Inference and Learning in Networks of Queues
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
Text
Sutton and Jordan. "Inference and Learning in Networks of Queues." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[Sutton and Jordan. "Inference and Learning in Networks of Queues." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/sutton2010aistats-inference/)BibTeX
@inproceedings{sutton2010aistats-inference,
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},
year = {2010},
pages = {796-803},
volume = {9},
url = {https://mlanthology.org/aistats/2010/sutton2010aistats-inference/}
}