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15

JMLR
2010

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Inference and Learning in Networks of Queues

12 years 11 months ago

Inference and Learning in Networks of Queues

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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.

Charles A. Sutton, Michael I. Jordan

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JMLR 2010 | Markov Chain Monte Carlo | Performance | Popular Performance Models |

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Added	19 May 2011
Updated	19 May 2011
Type	Journal
Year	2010
Where	JMLR
Authors	Charles A. Sutton, Michael I. Jordan

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