A Comprehensive Toolset for Workload Characterization, Performance Modeling, and Online Control

  • Li Zhang
  • Zhen Liu
  • Anton Riabov
  • Monty Schulman
  • Cathy Xia
  • Fan Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2794)


With the advances of computer hardware and software technologies, electronic businesses are moving towards the on-demand era, where services and applications can be deployed or accommodated in a dynamic and autonomic fashion. This leads to a more flexible and efficient way to manage various system resources. For on-demand services and applications, performance modeling and analysis play key roles in many aspects of such an autonomic system. In this paper, we present a comprehensive toolset developed for workload characterization, performance modeling and analysis, and on-line control. The development of the toolset is based on state-of-the art techniques in statistical analysis, queueing theory, scheduling techniques, and on-line control methodologies. Built on a flexible software architecture, this toolset provides significant value for key business processes. This includes capacity planning, performance prediction, performance engineering and on-line control of system resources.


Performance analysis performance prediction capacity planning Web service modeling queueing networks on-line control 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bigus, J.P., Bigus, J.: Constructing Intelligent Agents with JavaTM: A Programmer’s Guide to Smarter Applications. John Wiley & Sons, Chichester; Book and CD-ROM edition (December 1997) ISBN 0471191353Google Scholar
  2. 2.
    Bigus, J.P., Schlosnagle, D., et al.: Agent Building and Learning Environment,
  3. 3.
    Dmers, A., Keshav, S., Shenker, S.: Analysis and Simulation of Fair Queueing System. Internetworking Research and Experience 1 (1990)Google Scholar
  4. 4.
    Fayolle, G., Mitrani, I., Iasnogorodski, R.: Sharing a Processor Among Many Job Classes. J. ACM 14(2) (1967)Google Scholar
  5. 5.
    Fong, L.L., Kalantar, M.H., Pazel, D.P., Goldszmidt, G., Appleby, K., Eilam, T., Fakhouri, S.A., Krishnakumar, S.M., Miller, S., Pershing, J.A.: Dynamic Resource Management in an eUtility. In: Proceedings of NOMS 2002 IEEE/IFIP Network Operations and Management Symposium, Piscataway, NJ, pp. 727–740. IEEE, Los Alamitos (2002)Google Scholar
  6. 6.
    Hale, M., et al.: JSci - A science API for JavaTM,
  7. 7.
    Hunt, G., Goldszmidt, G., King, R., Mukherjee, R.: Network dispatcher: A connection router for scalable internet services. In: Proceedings of the 7th International World Wide Web Conference (April 1998)Google Scholar
  8. 8.
    Iyengar, A.K., Squillante, M.S., Zhang, L.: Analysis and characterization of large-scale web server access patterns and performance. World Wide Web 2 (June 1999)Google Scholar
  9. 9.
    Kleinrock, L.: Queueing Systems: Computer Applications, vol. II. John Wiley and Sons, Chichester (1976)zbMATHGoogle Scholar
  10. 10.
    Liu, Z., Niclausse, N., Jalpa-Villanueva, C.: Web traffic modeling and performance comparison between HTTP 1.0 and HTTP 1.1. In: Gelenbe, E. (ed.) Systems Performance Evaluation: Methodologies and Applications, pp. 177–189. CRC Press, Boca Raton (2000)Google Scholar
  11. 11.
    Liu, Z., Squillante, M.S., Xia, C.H., Zhang, L.: Preliminary analysis of various SurfAid customers. Technical report, IBM Research Division (July 2000) (revised December 2000)Google Scholar
  12. 12.
    Liu, Z., Squillante, M.S., Xia, C.H., Yu, S., Zhang, L.: Web Traffic Profiling, Clustering and Classification for Commercial Web Sites. In: The 10th International Conference on Telecommunication Systems, Modeling and Analysis, ICTSM10 (2002)Google Scholar
  13. 13.
    Menasce, D.A., Almeida, V.A.F.: Capacity Planning for Web Performance: metrics, models, and methods. Prentice-Hall, Englewood Cliffs (1998)Google Scholar
  14. 14.
    Parekh, A.K., Gallager, R.G.: A Generalized Processor Sharing Approach to Flow Control in Integrated Services Networks: The Single-Node Case. IEEE Transactions on Networking 1(3) (1993)Google Scholar
  15. 15.
    Squillante, M.S., Woo, B., Zhang, L.: Analysis of queues with dependent arrival processes and general service processes. Technical report, IBM Research Division (2000)Google Scholar
  16. 16.
    Squillante, M.S., Yao, D.D., Zhang, L.: Web traffic modeling and web server performance analysis. In: Proceedings of the IEEE Conference on Decision and Control (December 1999)Google Scholar
  17. 17.
    Squillante, M.S., Yao, D.D., Zhang, L.: Internet traffic: Periodicity, tail behavior and performance implications. In: Gelenbe, E. (ed.) Systems Performance Evaluation: Methodologies and Applications. CRC Press, Boca Raton (2000)Google Scholar
  18. 18.
    Sun Microsystems, Inc. JavaTM 2 Platform, Standard Edition (J2SETM),
  19. 19.
    Wolff, R.W.: Stochastic Modeling and the Theory of Queues. Prentice-Hall, Englewood Cliffs (1989)zbMATHGoogle Scholar
  20. 20.
    Zhang, L., Xia, C.H., Squillante, M.S., Mills III, W.N.: Workload service sequirements analysis: A queueing network optimization approach. In: Tenth IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, MASCOTS (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Li Zhang
    • 1
  • Zhen Liu
    • 1
  • Anton Riabov
    • 1
  • Monty Schulman
    • 1
  • Cathy Xia
    • 1
  • Fan Zhang
    • 1
  1. 1.IBM Thomas J. Watson Research CenterYorktown Heights

Personalised recommendations