Inference and Learning in Networks of Queues

Charles Sutton, Michael I. Jordan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Probabilistic models of the performance of computer systems are useful both for predicting system performance in new conditions, and for diagnosing past performance problems. The most popular performance models are networks of queues. However, no current methods exist for parameter estimation or inference in networks of queues with missing data. In this paper, we present a novel viewpoint that combines queueing networks and graphical models, allowing Markov chain Monte Carlo to be applied. We demonstrate the effectiveness of our sampler on real-world data from a benchmark Web application.
Original languageEnglish
Title of host publicationProceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 2010
PublisherJournal of Machine Learning Research: Workshop and Conference Proceedings
Pages796-813
Number of pages8
Publication statusPublished - 2010

Publication series

NameJMLR Workshop and Conference Proceedings
Volume9
ISSN (Electronic)1533-7928

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