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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