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Abstract

Adaptation in Web services has gained a significant attention and becomes a key feature of Web services. Indeed, in a dynamic environment such as the Web, it’s imperative to design an effective system which can continuously adapt itself to the changes (service failure, changing of QoS offering, etc.). However, current Web service standards and technologies don’t provide a suitable architecture in which all aspects of self-adaptability can be designed. Moreover, Web Services lack ability to adapt to the changing environment without human intervention. In this paper, we propose an autonomic computing approach for Web services’ self-adaptation. More precisely, Web services are considered as autonomic systems, that is, systems that have self-* properties. An agent-based approach is also proposed to deal with the achievement of Web services self-adaptation.

Keywords

Web service autonomic computing systems self*-properties 

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References

  1. 1.
    Ganek, A.G., Corbi, T.A.: The Dawning of the Autonomic Computing Era. J. IBM Systems 42(1), 5–18 (2003)CrossRefGoogle Scholar
  2. 2.
    IBM Group.: An Architectural Blueprint for Autonomic Computing, http://www-03.ibm.com/autonomic/pdfs/AC
  3. 3.
    Chainbi, W.: Agent Technology for Autonomic Computing. J. Transactions on Systems Science and Applications 1(3), 238–249 (2006)Google Scholar
  4. 4.
    Tianfield, H., Unland, R.: Towards Autonomic Computing Systems. J. Engineering Applications of Artificial Intelligence 17(7), 689–699 (2004)CrossRefGoogle Scholar
  5. 5.
    Console, L., Fugini, M.: The WS-Diamond Team: WS-DIAMOND: an Approach to Web Services - DIAgnosability, MONitoring and Diagnosis. In: e-Challenges Conference, The Hague (2007)Google Scholar
  6. 6.
    Baresi, L., Guinea, S., Pasquale, L.: Self-healing BPEL Processes with Dynamo and the JBoss Rule Engine. In: International Workshop on Engineering of Software Services for Pervasive Environments (ESSPE 2007), pp. 11–20 (2007)Google Scholar
  7. 7.
    Pernici, B., Rosati, A.M.: Automatic Learning of Repair Strategies for Web Services. In: 5th European Conference on Web Services (ECOWS 2007), pp. 119–128 (2007)Google Scholar
  8. 8.
    Brogi, A., Popescu, R.: Automated Generation of BPEL Adapters. In: International Conference on Service Oriented Computing (2006)Google Scholar
  9. 9.
    Ardagna, D., Comuzzi, M., Mussi, E., Pernici, B., Plebani, P.: PAWS: A Framework for Executing Adaptive Web-Service Processes. J. IEEE Software 24(6), 39–46 (2007)CrossRefGoogle Scholar
  10. 10.
    Chafle, G., Dasgupta, K., Kumar, A., Mittal, S., Srivastava, B.: Adaptation in Web Service Composition and Execution. In: International Conference on Web Services, pp. 549–557 (2006)Google Scholar
  11. 11.
    Narendra, N.C., Ponnalagu, K., Krishnamurthy, J., Ramkumar, R.: Run-Time Adaptation of Non-functional Properties of Composite Web Services Using Aspect-Oriented Programming. In: Krämer, B.J., Lin, K.-J., Narasimhan, P. (eds.) ICSOC 2007. LNCS, vol. 4749, pp. 546–557. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Jennings, N.: On Agent-based Software Engineering. J. Artificial Intelligence 117(2), 277–296 (2000)CrossRefzbMATHGoogle Scholar
  13. 13.
    Albus, J.S., Meystel, A.M.: Engineering of Mind: an Introduction to the Science of Intelligent Systems. Wiley, New York (2001)Google Scholar
  14. 14.
    Tianfield, H.: Formalized Analysis of Structural Characteristics of Large Complex Systems. J. IEEE Transactions on Systems, Man and Cybernetics. Part A: Systems and Humans 31(6), 59–572 (2001)Google Scholar
  15. 15.
    Booch, G.: Object-Oriented Analysis and Design with Applications. Addison-Wesley, Reading (1994)zbMATHGoogle Scholar
  16. 16.
    Baejis, C., Demazeau, Y.: Organizations in Multi-Agent Systems. Journées DAI, Toulouse (1996)Google Scholar
  17. 17.
    Fox, M.S.: An Organizational View of Distributed Systems. J. IEEE Transactions on Systems, Man and Cybernetics 11(1), 70–80 (1981)CrossRefGoogle Scholar
  18. 18.
    Sandholm, T.: Distributed Rational Decision Making. Multi-Agent Systems. MIT Press, Cambridge (1985)Google Scholar
  19. 19.
    Tambe, M.: Toward Flexible Teamwork. J. Artificial Intelligence Research 7, 83–124 (1997)Google Scholar
  20. 20.
    Luck, M., McBurney, P., Preist, C.: Agent Technology: Enabling Next Generation Computing. In: AgentLink II (2003)Google Scholar
  21. 21.
    D’inverno, M., Luck, M.: Understanding Agent Systems, 2nd edn. Springer, Heidelberg (2004)CrossRefzbMATHGoogle Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2010

Authors and Affiliations

  • Walid Chainbi
    • 1
  • Haithem Mezni
    • 2
  • Khaled Ghedira
    • 3
  1. 1.Sousse National School of Engineers/LI3 SousseTunisia
  2. 2.Jendouba University Campus/LI3 JendoubaTunisia
  3. 3.Institut Supérieur de Gestion de Tunis/LI3 TunisTunisia

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