The University of Southampton
University of Southampton Institutional Repository

Self-organising agent communities for autonomic computing

Self-organising agent communities for autonomic computing
Self-organising agent communities for autonomic computing
Efficient resource management is one of key problems associated with large-scale distributed computational systems. Taking into account their increasing complexity, inherent distribution and dynamism, such systems are required to adjust and adapt resources market that is offered by them at run-time and with minimal cost. However, as observed by major IT vendors such as IBM, SUN or HP, the very nature of such systems prevents any reliable and efficient control over their functioning through human administration.

For this reason, autonomic system architectures capable of regulating their own functioning are suggested as the alternative solution to looming software complexity crisis. Here, large-scale infrastructures are assumed to comprise myriads of autonomic elements, each acting, learning or evolving separately in response to interactions in their local environments. The self-regulation of the whole system, in turn, becomes a product of local adaptations and interactions between system elements.

Although many researchers suggest the application of multi-agent systems that are suitable for realising this vision, not much is known about regulatory mechanisms that are capable to achieve efficient organisation within a system comprising a population of locally and autonomously interacting agents.

To address this problem, the aim of the work presented in this thesis was to understand how global system control can emerge out of such local interactions of individual system elements and to develop decentralised decision control mechanisms that are capable to employ this bottom-up self-organisation in order to preserve efficient resource management in dynamic and unpredictable system functioning conditions. To do so, we have identified the study of complex natural systems and their self-organising properties as an area of research that may deliver novel control solutions within the context of autonomic computing.

In such a setting, a central challenge for the construction of distributed computational systems was to develop an engineering methodology that can exploit self-organising principles observed in natural systems. This, in particular, required to identify conditions and local mechanisms that give rise to useful self-organisation of interacting elements into structures that support required system functionality. To achieve this, we proposed an autonomic system model exploiting self-organising algorithms and its thermodynamic interpretation, providing a general understanding of self-organising processes that need to be taken into account within artificial systems exploiting self-organisation.
Jacyno, Mariusz
89365b6a-db94-4b05-9917-3a3f06032ca5
Jacyno, Mariusz
89365b6a-db94-4b05-9917-3a3f06032ca5
Bullock, Seth
2ad576e4-56b8-4f31-84e0-51bd0b7a1cd3
Payne, Terence
876f347c-2b24-43cd-867f-29ba21286a42
Luck, Michael
94f6044f-6353-4730-842a-0334318e6123

Jacyno, Mariusz (2010) Self-organising agent communities for autonomic computing. University of Southampton, School of Electronics and Computer Science, Doctoral Thesis, 257pp.

Record type: Thesis (Doctoral)

Abstract

Efficient resource management is one of key problems associated with large-scale distributed computational systems. Taking into account their increasing complexity, inherent distribution and dynamism, such systems are required to adjust and adapt resources market that is offered by them at run-time and with minimal cost. However, as observed by major IT vendors such as IBM, SUN or HP, the very nature of such systems prevents any reliable and efficient control over their functioning through human administration.

For this reason, autonomic system architectures capable of regulating their own functioning are suggested as the alternative solution to looming software complexity crisis. Here, large-scale infrastructures are assumed to comprise myriads of autonomic elements, each acting, learning or evolving separately in response to interactions in their local environments. The self-regulation of the whole system, in turn, becomes a product of local adaptations and interactions between system elements.

Although many researchers suggest the application of multi-agent systems that are suitable for realising this vision, not much is known about regulatory mechanisms that are capable to achieve efficient organisation within a system comprising a population of locally and autonomously interacting agents.

To address this problem, the aim of the work presented in this thesis was to understand how global system control can emerge out of such local interactions of individual system elements and to develop decentralised decision control mechanisms that are capable to employ this bottom-up self-organisation in order to preserve efficient resource management in dynamic and unpredictable system functioning conditions. To do so, we have identified the study of complex natural systems and their self-organising properties as an area of research that may deliver novel control solutions within the context of autonomic computing.

In such a setting, a central challenge for the construction of distributed computational systems was to develop an engineering methodology that can exploit self-organising principles observed in natural systems. This, in particular, required to identify conditions and local mechanisms that give rise to useful self-organisation of interacting elements into structures that support required system functionality. To achieve this, we proposed an autonomic system model exploiting self-organising algorithms and its thermodynamic interpretation, providing a general understanding of self-organising processes that need to be taken into account within artificial systems exploiting self-organisation.

Text
Mariusz_Jacyno.pdf - Other
Download (2MB)

More information

Published date: February 2010
Organisations: University of Southampton

Identifiers

Local EPrints ID: 143903
URI: http://eprints.soton.ac.uk/id/eprint/143903
PURE UUID: 2528cd2f-a700-456e-926e-db7edd92e701

Catalogue record

Date deposited: 16 Jun 2010 11:24
Last modified: 29 Jan 2020 13:46

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×