OSDI '04 Abstract
Pp. 231244 of the Proceedings
Correlating Instrumentation Data to System States: A Building Block for Automated Diagnosis and Control
Ira Cohen, Hewlett-Packard Laboratories; Jeffrey S. Chase, Duke University; Moises Goldszmidt, Terence Kelly, and Julie Symons, Hewlett-Packard Laboratories
This paper studies the use of statistical induction techniques as a
basis for automated performance diagnosis and performance management.
The goal of the work is to develop and evaluate tools for offline and
online analysis of system metrics gathered from instrumentation in
Internet server platforms. We use a promising class of probabilistic
models (Tree-Augmented Bayesian Networks or TANs) to identify
combinations of system-level metrics and threshold values that
correlate with high-level performance states--compliance with
Service Level Objectives (SLOs) for average-case response time--in a
three-tier Web service under a variety of conditions.
Experimental results from a testbed show that TAN models involving
small subsets of metrics capture patterns of performance behavior in
a way that is accurate and yields insights into the causes of
observed performance effects. TANs are extremely efficient to
represent and evaluate, and they have interpretability properties
that make them excellent candidates for automated diagnosis and
control. We explore the use of TAN models for offline forensic
diagnosis, and in a limited online setting for performance
forecasting with stable workloads.
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