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We describe an application of belief networks to the diagnosis of bottlenecksin computer systems. The technique relies on a high-level functional model ofthe interaction between application workloads, the Windows NT operating system,and system hardware. Given a workload description, the model predicts thevalues of observable system counters available from the Windows NT performancemonitoring tool. Uncertainty in workloads, predictions, and counter values arecharacterized with Gaussian distributions. During diagnostic inference, we useobserved performance monitor values to find the most probable assignment to theworkload parameters. In this paper we provide some background on automatedbottleneck detection, describe the structure of the system model, and discussempirical procedures for model calibration and verification. Part of thecalibration process includes generating a dataset to estimate a multivariateGaussian error model. Initial results in diagnosing bottlenecks are presented.

Automating Computer Bottleneck Detection with Belief Nets