Abstract

This paper shows how to obtain valuable insight into the performance of globally-distributed applications without abstruse techniques or detailed application knowledge: Simple queueing-theoretic observations together with standard optimization methods yield remarkably accurate performance models. The models can be used for performance anomaly detection, i.e., distinguishing performance faults from mere overload. This distinction can in turn suggest both performance debugging tools and remedial measures.

Extensive empirical results from three production systems serving real customers—two of which are globally distributed and span administrative domains— demonstrate that our method yields accurate performance models of diverse applications. Our method furthermore flagged as anomalous an episode of a real performance bug in one of the three systems.

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