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Bottleneck Detection Using Statistical Intervention Analysis

  • Simon Malkowski
  • Markus Hedwig
  • Jason Parekh
  • Calton Pu
  • Akhil Sahai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4785)

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.

Keywords

Bottleneck detection statistical analysis enterprise systems perforance analysi 

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Copyright information

© IFIP International Federation for Information Processing 2007

Authors and Affiliations

  • Simon Malkowski
    • 1
  • Markus Hedwig
    • 1
  • Jason Parekh
    • 1
  • Calton Pu
    • 1
  • Akhil Sahai
    • 2
  1. 1.CERCS, Georgia Institute of Technology, 266 Ferst Drive, Atlanta, GA 30332USA
  2. 2.HP Laboratories, Palo-Alto, CAUSA

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