Skip to main content
Log in

Translating Service Level Objectives to lower level policies for multi-tier services

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Service providers and their customers agree on certain quality of service guarantees through Service Level Agreements (SLA). An SLA contains one or more Service Level Objectives (SLO)s that describe the agreed-upon quality requirements at the service level. Translating these SLOs into lower-level policies that can then be used for design and monitoring purposes is a difficult problem. Usually domain experts are involved in this translation that often necessitates application of domain knowledge to this problem. In this article, we propose an approach that combines performance modeling with regression analysis to solve this problem. We demonstrate that our approach is practical and that it can be applied to different n-tier services. Our experiments show that for a typical 3-tier e-commerce application in a virtualized environment, the SLA can be met while improving CPU utilization by up to 3 times.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Barham, P., et al.: Xen and the art of virtualization. In: Proc. of the Nineteenth ACM SOSP, 2003

  2. Cecchet, E., Chanda, A., Elnikety, S., Marguerite, J., Zwaenepoel, W.: A comparison of software architectures for E-business applications. In: Proc. of 4th Middleware Conference, Rio de Janeiro, Brazil, June 2003

  3. Chandra, A., Gong, W., Shenoy, P.: Dynamic resource allocation for shared data centers using online measurements. In: Proc. of International Workshop on Quality of Service, June 2003

  4. Cockcroft, A., Walker, B.: Capacity Planning for Internet Services. Sun Press, Cleveland (2001)

    Google Scholar 

  5. Council, T.P.C.: TPC-W. http://www.tpc.org/tpcw

  6. Doyle, R., Chase, J., Asad, O., Jin, W., Vahdat, A.: Model-based resource provisioning in a Web service utility. In: Proc. of the 4th USENIX USITS, Mar. 2003

  7. Edward, D., Lazowska, J., Zahorjan, G., Graham, S., Sevcik, K.C.: Quantitative System Performance: Computer System Analysis Using Queueing Network Models. Prentice-Hall, Englewood Cliffs (1984)

    Google Scholar 

  8. Gennaro, C., King, P.J.B.: Parallelising the mean value analysis algorithm. Simulation 72(3), 148 (1999). doi:10.1177/003754979907200304

    Article  Google Scholar 

  9. Graupner, S., Kotov, V., Trinks, H.: Resource-sharing and service deployment in virtual data centers. In: Proc. of the 22nd ICDCS, pp. 666–674, July 2002

  10. Kelley, T.: Detecting performance anomalies in global applications. In: Proc. of Second USENIX Workshop on Real, Large Distributed Systems (WORLDS 2005), 2005

  11. Levy, R., Nagarajarao, J., Pacifici, G., Spreitzer, M., Tantawi, A., Yousse, A.: Performance management for cluster based Web services. In: Proc. of IFIP/IEEE 8th IM, 2003

  12. Liu, X., Heo, J., Sha, L.: Modeling 3-tiered Web applications. In: Proc. of 13th IEEE MASCOTS, Atlanta, Georgia, 2005

  13. Menasce, D., Almeida, V.: Capacity Planning for Web Services: Metrics, Models, and Methods. Prentice-Hall PTR, Englewood Cliffs (2001)

    Google Scholar 

  14. Reiser, M., Lavenberg, S.S.: Mean-value analysis of closed multichain queueing networks. J. ACM 27, 313–322 (1980). doi:10.1145/322186.322195

    Article  MATH  MathSciNet  Google Scholar 

  15. RUBiS: Rice University Bidding System. http://www.cs.rice.edu/CS/Systems/DynaServer/rubis

  16. Seidmann, A., Schweitzer, P.J., Shalev-Oren, S.: Computerized closed queueing network models of flexible manufacturing systems. Large Scale Syst. 12, 91–107 (1987)

    MATH  MathSciNet  Google Scholar 

  17. Slothouber, L.: A model of Web server performance. In: Proc. of Int’l World Wide Web Conference, 1996

  18. Stewart, C., Shen, K.: Performance modeling and system management for multi-component online services. In: Proc. of USENIX NSDI, 2005

  19. Stewart, C., Kelly, T., Zhang, A.: Exploiting nonstationarity for performance prediction. In: Proc. of EuroSys, 2007

  20. Urgaonkar, B., Shenoy, P.: Cataclysm. Handling extreme overloads in Internet services. In: Proc. of ACM SIGACT-SIGOPS PODC, July 2004

  21. Urgaonkar, B., Pacifici, G., Shenoy, P., Spreitzer, M., Tantawi, A.: An analytical model for multi-tier Internet services and its applications. In: Proc. of ACM SIGMETRICS, June 2005

  22. Urgaonkar, B., Shenoy, P., Chandra, A., Goyal, O.P.: Dynamic provisioning of multi-tier Internet applications. In: Proc. of IEEE ICAC, June 2005

  23. VMware, Inc., VMware ESX Server User’s Manual Version 1.5, Palo Alto, CA, April 2002

  24. Yaikhom, G., Cole, M., Gilmore, S.: Combining measurement and stochastic modelling to enhance scheduling decisions for a parallel mean value analysis algorithm. In: Proc. of International Conference on Computational Science (ICCS 2006), LNCS. Springer, Berlin (2006)

  25. Zhang, A., Santos, P., Beyer, D., Tang, H.: Optimal server resource allocation using an open queueing network model of response time. HP Labs Technical Report, HPL-2002-301

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yuan Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, Y., Iyer, S., Liu, X. et al. Translating Service Level Objectives to lower level policies for multi-tier services. Cluster Comput 11, 299–311 (2008). https://doi.org/10.1007/s10586-008-0059-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-008-0059-6

Keywords

Navigation