ZebraX: A Model for Service Composition with Multiple QoS Constraints

  • Xingzhi Feng
  • Quanyuan Wu
  • Huaimin Wang
  • Yi Ren
  • Changguo Guo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4459)


With the development of theory and technology of Web Service, Web Service Composition (WSC) has become the core Service-Oriented Computing technology. It is important for business process to select the best component services with multi-dimensional QoS assurances to construct a complex one. But there exist some problems, such as evaluation for QoS properties of a service is not full-scale and the criteria is not clear, the weight for each QoS metric doesn’t consider both subjective sensations and objective facts. In this paper we propose a WSC model to provide multi-dimensional QoS supports in service selection and replacement. We consider SLA and recovery mechanism for the service failure during its execution. A utility function is defined as the evaluation standard, which aggregates all QoS metrics after normalizing their values. Then we use Subjective-Objective Weight Mode (SOWM) to set the weight of each QoS metric. Finally we introduce our prototype and evaluations, test the availability of the decision mode and the results prove it is predominant compared with other decision modes.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Curbera, F., et al.: Business Process Execution Language for Web Services,Version 1.1 (May 2003), http://www-106.ibm.com/developerworks/webservices/library/ws-bpel
  2. 2.
    BPMI.org.: Business Process Modeling Language (BPML), Version 1.0 (November 2002), http://www.bpmi.org/bpml.esp
  3. 3.
    Ponnekanti, S.R., Fox, A.: Sword: A developer toolkit for Web service composition. In: 11th World Wide Web Conference (Engineering Track), Honolulu, Hawaii (May 2002)Google Scholar
  4. 4.
    Casati, F., et al.: Adaptive and Dynamic Service Composition in eFlow. In: Wangler, B., Bergman, L.D. (eds.) CAiSE 2000. LNCS, vol. 1789, pp. 13–31. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  5. 5.
    Zeng, L., et al.: Quality Driven Web Service Composition. In: Proc. of 12th International World Wide Web Conference (WWW03), Budapest, Hungary (May 2003)Google Scholar
  6. 6.
    Zeng, L., et al.: QoS-Aware Middleware for Web Services Composition. IEEE Transactions on Software Engineering 30(5), 311–327 (2004)CrossRefGoogle Scholar
  7. 7.
    Aggarwal, R., et al.: Constraint driven Web service composition in METEOR-S. In: Proc. of IEEE International Conference on Service Computing (SCC’04), Shanghai, China, Sep. 2004, pp. 23–30 (2004)Google Scholar
  8. 8.
    Patil, A., et al.: METEOR-S Web service Annotation Framework. In: Proc. of 13th international World Wide Web conference(WWW04), New York, USA, May 2004, pp. 533–562 (2004)Google Scholar
  9. 9.
    Dan, A., et al.: Web services on demand: WSLA-driven automated management. IBM Systems Journal 43(1), 136–158 (2004)CrossRefGoogle Scholar
  10. 10.
    Lin, K.-J., Yu, T.: Service Selection Algorithms for Composing Complex Services with Multiple QoS Constraints. In: Benatallah, B., Casati, F., Traverso, P. (eds.) ICSOC 2005. LNCS, vol. 3826, pp. 130–143. Springer, Heidelberg (2005)Google Scholar
  11. 11.
    Berbner, R., et al.: Heuristics for QoS-aware Web Service Composition. In: Proc. of IEEE International Conference on Web Services (ICWS’06), pp. 72–82 (2006)Google Scholar
  12. 12.
    Benatallah, B., Sheng, Q.Z., Dumas, M.: The Self-Serv Environment for Web Services Composition. IEEE Internet Computing, 40–48 (Jan.-Feb. 2003)Google Scholar
  13. 13.
  14. 14.
    Khan, S., Li, K.F., Manning, E.G., et al.: Solving the knapsack problem for adaptive multimedia systems. Studia Informatica Universalis 2(1), 157–178 (2002)Google Scholar
  15. 15.
    Yue, C.: Theory and Methods. The Science Press, Beijing (March 2003)Google Scholar
  16. 16.
    Hu, J., et al.: Quality Driven Web Services Selection. In: Proc. of IEEE International Conference on e-Business Engineering (ICEBE 2005), Beijing, China, Oct. 2005, pp. 681–688 (2005)Google Scholar
  17. 17.
    Fan, Z.P.: A New Method for Multiple Attribute Decision Making. Journal of System Engineering 12(1), 15–17 (1994)Google Scholar
  18. 18.
    Ma, J., Fan, Z., Huang, L.: A Subjective and Objective Integrated Approach to Determine Attribute Weights. European Journal of Operational Research 112(2), 397–404 (1999)MATHCrossRefGoogle Scholar
  19. 19.
  20. 20.
    Gu, X., Nahrstedt, K.: Dynamic QoS-aware multimedia service configuration in ubiquitous computing environments. In: Proc. of IEEE 22nd International Conference on Distributed Computing Systems, Vienna, Austria, July 2002, pp. 311–318 (2002)Google Scholar
  21. 21.
    Gu, X., Nahrstedt, K.: A scalable QoS-aware service aggregation model for peer-to-peer computing grids. In: Proc. of the 11th IEEE International Symposium on High Performance Distributed Computing (HPDC-11), Edinburgh, Scotland, July 2002, pp. 73–82 (2002)Google Scholar
  22. 22.
    Magoni, D., Pansiot, J.-J.: Internet Topology Modeler Based on Map Sampling. In: Proc. of the Seventh International Symposium on Computers and Communications (ISCC’02), Taormina, July 2002, pp. 1021–1027. IEEE Computer Society, Los Alamitos (2002)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Xingzhi Feng
    • 1
  • Quanyuan Wu
    • 1
  • Huaimin Wang
    • 1
  • Yi Ren
    • 1
  • Changguo Guo
    • 1
  1. 1.School of Computer, National University of Defense Technology, 410073 ChangshaChina

Personalised recommendations