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DAACS : a Decision Approach for Autonomic Computing Systems

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Abstract

Systems running in ubiquitous environments are characterized by a context that changes frequently. The adaptation of this kind of systems according to the context is a complex task. Autonomic computing has received a great attention as a solution for this increasing complexity, through an architecture based on the MAPE-K loop. Decisions within the phases of the MAPE-K loop have an important impact on the success of systems adaptation. In the literature, many research activities propose decision approaches and frameworks for autonomic applications adaptation. Nevertheless, there is a lack of guidelines for the adaptation decisions design task. In this work, we propose a Decision Approach for Autonomic Computing Systems, called DAACS, which includes recommendations and steps that should be followed by the autonomic applications designers. DAACS was implemented in a Smart Building case study, and it was evaluated in term of the processing time dedicated for the adaptation decisions.

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Notes

  1. https://github.com/imenGithub/DecisionModule.git

  2. https://github.com/imenGithub/SmartBuilding.git

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  1. ReDCAD, University of Sfax, B.P. 1173, 3038, Sfax, Tunisia

    Imen Abdennadher

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  1. Imen Abdennadher
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Abdennadher, I. DAACS : a Decision Approach for Autonomic Computing Systems. J Supercomput 78, 3883–3904 (2022). https://doi.org/10.1007/s11227-021-04011-z

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