skip to main content
10.1145/1254882.1254899acmconferencesArticle/Chapter ViewAbstractPublication PagesmetricsConference Proceedingsconference-collections
Article

Optimizing system configurations quickly by guessing at the performance

Published:12 June 2007Publication History

ABSTRACT

The performance of a Web system can be greatly improved by tuning its configuration parameters. However, finding the optimal configuration has been a time-consuming task due to the long measurement time needed to evaluate the performance of a given configuration. We propose an algorithm, which we refer to as Quick Optimization via Guessing (QOG), that quickly selects one of nearly best configurations with high probability. The key ideas in QOG are (i) the measurement of a configuration is terminated as soon as the configuration is found to be suboptimal, and (ii) the performance of a configuration is guessed at based on the measured similar configurations, so that the better configurations are more likely to be measured before the others. If the performance of a good configuration has been measured, a poor configuration will be quickly found to be suboptimal with short measurement time. We apply QOG to optimizing the configuration of a real Web system, and find that QOG can drastically reduce the total measurement time needed to select the best configuration. Our experiments also illuminate several interesting properties of QOG specifically when it is applied to optimizing Web systems.

References

  1. T. W. Anderson. A modification of the sequential probability ratio test to reduce the sample size. Annals of Mathematical Statistics, 31:165--197, 1960.Google ScholarGoogle ScholarCross RefCross Ref
  2. R. E. Bechhofer, T. J. Santner, and D. M. Goldsman. Design and analysis of experiments for statistical selection, screening, and multiple comparisons. John Wiley & Sons, 1995.Google ScholarGoogle Scholar
  3. I. Chung and J. K. Hollingsworth. Automated cluster--based web service performance tuning. In Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing, pages 36--44, June 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. L. J. Hong and B. L. Nelson. The tradeoff between sampling and switching: New sequential procedures for indifference-zone selection. IIE Transactions, 37(7):623--634, 2005.Google ScholarGoogle ScholarCross RefCross Ref
  5. S. Kim and B. L. Nelson. A fully sequential procedure for indifference-zone selection in simulation. ACM Transactions on Modeling and Computer Simulation, 11(3):251--273, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. A. M. Law and W. D. Kelton. Simulation Modeling and Analysis. McGraw-Hill, third edition, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. X. Liu, L. Sha, S. Froehlich, J. L. Hellerstein, and S. Parekh. Online response time optimization of Apache Web server. In Proceedings of the 11th International Workshop on Quality of Service (IWQoS 2003), pages 461--478, June 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. T. M. Mitchell. Machine Learning. McGraw-Hill, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. B. L. Nelson, J. Swann, D. Goldsman, and W. Song. Simple procedures for selecting the best simulated system when the number of alternatives is large. Operations Research, 49(6):950--963, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. T. Osogami. Finding probably best systems quickly via simulations. Technical Report RT0684, IBM Tokyo Research Laboratory, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  11. T. Osogami and T. Itoko. Finding probably better system configurations quickly. In Proceedings of the Joint International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS/PERFORMANCE 2006), pages 264--275, June 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Raghavachari, D. Reimer, and R. D. Johnson. The deployer's problem: Configuring application servers for performance and reliability. In Proceedings of the 25th International Conference on Software Engineering, pages 484--489, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. J. R. Swisher, S. H. Jacobson, and E. Yücesan. Discrete-event simulation optimization using ranking, selection, and multiple comparison procedures: A survey. ACM Transactions on Modeling and Computer Simulation, 13(2):134--154, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. B. Xi, Z. Liu, M. Raghavachari, C. H. Xia, and L. Zhang. A smart hill-climbing algorithm for application server configuration. In Proceedings of the 13th International Conference on World Wide Web, pages 287--296, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Y. Zhang, W. Qu, and A. Liu. Automatic performance tuning for J2EE application server systems. In Proceedings of the 6th International Conference on Web Information Systems Engineering (WISE 2005), pages 520--527, November 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Optimizing system configurations quickly by guessing at the performance

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        SIGMETRICS '07: Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
        June 2007
        398 pages
        ISBN:9781595936394
        DOI:10.1145/1254882
        • cover image ACM SIGMETRICS Performance Evaluation Review
          ACM SIGMETRICS Performance Evaluation Review  Volume 35, Issue 1
          SIGMETRICS '07 Conference Proceedings
          June 2007
          382 pages
          ISSN:0163-5999
          DOI:10.1145/1269899
          Issue’s Table of Contents

        Copyright © 2007 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 12 June 2007

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • Article

        Acceptance Rates

        Overall Acceptance Rate459of2,691submissions,17%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader