QoS-Driven Web Services Selection in Autonomic Grid Environments

  • Danilo Ardagna
  • Gabriele Giunta
  • Nunzio Ingraffia
  • Raffaela Mirandola
  • Barbara Pernici
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4276)


In the Service Oriented Architecture (SOA) complex applications can be described as business processes from independently developed services that can be selected at run time on the basis of the provided Quality of Service (QoS). However, QoS requirements are difficult to satisfy especially for the high variability of Internet application workloads. Autonomic grid architectures, which provide basic mechanisms to dynamically re-configure service center infrastructures, can be be exploited to fullfill varying QoS requirements. We tackle the problem of selection of Web services that assure the optimum mapping between each abstract Web service of a business process and a Web service which implements the abstract description, such that the overall quality of service perceived by the user is maximized. The proposed solution guarantees the fulfillment of global constraints, considers variable quality of service profile of component Web services and the long term process execution. The soundness of the proposed solution is shown trough the results obtained on an industrial application example. Furthermore, preliminary computational experiments show that the identified solution has a gap of few percentage units to the global optimum of the problem.


Service Level Agreement Global Constraint Composite Service Execution Path Constraint Family 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    The egee Project (Enabling Grid for E-Science), http://public.eu-egee.org/test/
  2. 2.
    Almeida, J., Almeida, V., Ardagna, D., Francalanci, C., Trubian, M.: Resource management in the autonomic service-oriented architecture. In: ICAC 2006 Proc. (in press, 2006)Google Scholar
  3. 3.
    Ardagna, D., Cappiello, C., Plebani, P., Pernici, B.: A Framework for Describing and Supporting Adaptive Context-aware Web Services. Politecnico di Milano Technical Report 2006.48 (June 2006), http://www.elet.polimi.it/upload/ardagna/Tech2006-48.pdf
  4. 4.
    Ardagna, D., Lucchini, S., Mirandola, R., Pernici, B.: Web services composition in autonomic grid environments. In: Grid and P2P Worflow Workshop Proc. (in press, 2006)Google Scholar
  5. 5.
    Ardagna, D., Pernici, B.: Global and local qoS guarantee in web service selection. In: Bussler, C.J., Haller, A. (eds.) BPM 2005. LNCS, vol. 3812, pp. 32–46. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Berlich, D., Kunze, M., Schwarz, K.: Grid computing in Europe: from research to deployment. In: CRPIT ’44: Proc. of the 2005 Australian workshop on Grid computing and e-research, Darlinghurst, Australia, pp. 21–27. Australian Computer Society, Inc. (2005)Google Scholar
  7. 7.
    Bianchini, D., Antonellis, V.D., Pernici, B., Plebani, P.: Ontology-based methodology for e-Service discovery. Information Systems 31, 361–380 (2006)CrossRefGoogle Scholar
  8. 8.
    Bonatti, P.A., Festa, P.: On optimal service selection. In: WWW 2005 Proc., Chiba, pp. 530–538 (2005)Google Scholar
  9. 9.
    Canfora, G., Penta, M., Esposito, R., Villani, M.L.: QoS-Aware Replanning of Composite Web Services. In: ICWS 2005 Proc. (2005)Google Scholar
  10. 10.
    Cao, J., Jarvis, S.A., Saini, S., Nudd, G.R.: GridFlow: Workflow Management for Grid Computing. In: CCGRID 2003 Proc. (July 2003)Google Scholar
  11. 11.
    Chandrasekaran, S., Miller, J.A., Silver, G., Arpinar, I.B., Sheth, A.P.: Performance Analysis and Simulation of Composite Web Services. Electronic Market: The Intl. Journal of Electronic Commerce and Business Media 13(2), 120–132 (2003)Google Scholar
  12. 12.
    Chase, J.S., Anderson, D.C., Thakar, P.N., Vahdat, A.M., Doyle, R.P.: Managing energy and server resources in hosting centers. In: SOSP 2001 Proc., Banff, pp. 103–116 (2001)Google Scholar
  13. 13.
    Chunlin, L., Layuan, L.: A distributed utility-based two level market solution for optimal resource scheduling in computational grid. Parallel Comput. 31(3+4), 332–351 (2005)CrossRefGoogle Scholar
  14. 14.
    Foster, I., Kesselman, C., Tuecke, S.: The Anatomy of the Grid: Enabling Scalable Virtual Organizations. Intl. J. of Supercomputer Applications (2001)Google Scholar
  15. 15.
    Kephart, J.O., Chess, D.M.: The vision of autonomic computing. IEEE Computer 1(31), 41–50 (2003)Google Scholar
  16. 16.
    Liu, Z., Squillante, M., Wolf, J.L.: On Maximizing Service-Level-Agreement Profits. In: Proc. of ACM Eletronic Commerce Conference (October 2001)Google Scholar
  17. 17.
    Maximilien, E.M., Singh, M.P.: A Framework and Ontology for Dynamic Web Services Selection. IC 8(5), 84–93 (2004)Google Scholar
  18. 18.
    Menascé, D., Almeida, V., Dowdy, L.: Performance by Design. Prentice Hall, Englewood Cliffs (2003)Google Scholar
  19. 19.
    Menasce, D., Casalicchio, E.: QoS in Grid Computing. IEEE Internet Computing (July–August 2004)Google Scholar
  20. 20.
    Ouzzani, M., Bouguettaya, A.: Efficient Access to Web Services. IEEE Internet Comp. 37(3), 34–44 (2004)Google Scholar
  21. 21.
    Patel, C., Supekar, K., Lee, Y.: A QoS Oriented Framework for Adaptive Management of Web Service Based Workflows. In: Mařík, V., Štěpánková, O., Retschitzegger, W. (eds.) DEXA 2003. LNCS, vol. 2736, pp. 826–835. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  22. 22.
    Perros, H.G., Elsayed, K.H.: Call Admission Control Schemes: A Review. IEEE Magazine on Communications (1996)Google Scholar
  23. 23.
    Yu, J., Buyya, R.: A taxonomy of scientific workflow systems for grid computing. SIGMOD Rec. 34(3), 44–49 (2005)CrossRefGoogle Scholar
  24. 24.
    Yu, T., Lin, K.J.: A Broker-Based Framework for QoS-Aware Web Service Composition. In: Proc. of 2005 IEEE Int’l. Conf. on e-Technology, e-Commerce and e-Service (March 2005)Google Scholar
  25. 25.
    Zeng, L., Benatallah, B., Dumas, M., Kalagnamam, J., Chang, H.: QoS-Aware Middleware for Web Services Composition. IEEE Trans. on Soft. Eng. (May 2004)Google Scholar
  26. 26.
    Zhang, L., Ardagna, D.: SLA based profit optimization in autonomic computing systems. In: ICSOC 2004 Proc., New York, pp. 173–182 (2004)Google Scholar
  27. 27.
    Zhang, L., Ardagna, D.: SLA Based Profit Optimization in Autonomic Computing Systems. In: ICSOC 2004 Proc., pp. 173–182 (2004)Google Scholar
  28. 28.
    Zhang, L.J., Bing, L.: Requirements driven dynamic services composition for web services and grid solutions. Journal of Grid Computing 2(2), 121–140 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Danilo Ardagna
    • 1
  • Gabriele Giunta
    • 2
  • Nunzio Ingraffia
    • 2
  • Raffaela Mirandola
    • 1
  • Barbara Pernici
    • 1
  1. 1.Dipartimento Elettronica e InformazionePolitecnico di MilanoItaly
  2. 2.Engineering Ingegneria Informatica S.p.A, R&D LabItaly

Personalised recommendations