Uncertainty in Artificial Intelligence
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Automating Computer Bottleneck Detection with Belief Nets
John Breese, Russ Blake
Abstract:
We describe an application of belief networks to the diagnosis of bottlenecks in computer systems. The technique relies on a high-level functional model of the interaction between application workloads, the Windows NT operating system, and system hardware. Given a workload description, the model predicts the values of observable system counters available from the Windows NT performance monitoring tool. Uncertainty in workloads, predictions, and counter values are characterized with Gaussian distributions. During diagnostic inference, we use observed performance monitor values to find the most probable assignment to the workload parameters. In this paper we provide some background on automated bottleneck detection, describe the structure of the system model, and discuss empirical procedures for model calibration and verification. Part of the calibration process includes generating a dataset to estimate a multivariate Gaussian error model. Initial results in diagnosing bottlenecks are presented.
Keywords: Bottleneck, diagnosis, operating system, performance modeling, belief networks, prob
Pages: 36-45
PS Link:
PDF Link: /papers/95/p36-breese.pdf
BibTex:
@INPROCEEDINGS{Breese95,
AUTHOR = "John Breese and Russ Blake",
TITLE = "Automating Computer Bottleneck Detection with Belief Nets",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "36--45"
}


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