Skip to main content
Log in

A regression-based analytic model for capacity planning of multi-tier applications

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The multi-tier implementation has become the industry standard for developing scalable client-server enterprise applications. Since these applications are performance sensitive, effective models for dynamic resource provisioning and for delivering quality of service to these applications become critical. Workloads in such environments are characterized by client sessions of interdependent requests with changing transaction mix and load over time, making model adaptivity to the observed workload changes a critical requirement for model effectiveness. In this work, we apply a regression-based approximation of the CPU demand of client transactions on a given hardware. Then, we use this approximation in an analytic model of a simple network of queues, each queue representing a tier, and show the approximation’s effectiveness for modeling diverse workloads with a changing transaction mix over time. Using two case studies, we investigate factors that impact the efficiency and accuracy of the proposed performance prediction models. Experimental results show that this regression-based approach provides a simple and powerful solution for efficient capacity planning and resource provisioning of multi-tier applications under changing workload conditions.

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.

Institutional subscriptions

Similar content being viewed by others

References

  1. Ari, B., Giivenir, H.A.: Clustered linear regression. Knowl.-Based Syst. 15(3) (2002)

  2. Arlitt, M., Williamson, C.: Web server workload characterization: the search for invariants. In: Proc. of the ACM SIGMETRICS ’96 Conference, Philadelphia, PA, May 1996

  3. Almeida, V., Bestavros, A., Crovella, M., de Oliveira, A.: Characterizing reference locality in the WWW. Technical Report, Boston University, TR-96-11 (1996)

  4. Arlitt, M., Krishnamurthy, D., Rolia, J.: Characterizing the scalability of a large web-based shopping system. J. ACM Trans. Internet Technol. 1(1) (2001)

  5. Capacity Planning for WebLogic Portal: URL http://edocs.bea.com/wlp/docs81/capacityplanning/capacityplanning.html

  6. Cherkasova, L., Phaal, P.: Session based admission control: a Mechanism for Peak Load Management of Commercial Web Sites. IEEE J. Trans. Comput. 51(6) (2002)

  7. Kachigan, T.M.: A multi-dimensional approach to capacity planning. In: Proc. of CMG Conference, Boston, MA (1980)

  8. Kelly, T.: Detecting Performance Anomalies in Global Applications. Second Workshop on Real, Large Distributed Systems (WORLDS’2005) (2005)

  9. Kelly, T., Zhang, A.: Predicting performance in distributed enterprise applications. HPLabs Tech Report, HPL-2006-76, May 2006

  10. Krishnamurthy, D., Rolia, J., Majumdar, S.: A synthetic workload generation technique for stress testing session-based systems. IEEE Trans. Softw. Eng. 32(11) (2006)

  11. Mi, N., Zhang, Q., Riska, A., Smirni, E., Riedel, E.: Performance impacts of autocorrelated flows in multi-tiered systems. Perform. Eval. 64(9–12), 1082–1101 (2007)

    Article  Google Scholar 

  12. Lampman, D.: Building the Next Generation of IT. URL www.hpl/hp.com/news/2006/apr-jun/technology.html

  13. Menasce, D., Almeida, V., Dowdy, L.: Capacity Planning and Performance Modeling: from Mainframes to Client-Server Systems. Prentice Hall, New York (1994)

    Google Scholar 

  14. Menasce, D., Almeida, V.: Scaling for E-Business: Technologies, Models, Performance, and Capacity Planning. Prentice Hall, New York (2000)

    Google Scholar 

  15. PHP HyperText preprocessor: www.php.net

  16. Ranjan, S., Rolia, J., Fu, H., Knightly, E.: QoS-driven server migration for Internet data centers. In: Proc. of IWQoS’2002, Miami (2002)

  17. Rolia, J., Vetland, V.: Correlating resource demand information with ARM data for application services. In: Proc. of the ACM Workshop on Software and Performance (1998)

  18. Schwetman, H.: Object-oriented simulation modeling with C++/CSIM. In: Proc. of 1995 Winter Simulation Conference, Washington, DC (1995)

  19. The Workload for the SPECweb96 Benchmark: URL http://www.specbench.org/osg/web96/workload.html

  20. TPC-W Benchmark: URL http://www.tpc.org

  21. Urgaonkar, B., Shenoy, P., Chandra, A., Goyal, P.: Dynamic provisioning of multi-tier Internet applications. In: Proc. of the 2nd IEEE International Conference on Autonomic Computing (ICAC-05), Seattle, June 2005

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

  23. Villela, D., Pradhan, P., Rubenstein, D.: Provisioning servers in the application tier for E-commerce systems. In: Proc. of IWQoS’04, Montreal, Canada (2004)

  24. Zhang, Q.: The effect of workload dependence in systems: experimental evaluation, analytic models, and policy development. PhD thesis, College of William and Mary, December 2006

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qi Zhang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhang, Q., Cherkasova, L., Mi, N. et al. A regression-based analytic model for capacity planning of multi-tier applications. Cluster Comput 11, 197–211 (2008). https://doi.org/10.1007/s10586-008-0052-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-008-0052-0

Keywords

Navigation