skip to main content
10.1145/1808885.1808900acmconferencesArticle/Chapter ViewAbstractPublication PagesicseConference Proceedingsconference-collections
research-article

Protecting SLAs with surrogate models

Authors Info & Claims
Published:01 May 2010Publication History

ABSTRACT

In this paper, we propose the use of surrogate models to avoid or limit violations of the service level agreements (protect SLAs) of enterprise applications executed within virtualized data centers (VDCs).

Modern enterprise services are delivered along with service level agreements (SLAs) that formalize the expected quality of service, and define penalties in case of violations. By deploying enterprise applications within VDCs, providers can dynamically change the execution configuration of the services to react to unplanned environmental conditions, like sudden changes in the workload mix and intensity, with the goal of avoiding SLA violations while reducing operational costs with respect to traditional over-provisioning solutions.

Surrogate models are successfully used in modern engineering to approximate systems' behavior, and thus support a wide scope of activities, especially design optimization. In this paper, we show that by reducing the problem of protecting SLAs in VDCs to an optimization problem, we can adapt surrogate models to this new framework and implement SLA protection controller components. In the paper, we present the main ideas, we illustrate how surrogate models can be used to protect SLAs, and we discuss preliminary results obtained on a case study deployed in an industrial virtualized infrastructure.

References

  1. D. Ardagna, C. Ghezzi, and R. Mirandola. Rethinking the use of models in software architecture. In Proc. of International Conference Series on the Quality of Software Architectures, pages 1--27, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Y. Brun, G. D. M. Serugendo, C. Gacek, H. Giese, H. M. Kienle, M. Litoiu, H. A. Müller, M. Pezzè, and M. Shaw. Engineering self-adaptive systems through feedback loops. In Software Engineering for Self-Adaptive Systems, pages 48--70, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. S. Duan and S. Babu. Proactive identification of performance problems. In Proc. of ACM SIGMOD international conference on Management of data, pages 766--768, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. R. B. Gramacy, H. K. H. Lee, and W. G. Macready. Parameter space exploration with gaussian process trees. In Proc. of the international conference on Machine learning, pages 353--360, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. IBM. An Architectural Blueprint for Autonomic Computing. Technical report, IBM, 2003.Google ScholarGoogle Scholar
  6. R. Jin, X. Du, and W. Chen. The use of metamodeling techniques for optimization under uncertainty. Structural and Multidisciplinary Optimization, 25(2):99--116, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  7. G. Jung, K. Joshi, M. Hiltunen, R. Schlichting, and C. Pu. Generating adaptation policies for multi-tier applications in consolidated server environments. In Proc. of International Conference on Autonomic Computing, pages 23--32, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. J. O. Kephart and D. M. Chess. The vision of autonomic computing. IEEE Computer, 36(1):41--50, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. P. Leitner, B. Wetzstein, F. Rosenberg, A. Michlmayr, S. Dustdar, and F. Leymann. Runtime prediction of service level agreement violations for composite services. In Proc. of the Workshop on Non-Functional Properties and SLA Management in Service-Oriented Computing, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. G. Mariani, G. Palermo, C. Silvano, and V. Zaccaria. Meta-model assisted optimization for design space exploration of multi-processor systems-on-chip. In Proc. of Euromicro Conference on Digital System Design, pages 383--389, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. D. C. Montgomery. Design and Analysis of Experiments. Wiley, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. S. Parekh, N. Gandhi, J. Hellerstein, D. Tilbury, T. Jayram, and J. Bigus. Using control theory to achieve service level objectives in performance management. Real-Time Syst., 23(1/2):127--141, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. The Reservoir Seed Team. Reservoir - An ICT infrastructure for reliable and effective delivery of services as utilities. Technical Report H-0262, IBM Research Division, 2008.Google ScholarGoogle Scholar
  14. B. Urgaonkar, G. Pacifici, P. Shenoy, M. Spreitzer, and A. Tantawi. Analytic modeling of multitier internet applications. ACM Transactions on the Web, 1(1):2--37, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. W. van Beers and J. Kleijnen. Kriging interpolation in simulation: a survey. In Proc. of conference on Winter simulation, pages 113--121, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. G. G. Wang and S. Shan. Review of metamodeling techniques in support of engineering design optimization. Mechanical Design, 129(4):370--380, 2007.Google ScholarGoogle ScholarCross RefCross Ref
  17. Y. Wang, M. J. Rutherford, A. Carzaniga, and A. L. Wolf. Automating experimentation on distributed testbeds. In Proc. of International Conference on Automated Software Engineering, pages 164--173, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. M. Woodside, T. Zheng, and M. Litoiu. Service system resource management based on a tracked layered performance model. In Proc. of International Conference on Autonomic Computing, pages 175--184, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. X. Zhu, M. Uysal, Z. Wang, S. Singhal, A. Merchant, P. Padala, and K. Shin. What does control theory bring to systems research? ACM SIGOPS Operating Systems Review, 43(1):62--69, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Protecting SLAs with surrogate models

          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
            PESOS '10: Proceedings of the 2nd International Workshop on Principles of Engineering Service-Oriented Systems
            May 2010
            91 pages
            ISBN:9781605589633
            DOI:10.1145/1808885

            Copyright © 2010 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: 1 May 2010

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article

            Upcoming Conference

            ICSE 2025

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader