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• W2045978781 abstract "The difficulties in dealing with increasingly complex information systems that operate in dynamic operational environments ask for self-management policies able to deal intelligently and autonomously with problems and tasks. Biology has been a key source of inspiration in the definition of self-management approaches in the area of computing systems. In this paper we show how some biologically inspired self-organization algorithms have been incorporated into a framework that supports development of autonomic components called SelfLets. The features of a SelfLet include the ability to dynamically change and adapt its internal behaviour according to modifications in the environment, to interact with other SelfLets, in order to provide high-level services, and to make use of autonomic reasoning in order to enable self-* capabilities. In this context, self-organization features represent one of the SelfLets autonomic abilities, and allow them to create groups of SelfLets individuals able to cooperate between each other. The work is complemented with a performance study whose goal is to give insights about strengths and weaknesses of these algorithms." @default.
• W2045978781 created "2016-06-24" @default.
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• W2045978781 date "2007-10-28" @default.
• W2045978781 modified "2023-09-26" @default.
• W2045978781 title "Self-organization algorithms for autonomic systems in the SelfLet approach" @default.
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• W2045978781 doi "https://doi.org/10.5555/1365562.1365597" @default.
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