Inference and Learning in Networks of Queues

Charles Sutton, Michael I. Jordan
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR 9:796-803, 2010.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v9-sutton10a, title = {Inference and Learning in Networks of Queues}, author = {Sutton, Charles and Jordan, Michael I.}, booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics}, pages = {796--803}, year = {2010}, editor = {Teh, Yee Whye and Titterington, Mike}, volume = {9}, series = {Proceedings of Machine Learning Research}, address = {Chia Laguna Resort, Sardinia, Italy}, month = {13--15 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v9/sutton10a/sutton10a.pdf}, url = {https://proceedings.mlr.press/v9/sutton10a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Inference and Learning in Networks of Queues %A Charles Sutton %A Michael I. Jordan %B Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2010 %E Yee Whye Teh %E Mike Titterington %F pmlr-v9-sutton10a %I PMLR %P 796--803 %U https://proceedings.mlr.press/v9/sutton10a.html %V 9 %X 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.
RIS
TY - CPAPER TI - Inference and Learning in Networks of Queues AU - Charles Sutton AU - Michael I. Jordan BT - Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics DA - 2010/03/31 ED - Yee Whye Teh ED - Mike Titterington ID - pmlr-v9-sutton10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 9 SP - 796 EP - 803 L1 - http://proceedings.mlr.press/v9/sutton10a/sutton10a.pdf UR - https://proceedings.mlr.press/v9/sutton10a.html AB - 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. ER -
APA
Sutton, C. & Jordan, M.I.. (2010). Inference and Learning in Networks of Queues. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 9:796-803 Available from https://proceedings.mlr.press/v9/sutton10a.html.

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