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Service-Context Knowledge-Based Solution for Autonomic Adaptation

  • Marcel Cremene
  • Michel Riveill
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4610)

Abstract

In most adaptive systems, the adaptation control is based on developer-made rules and strategies that are specific for each service and context. Our proposal for autonomic computing is to replace this mechanism with a machine-based reasoning. The key element in making this possible is a service-context model that offers a knowledge support for the adaptive platform, which can diagnose the service adequacy to the context and search for solutions. We have tested our model using a prototype that adapts a service by inserting the ’right’ component at the ’right’ place into the service architecture.

Keywords

Service Architecture Autonomic Computing Solution Search Service Adequacy Autonomic Adaptation 
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.

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References

  1. 1.
    Blay-Fornarino, M., Charfi, A., Emsellem, D., Pinna-Dery, A.-M., Riveill, M.: Software interactions. Journal of Object Technology 3(10), 161–180 (2004)Google Scholar
  2. 2.
    Brezillon, P.: Context-based modeling of operators practices by contextual graphs. In: Proceedings of the 14th Mini Euro Conference, Human Centered Processes, pp. 129–137 (May 2003)Google Scholar
  3. 3.
    Dey, A.K.: Understanding and Using Context. Personal and Ubiquitous Computing Journal 5(1), 4–7 (2001)CrossRefGoogle Scholar
  4. 4.
    Dubus, J., Merle, P.: Vers l’auto-adaptabilite des architectures logicielles dans les environnements ouverts distribues. In: Proceedings of the 1ere Conference Francophone sur les Architectures Logicielles, CAL 2006, Nantes, France, Hermes Sciences, pp. 3–29 (September 2006)Google Scholar
  5. 5.
    Floch, J., Hallsteinsen, S., Stav, E., Eliassen, F., Lund, K., Gjorven, E.: Using Architecture Models for Runtime Adaptability. Software, IEEE 23(2), 62–70 (2006)CrossRefGoogle Scholar
  6. 6.
    Garlan, D., Cheng, S.W., Huang, A.-C., Schmerl, B.R., Steenkiste, P.: Rainbow: Architecture-based self-adaptation with reusable infrastructure. Computer, IEEE 37(10), 46–54 (2004)Google Scholar
  7. 7.
    Keeney, J.: Completely Unanticipated Dynamic Adaptation of Software. PhD Thesis, University of Dublin, Trinity College, Distributed Systems Group (October 2004)Google Scholar
  8. 8.
    Kephart, J.O.: Research challenges of autonomic computing. In: Inverardi, P., Jazayeri, M. (eds.) ICSE 2005. LNCS, vol. 4309, pp. 15–21. Springer, Heidelberg (2006)Google Scholar
  9. 9.
    Lemos, R., Fiadeiro, J.L.: An architectural support for self-adaptive software for treating faults. In: Proceedings of the first Workshop on Self-healing systems, pp. 39–42. ACM Press, New York (2002)CrossRefGoogle Scholar
  10. 10.
    Wang, X.H., Gu, T., Zhang, D.Q., Pung, H.K.: Ontology-Based Context Modeling and Reasoning using OWL. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops, pp. 18–22 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Marcel Cremene
    • 1
  • Michel Riveill
    • 2
  1. 1.Technical University of Cluj-Napoca, Cluj-NapocaRomania
  2. 2.University of Nice, Sophia-AntipolisFrance

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