Skip navigation

Natural inspiration for self-adaptive systems

Natural inspiration for self-adaptive systems

Anthony, Richard John (2004) Natural inspiration for self-adaptive systems. In: Proceedings: 15th International Workshop on Database and Expert Systems Applications. IEEE Computer Society, Los Alamitos, California, USA, pp. 732-736. ISBN 0769521959 ISSN 1529-4188 (doi:10.1109/DEXA.2004.1333561)

Full text not available from this repository.

Abstract

The emergent behaviour of autonomic systems, together with the scale of their deployment, impedes prediction of the full range of configuration and failure scenarios; thus it is not possible to devise management and recovery strategies to cover all possible outcomes. One solution to this problem is to embed self-managing and self-healing abilities into such applications.

Traditional design approaches favour determinism, even when unnecessary. This can lead to conflicts between the non-functional requirements.

Natural systems such as ant colonies have evolved cooperative, finely tuned emergent behaviours which allow the colonies to function at very large scale and to be very robust, although non-deterministic. Simple pheromone-exchange communication systems are highly efficient and are a major contribution to their success.

This paper proposes that we look to natural systems for inspiration when designing architecture and communications strategies, and presents an election algorithm which encapsulates non-deterministic behaviour to achieve high scalability, robustness and stability.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings: 15th International Workshop on Database and Expert Systems Applications
Additional Information: [1] This paper was first presented at the 15th International Conference on Database and Expert Systems Applications (DEXA 2004), (DEXA’04), held from 30 August – 3 September 2004 in Zaragoza, Spain.
Uncontrolled Keywords: emergence, distributed systems, self-healing, self- adaptation, election algorithms
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Computer & Computational Science Research Group
School of Computing & Mathematical Sciences > Department of Computer Systems Technology
Related URLs:
Last Modified: 14 Oct 2016 09:02
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/806

Actions (login required)

View Item View Item