Bottleneck detection using statistical intervention analysis

Authors:
Simon Malkowski;Markus Hedwig;Jason Parekh;Calton Pu;Akhil Sahai
Affiliations:
CERCS, Georgia Institute of Technology, Atlanta, GA;CERCS, Georgia Institute of Technology, Atlanta, GA;CERCS, Georgia Institute of Technology, Atlanta, GA;CERCS, Georgia Institute of Technology, Atlanta, GA;HP Laboratories, Palo-Alto, CA
Venue:
DSOM'07 Proceedings of the Distributed systems: operations and management 18th IFIP/IEEE international conference on Managing virtualization of networks and services
Year:
2007

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Abstract

The complexity of today's large-scale enterprise applications demands system administrators to monitor enormous amounts of metrics, and reconfigure their hardware as well as software at run-time without thorough understanding of monitoring results. The Elba project is designed to achieve an automated iterative staging to mitigate the risk of violating Service Level Objectives (SLOs). As part of Elba we undertake performance characterization of system to detect bottlenecks in their configurations. In this paper, we introduce our concrete bottleneck detection approach used in Elba, and then show its robustness and accuracy in various configurations scenarios. We utilize a wellknown benchmark application, RUBiS (Rice University Bidding System), to evaluate the classifier with respect to successful identification of different bottlenecks.