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An Architectural Model for Building Distributed Adaptation Systems

  • Mohamed Zouari
  • Maria-Teresa Segarra
  • Françoise André
  • André Thépaut
Part of the Studies in Computational Intelligence book series (SCI, volume 382)

Abstract

Dynamic adaptation allows the modification of an application configuration at runtime, according to changes in the environment and/or in users’ requirements. The case of adaptive distributed applications has not been substantially addressed. In particular, the distribution of the adaptation system itself has been rarely considered. We address this issue by proposing an architectural model of distributed adaptation systems. Our model allows dynamic adaptation management in a distributed and coordinated manner and expresses variation points of the system. In this paper, we present our model and its use to build distributed adaptation systems. We have applied our results to build an adaptive distributed data replication system used in a medical environment dedicated to remote health care delivery for patients at home.

Keywords

Adaptation System Architectural Model Component Type Dynamic Adaptation Server Interface 
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.
    Birman, K.P., Constable, R., Hayden, M., Hickey, J., Kreitz, C., Renesse, R.V., Rodeh, O., Vogels, W.: The horus and ensemble projects: Accomplishments and limitations. Technical report, New York (1999)Google Scholar
  2. 2.
    Bruneton, E., Coupaye, T., Leclercq, M., Quéma, V.: The FRACTAL component model and its support in java. Softw, Pract. Exper., 1257–1284 (2006)Google Scholar
  3. 3.
    Chen, W.K., Hiltunen, M.A., Schlichting, R.D.: Constructing adaptive software in distributed systems. In: ICDCS 2001: Proceedings of the 21st Inter. Conf. on Distributed Computing Systems, Washington, pp. 635–643 (2001)Google Scholar
  4. 4.
    Cheng, B.H., Lemos, R., Giese, H.: Software engineering for self-adaptive systems: A research roadmap. In: Cheng, B.H.C., de Lemos, R., Giese, H., Inverardi, P., Magee, J. (eds.) Software Engineering for Self-Adaptive Systems. LNCS, vol. 5525, pp. 1–26. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Dowling, J., Cahill, V.: Self-managed decentralised systems using K-Components and collaborative reinforcement learning. In: WOSS 2004: Proceedings of the SIGSOFT Workshop on Self-Managed Systems, New York, pp. 39–43 (2004)Google Scholar
  6. 6.
    Zouari, M., Segarra, M.T., André, F.: A framework for distributed management of dynamic self-adaptation in heterogeneous environments. In: 10th IEEE Inter. Conf. on Computer and Information Technology, Bradford, pp. 265–272 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mohamed Zouari
    • 1
  • Maria-Teresa Segarra
    • 2
  • Françoise André
    • 1
  • André Thépaut
    • 2
  1. 1.INRIA/IRISARennesFrance
  2. 2.Télécom Bretagne, Technopôle Brest-IroiseBrestFrance

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