Towards a Stable Decision-Making Middleware for Very-Large-Scale Self-Adaptive Systems.

Abstract : With the development of the communication infrastructures, the number of applications collaborating at large scale increases. To maintain, and continue to deliver services of good quality to the end-users, very-large-scale applications continuously adapt themselves, depending on the changes in their surrounding. Stabilization of the system thus becomes a keystone issue in the adaptation process, in order to reduce the system reconfiguration cost. Existing approaches, for the stabilization of very-large-scale systems, provide solutions that are partially efficient. For example, learning-based stabilization algorithms give good results in predicting application behaviors, but still suffer from their weak reactivity. In this paper, we propose an approach of combining different goals-oriented stabilization algorithms, in order to provide sustainable and efficient stabilization for large-scale systems.
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Communication dans un congrès
BElgian-NEtherlands software eVOLution seminar (BENEVOL), Dec 2009, Louvain-la-Neuve, Belgium. 2009
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Contributeur : Russel Nzekwa <>
Soumis le : mercredi 25 novembre 2009 - 16:59:04
Dernière modification le : jeudi 11 janvier 2018 - 06:22:13
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Russel Nzekwa, Romain Rouvoy, Lionel Seinturier. Towards a Stable Decision-Making Middleware for Very-Large-Scale Self-Adaptive Systems.. BElgian-NEtherlands software eVOLution seminar (BENEVOL), Dec 2009, Louvain-la-Neuve, Belgium. 2009. 〈inria-00436013〉

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