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
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Blay-Fornarino, M., Charfi, A., Emsellem, D., Pinna-Dery, A.-M., Riveill, M.: Software interactions. Journal of Object Technology 3(10), 161–180 (2004)
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)
Dey, A.K.: Understanding and Using Context. Personal and Ubiquitous Computing Journal 5(1), 4–7 (2001)
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)
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)
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)
Keeney, J.: Completely Unanticipated Dynamic Adaptation of Software. PhD Thesis, University of Dublin, Trinity College, Distributed Systems Group (October 2004)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cremene, M., Riveill, M. (2007). Service-Context Knowledge-Based Solution for Autonomic Adaptation. In: Xiao, B., Yang, L.T., Ma, J., Muller-Schloer, C., Hua, Y. (eds) Autonomic and Trusted Computing. ATC 2007. Lecture Notes in Computer Science, vol 4610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73547-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-73547-2_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73546-5
Online ISBN: 978-3-540-73547-2
eBook Packages: Computer ScienceComputer Science (R0)