Bioinspired Models of Network, Information, and Computing Systems. 4th International Conference, BIONETICS 2009, Avignon, France, December 9-11, 2009, Revised Selected Papers

Research Article

Embryonic Models for Self–healing Distributed Services

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  • @INPROCEEDINGS{10.1007/978-3-642-12808-0_15,
        author={Daniele Miorandi and David Lowe and Lidia Yamamoto},
        title={Embryonic Models for Self--healing Distributed Services},
        proceedings={Bioinspired Models of Network, Information, and Computing Systems. 4th International Conference, BIONETICS 2009, Avignon, France, December 9-11, 2009, Revised Selected Papers},
        proceedings_a={BIONETICS},
        year={2012},
        month={5},
        keywords={distributed services autonomic computing self--healing behaviour robustness embryogenesis differentiation mechanisms},
        doi={10.1007/978-3-642-12808-0_15}
    }
    
  • Daniele Miorandi
    David Lowe
    Lidia Yamamoto
    Year: 2012
    Embryonic Models for Self–healing Distributed Services
    BIONETICS
    Springer
    DOI: 10.1007/978-3-642-12808-0_15
Daniele Miorandi1,*, David Lowe,*, Lidia Yamamoto2,*
  • 1: CREATE-NET
  • 2: Computer Science Department
*Contact email: daniele.miorandi@create-net.org, david.lowe@uts.edu.au, Lidia.Yamamoto@unibas.ch

Abstract

A major research challenge in distributed systems is the design of services that incorporate robustness to events such as network changes and node faults. In this paper we describe an approach – which we refer to as – that is inspired by cellular development and differentiation processes. The approach uses “artificial stem cells” in the form of totipotent nodes that differentiate into the different types needed to obtain the desired system–level behaviour. Each node has a genome that contains the full service specification, as well as rules for the differentiation process. We describe the system architecture and present simulation results that assess the overall performance and fault tolerance properties of the system in a decentralized network monitoring scenario.