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Dong Guo ; Dept. of Electr. Eng., Columbia Univ., New York, NY, USA ; Xiaodong Wang

Network tomography, inferring internal network behavior based on the "external" end-to-end network measurements, is of particular interest when the network itself can not cooperate in characterizing its own behavior. In particular, it is impractical to directly measure packet losses or delays at every router. On the other hand, measuring end-to-end (from sources to receivers) losses is relatively easy. In this paper, the problems of characterizing links behavior in a network is formulated as Bayesian inference problems and develop several Markov chain Monte Carlo (MCMC) algorithms to solve them. The proposed link loss algorithms are then applied to data generated by the network simulator (NS2) software, and obtain good agreements between the theoretical results and the actual measurements.

Published in:

Statistical Signal Processing, 2003 IEEE Workshop on

Date of Conference:

28 Sept.-1 Oct. 2003