3rd International Week on Management of Networks and Services End-to-End Virtualization of Networks and Services Manweek 2007, October 29-November 2, San José, CA, USA
Bottleneck Detection using Statistical Intervention Analysis
Simon Malkowski1, Markus Hedwig1, Jason Parekh1, Calton Pu1, Akhil Sahai2
1Georgia Institute of Technology, United States 2HP Laboratories, United States
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 well-known benchmark application, RUBiS (Rice
University Bidding System), to evaluate the classifier with respect to successful
identification of different bottlenecks.
