Learning and Inference in Queueing Networks
Charles Sutton and Michael I Jordan
In: AISTATS 2010, 13-15 May 2010, Sardinia, Italy.

Abstract

Probabilistic models of the performance of computer systems are useful both for predicting system performance in new conditions, and for diagnosing past performance prob- lems. The most popular performance models are networks of queues. However, no cur- rent 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 eectiveness of our sampler on real-world data from a benchmark Web application.

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