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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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Contributor : Russel Nzekwa Connect in order to contact the contributor
Submitted on : Wednesday, November 25, 2009 - 4:59:04 PM
Last modification on : Friday, February 4, 2022 - 3:16:45 AM
Long-term archiving on: : Tuesday, October 16, 2012 - 2:51:05 PM


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  • HAL Id : inria-00436013, version 1



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. ⟨inria-00436013⟩



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