Cluster Computing

, Volume 15, Issue 4, pp 363–371 | Cite as

Stochastic bounds for composite Web services response times

  • Lynda Mokdad
  • Samir Youcef


In this paper, we propose bounding models, which provide upper and lower bounds on response time in composite Web service model, for alleviating the state explosion problem. The considered models have heterogeneous servers and the number of elementary Web services can be very large. More precisely, we study two types of composite Web services. First, we investigate the performance of a single composite Web service execution instance. Second, this assumption is relaxed (i.e. multiple composite Web services execution instances are considered). These models allows to find trade-off between the accuracy of the bounds and the computation complexity.


Web services Process coupling Response time Markov Chain 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Menascé, D.A.: Response-time analysis of composite Web services. IEEE Internet Comput. 8(1), 90–92 (2004) CrossRefGoogle Scholar
  2. 2.
    Menascé, D.A.: Static and dynamic processor scheduling disciplines in heterogeneous parallel architectures. J. Parallel Distrib. Comput., 28(1), 1–18 (1995) MATHCrossRefGoogle Scholar
  3. 3.
    Menascé, D.A., Mason, G.: QoS issues in Web services. IEEE Internet Comput., 6(6), 72–74 (2002) CrossRefGoogle Scholar
  4. 4.
    Massey, W.: Stochastic orderings for Markov processes on partially ordered spaces. Mathematics of Operations Research 12(2), 350–367 (1987) MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    Lindvall, T.: Lectures on the Coupling Method. Wiley Series in Probability and Mathematical Statistics. Dover, New York (1992) MATHGoogle Scholar
  6. 6.
    Lui, J.C.S., Muntz, R.R., Towsley, D.: Computing performance bounds for fork-join queuing models 9(3), 295–311 (1998) Google Scholar
  7. 7.
    Baccelli, F., Makowski, A.M., Shwartz, A.: Simple computable bounds and approximations for fork-join queue. In: Intern. Workshop on Computer Performance Evaluation, Tokyo, September 1985, pp. 437–450 (1985) Google Scholar
  8. 8.
    Tan, Z., Lin, C., Yin, H., Hong, Y., Zhu, G.: Approximate performance analysis of Web services flow using stochastic Petri net. In: GCC 2004. Lecture Notes in Computer Science, vol. 3251, pp. 193–200 (2004) Google Scholar
  9. 9.
    Tidwell, D.: Web services—The Web’s next revolution. IBM developerWorks (2000) Google Scholar
  10. 10.
    Scharf, M.: On the response time of the large-scale composite Web services. In: Proceedings of the 19th International Teletraffic Congress (ITC 19), Beijing (2005) Google Scholar
  11. 11.
    Stoyan, D.: Comparison Methods for Queues and Other Stochastic Models. Wiley, New York (1976) Google Scholar
  12. 12.
    Gudgin, M., Hadley, M., Mendelsohn, N., Moreau, J., Nielsen, H.: Simple Object Access Protocol (SOAP) 1.1. World Wide Web Consortium (2000) Google Scholar
  13. 13.
    Bellwood, T., Clément, L., von Riegen, C.: Universal description, discovery and integration. OASIS UDDI Specification Technical Committee Google Scholar
  14. 14.
    Block, G., Greiner, S., De Meer, H., Trivedi, K.S.: Queueing Networks and Markov Chains. Wiley, New York (1998) Google Scholar
  15. 15.
    Nelson, R., Tantawi, A.N.: Approximate analysis of fork/join synchronization in parallel queues. IEEE Trans. Comput. 37(6), 739–743 (1998) CrossRefGoogle Scholar
  16. 16.
    Hu, M.: Web services composition, partition, and quality of service in distributed system integration and re-engineering. In: IEEE, Internet Computing (2005) Google Scholar
  17. 17.
    Christensen, E., Curbera, F., Meredith, G., Weerawarana, S.: Web Services Description Language (WSDL) 1.1. World Wide Web Consortium (2003) Google Scholar
  18. 18.
    Weerawarana, S., Curbera, F.: Business Process Execution Language for Web Services. IBM Corporation (2002) Google Scholar
  19. 19.
    Vallamsetty, U., Kant, K., Mohapatra, P.: Characterization of e-commerce traffic. Electron. Commer. Res., 3(1–2), 167–192 (2003) CrossRefGoogle Scholar
  20. 20.
    Crovella, M.E., Bestavros, A.: Self-similarity in World Wide Web traffic: evidence and possible causes. IEEE/ACM Trans. Netw., 5(6), 835–846 (1997) CrossRefGoogle Scholar
  21. 21.
    Kiczales, G.: Aspect-oriented programming. ACM Comput. Surv. 154 (1996) Google Scholar
  22. 22.
    van der Aalst, W.M.P., ter Hofstede, A.H.M., Kiepuszewski, B., Barros, A.P.: Workow patterns. Technical report FIT-TR-2002-2, Faculty of IT, Queensland University of Technology, July (2002). Accessed from
  23. 23.
    Haddad, S., Mokdad, L., Youcef, S.: Response time analysis of composite Web services. In: 6th IEEE Symposium on Communication Systems, Networks and Digital Signal Processing (CNDSPS’08), Graz, July, pp. 1–5 (2008) Google Scholar
  24. 24.
    Kasse, Y., Mokdad, L., Youcef, S.: Stochastic bounds for end-to-end delay in Web service model. In: Proceeding of IEEE Symposium on Computers and Communications (ISCC’10), Riccione, Italy, June, pp. 1121–1126. IEEE Communication Society, New York (2010) Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.LACL LaboratoryUniversity of Paris-EstParisFrance
  2. 2.LAMSADE LaboratoryUniversity of Paris-DauphineParisFrance

Personalised recommendations