Service Adaptation with Probabilistic Partial Models

  • Manman Chen
  • Tian Huat Tan
  • Jun Sun
  • Jingyi Wang
  • Yang Liu
  • Jing Sun
  • Jin Song Dong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10009)


Web service composition makes use of existing Web services to build complex business processes. Non-functional requirements are crucial for the Web service composition. In order to satisfy non-functional requirements when composing a Web service, one needs to rely on the estimated quality of the component services. However, estimation is seldom accurate especially in the dynamic environment. Hence, we propose a framework, ADFlow, to monitor and adapt the workflow of the Web service composition when necessary to maximize its ability to satisfy the non-functional requirements automatically. To reduce the monitoring overhead, ADFlow relies on asynchronous monitoring. ADFlow has been implemented and the evaluation has shown the effectiveness and efficiency of our approach. Given a composite service, ADFlow achieves 25 %–32 % of average improvement in the conformance of non-functional requirements, and only incurs 1 %–3 % of overhead with respect to the execution time.


Service Composition Component Service Global Constraint Composite Service Guard Condition 
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.


  1. 1.
  2. 2.
  3. 3.
  4. 4.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E., et al.: A survey on sensor networks. IEEE Commun. Mag. 40(8), 102–114 (2002)CrossRefGoogle Scholar
  5. 5.
    Cardellini, V., Casalicchio, E., Grassi, V., Iannucci, S., Presti, F.L., Mirandola, R.: Moses: a framework for qos driven runtime adaptation of service-oriented systems. TSE 38(5), 1138–1159 (2012)Google Scholar
  6. 6.
    Chen, M., Tan, T.H., Sun, J., Liu, Y., Dong, J.S.: VeriWS: a tool for verification of combined functional and non-functional requirements of web service composition. In: ICSE, pp. 564–567 (2014)Google Scholar
  7. 7.
    Chen, M., Tan, T.H., Sun, J., Liu, Y., Pang, J., Li, X.: Verification of functional and non-functional requirements of web service composition. In: ICFEM, pp. 313–328 (2013)Google Scholar
  8. 8.
    Chinnici, R., Moreau, J.-J., Ryman, A., Weerawarana, S.: Web services description language (WSDL) version 2.0.
  9. 9.
    Epifani, I., Ghezzi, C., Mirandola, R., Tamburrelli, G.: Model evolution by run-time parameter adaptation. In: ICSE, pp. 111–121 (2009)Google Scholar
  10. 10.
    Ermedahl, A., Sandberg, C., Gustafsson, J., Bygde, S., Lisper, B.: Loop bound analysis based on a combination of program slicing, abstract interpretation, and invariant analysis. In: WCET (2007)Google Scholar
  11. 11.
    Famelis, M., Salay, R., Chechik, M.: Partial models: towards modeling and reasoning with uncertainty. In: ICSE, pp. 573–583 (2012)Google Scholar
  12. 12.
    Foster, H.: A rigorous approach to engineering web service compositions. Ph.D. thesis, Citeseer (2006)Google Scholar
  13. 13.
    Fung, C.K., Hung, P.C.K., Wang, G., Linger, R.C., Walton, G.H.: A study of service composition with QoS management. In: ICWS, pp. 717–724 (2005)Google Scholar
  14. 14.
    Gudgin, M., Hadley, M., Mendelsohn, N., Moreau, J.-J., Nielsen, H.F., Karmarkar, A., Lafon. Y.: Simple object access protocol (SOAP) version 1.2.
  15. 15.
    Irmert, F., Fischer, T., Meyer-Wegener, K.: Runtime adaptation in a service-oriented component model. In: SEAMS, pp. 97–104. ACM (2008)Google Scholar
  16. 16.
    Koizumi, S., Koyama, K.: Workload-aware business process simulation with statistical service analysis and timed Petri Net. In: ICWS, pp. 70–77 (2007)Google Scholar
  17. 17.
    Moser, O., Rosenberg, F., Dustdar, S.: Non-intrusive monitoring and service adaptation for WS-BPEL. In: WWW, pp. 815–824 (2008)Google Scholar
  18. 18.
    Mukhija, A., Glinz, M.: Runtime adaptation of applications through dynamic recomposition of components. In: ARCS, pp. 124–138 (2005)Google Scholar
  19. 19.
    Tan, T.H.: Towards verification of a service orchestration language. In: ISSRE, pp. 36–37 (2010)Google Scholar
  20. 20.
    Tan, T.H., André, É., Sun, J., Liu, Y., Dong, J.S., Chen, M.: Dynamic synthesis of local time requirement for service composition. In: ICSE, pp. 542–551 (2013)Google Scholar
  21. 21.
    Tan, T.H., Chen, M., André, É., Sun, J., Liu, Y., Dong, J.S.: Automated runtime recovery for QoS-based service composition. In: 23rd International World Wide Web Conference, WWW 2014, Seoul, Republic of Korea, 7–11 April 2014, pp. 563–574 (2014)Google Scholar
  22. 22.
    Tan, T.H., Chen, M., Sun, J., Liu, Y., André, É., Xue, Y., Dong, J.S.: Optimizing selection of competing services with probabilistic hierarchical refinement. In: ICSE, pp. 85–95 (2016)Google Scholar
  23. 23.
    Tan, T.H., Liu, Y., Sun, J., Dong, J.S.: Verification of orchestration systems using compositional partial order reduction. In: Qin, S., Qiu, Z. (eds.) ICFEM 2011. LNCS, vol. 6991, pp. 98–114. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  24. 24.
    Yoon, K., Hwang, C.: Multiple Attribute Decision Making: An Introduction. Sage Publications, Incorporated, Thousand Oaks (1995)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Manman Chen
    • 1
  • Tian Huat Tan
    • 1
  • Jun Sun
    • 1
  • Jingyi Wang
    • 1
  • Yang Liu
    • 2
  • Jing Sun
    • 3
  • Jin Song Dong
    • 4
  1. 1.Singapore University of Technology and DesignSingaporeSingapore
  2. 2.Nanyang Technological UniversitySingaporeSingapore
  3. 3.The University of AucklandAucklandNew Zealand
  4. 4.National University of SingaporeSingaporeSingapore

Personalised recommendations