用信度网自动检测计算机瓶颈
Automating Computer Bottleneck Detection with Belief Nets
摘要：
我们描述了信度网络在计算机系统瓶颈诊断中的应用。该技术依赖于应用程序工作负载、Windows NT操作系统和系统硬件之间交互的高级功能模型。给定工作负载描述，该模型预测可从Windows NT性能监视工具获得的可观察系统计数器的值。工作负载、预测和计数器值的不确定性以高斯分布为特征。在诊断推理过程中，我们使用观察到的性能监视器值来查找工作负载参数的最可能分配。在本文中，我们提供了一些自动瓶颈检测的背景，描述了系统模型的结构，并讨论了模型校准和验证的经验程序。校准过程的一部分包括生成数据集以估计多变量高斯误差模型。诊断瓶颈的初步结果。
英文摘要：
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.
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