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Capacity Planning for Web Services Techniques and Methodology

  • Virgilio A. F. Almeida
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2459)

Abstract

Capacity planning is a powerful tool for managing quality of service on the Web. This tutorial presents a capacity planning methodology for Web-based environments, where the main steps are: understanding the environment, characterizing the workload, modeling the workload, validating and calibrating the models, forecasting the workload, predicting the performance, analyzing the cost-performance plans, and suggesting actions. The main steps are based on two models: a workload model and a performance model. The first model results from understanding and characterizing the workload and the second from a quantitative description of the system behavior. Instead of relying on intuition, ad hoc procedures and rules of thumb to understand and analyze the behavior of Web services, this tutorial emphasizes the role of models, as a uniform and formal way of dealing with capacity planning problems.

Keywords

Capacity Planning State Transition Graph Queue Network Model Workload Model Workload Characterization 
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.

References

  1. 1.
    V. Almeida and D. Menascé, “Capacity Planning: an essential tool for managing Web services”, IEEE IT Pro, Vol. 4, Issue 4, July–August, 2002.Google Scholar
  2. 2.
    M. Arlitt and C. Williamson, “InternetWeb Servers: workload characterization and performance implication”, in IEEE/ACM Trans. on Networking, October 1997.Google Scholar
  3. 3.
    M. Arlitt, D. Krishnamurthy, and J. Rolia, “Workload Characterization and Performance Scalability of a Large Web-based Shopping System”, in ACM Transactions on Internet Technologies, Vol.1, No. 1, Aug. 2001.Google Scholar
  4. 4.
    M. Calzarossa and G. Serazzi, “Workload Characterization: A Survey,” Proceedings of the IEEE, Vol. 81, No. 8, August 1993.Google Scholar
  5. 5.
    M. Crovella and A. Bestravos, “ Self-Similarity in the World Wide Web: evidence possible causes”, in IEEE/ACM Transactions on Networking, 5(6):835–846, December 1997.CrossRefGoogle Scholar
  6. 6.
    P. Denning and J. Buzen, “The operational analysis of queuing network models”, Computing Surveys, Vol. 10, No. 3, September 1978, pp. 225–261.zbMATHCrossRefGoogle Scholar
  7. 7.
    R. Jain, The Art of Computer Systems Performance Analysis. New York: Wiley, 1991.zbMATHGoogle Scholar
  8. 8.
    K. Kant and Y. Won “Server Capacity Planning for Web Traffic Workload”, in IEEE Trans. on Knowledge and Data Engineering, September 1999.Google Scholar
  9. 9.
    D. Krishnamurthy and J. Rolia, “Predicting the Performance of an E-Commerce Server: Those Mean Percentiles,” in Proc. First Workshopon Internet Server Performance, ACM SIGMETRICS 98, June 1998.Google Scholar
  10. 10.
    A. Law and W. Kelton, Simulation Modeling and Techniques. 2nd ed. New York: McGraw-Hill, 1990.Google Scholar
  11. 11.
    D. Levine, P. Ramsey, R. Smidt, Applied Statistics for Engineers and Scientists: Using Microsoft Excel & MINITAB, Upper Saddle River, Prentice Hall, 2001zbMATHGoogle Scholar
  12. 12.
    J. Martinich, Production and Operations Management: An Applied Modern Approach, John Wiley & Sons, 1996.Google Scholar
  13. 13.
    D. A. Menascé, V. A. F. Almeida, and L. W. Dowdy, Capacity Planning and Performance Modeling: From Mainframes to Client-Server Systems. Upper Saddle River, NJ: Prentice Hall, 1994.Google Scholar
  14. 14.
    D. A. Menascé, D. Dregits, R. Rossin, and D. Gantz, A federation-oriented capacity management methodology for LAN environments, Proc. 1995 Conf. Comput. Measurement Group, Nashville, TN, Dec. 3–8, 1995Google Scholar
  15. 15.
    D. A. Menascé, V. Almeida, R. Fonseca, and M. Mendes, “A Methodology for Workload Characterization for E-Commerce Servers”, Proc. 1999 ACM Conference in Electronic Commerce, Denver, 1999.Google Scholar
  16. 16.
    D. A. Menascé and V. A. F. Almeida, Scaling for E-Business: technologies, models, performance and capacity planning, Prentice Hall, Upper Saddle River, 2000.Google Scholar
  17. 17.
    D. A. Menascé, V. A. F. Almeida, R. Fonseca, and M. A. Mendes, “Businessoriented Resource Management Policies for E-Commerce Servers,” Performance Evaluation, September 2000.Google Scholar
  18. 18.
    D. A. Menascé, V. Almeida, R. Fonseca, R. Riedi, F. Ribeiro, and W. Meira Jr., “In Search of Invariants for E-Business Workloads ”, Proc. 2000 ACM Conference in Electronic Commerce, Minneapolis, 2000.Google Scholar
  19. 19.
    D. A. Menascé and V. A. F. Almeida, Capacity Planning for Web Services: metrics, models and methods, Prentice Hall, Upper Saddle River, 2002.Google Scholar
  20. 20.
    V. Paxson and S. Floyd, “Wide area traffic: The failure of Poisson modeling,” IEEE/ACM Transactions on Networking 3, pp. 226–244, 1995.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Virgilio A. F. Almeida
    • 1
  1. 1.Department of Computer ScienceFederal University of Minas GeraisBelo HorizonteBrazil

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