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Generic Online Optimization of Multiple Configuration Parameters with Application to a Database Server

  • Yixin Diao
  • Frank Eskesen
  • Steven Froehlich
  • Joseph L. Hellerstein
  • Lisa F. Spainhower
  • Maheswaran Surendra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2867)

Abstract

Optimizing configuration parameters is time-consuming and skills-intensive. This paper proposes a generic approach to automating this task. By generic, we mean that the approach is relatively independent of the target system for which the optimization is done. Our approach uses online adjustment of configuration parameters to discover the system’s performance characteristics. Doing so creates two challenges: (1) handling interdependencies between configuration parameters and (2) minimizing the deleterious effects on production workload while the optimization is underway. Our approach addresses (1) by including in the architecture a rule-based component that handles interdependencies between configuration parameters. For (2), we use a feedback mechanism for online optimization that searches the parameter space in a way that generally avoids poor performance at intermediate steps. Our studies of a DB2 Universal Database Server under an e-commerce workload indicate that our approach can be effective in practice.

Keywords

Pool Size Target System Direct Search Method Online Optimization Common Information Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Yixin Diao
    • 1
  • Frank Eskesen
    • 1
  • Steven Froehlich
    • 1
  • Joseph L. Hellerstein
    • 1
  • Lisa F. Spainhower
    • 2
  • Maheswaran Surendra
    • 1
  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA
  2. 2.IBM Server GroupPoughkeepsieUSA

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