1st International ICST Conference on Autonomic Computing and Communication Systems

Research Article

Self-Organization Algorithms for Autonomic Systems in the SelfLet Approach

Download452 downloads
  • @INPROCEEDINGS{10.4108/ICST.AUTONOMICS2007.2176,
        author={Davide Devescovi and Elisabetta Di Nitto and Daniel Dubois and Raffaela Mirandola},
        title={Self-Organization Algorithms for Autonomic Systems in the SelfLet Approach},
        proceedings={1st International ICST Conference on Autonomic Computing and Communication Systems},
        publisher={ICST},
        proceedings_a={AUTONOMICS},
        year={2007},
        month={10},
        keywords={Autonomic Computing distributed and adaptable systems clustering algorithms performance analysis},
        doi={10.4108/ICST.AUTONOMICS2007.2176}
    }
    
  • Davide Devescovi
    Elisabetta Di Nitto
    Daniel Dubois
    Raffaela Mirandola
    Year: 2007
    Self-Organization Algorithms for Autonomic Systems in the SelfLet Approach
    AUTONOMICS
    ICST
    DOI: 10.4108/ICST.AUTONOMICS2007.2176
Davide Devescovi1,*, Elisabetta Di Nitto1,*, Daniel Dubois1,*, Raffaela Mirandola1,*
  • 1: Dipartimento di Elettronica e Informazione Politecnico di Milano via Ponzio 34/5, 20133 Milano, ITALY
*Contact email: devescovi@elet.polimi.it, dinitto@elet.polimi.it, dubois@elet.polimi.it, mirandola@elet.polimi.it

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.