Automating Computer Bottleneck Detection with Belief Nets
  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.
