Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.745379
Title: Topologically constrained self-organisation
Author: Shaw, Matthew James
ISNI:       0000 0004 7223 9117
Awarding Body: King's College London
Current Institution: King's College London (University of London)
Date of Award: 2014
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Abstract:
Autonomic computing suggests the need for dynamic systems that adapt their struc-ture in response to environmental changes in a bottom-up fashion. Such systems can be considered to be composed of agents that self-organise in support of more effective operation. While various self-organisation approaches that aim to meet this need for continuous adaptation have been developed, these typically operate on structures that are not constrained to particular patterns (pipelines, hierarchies). Yet the ubiquitous use of such patterns when structuring task workflows, communication protocols, and traditional organisational design, suggests a need for their preservation when reorganis-ing. In cases where specific patterns, or topologies, result from self-organisation, these are artefacts of the self-organisation mechanism, rather than the underlying topology itself being preserved. In contrast, this thesis explicitly tackles such adaptation, while accommodating the need to preserve topology. The thesis introduces techniques for adapting a system’s structure to improve task throughput, and builds on these techniques, to provide a means of preserving particular topologies. A framework for the reorganisation of defined topologies is introduced, and specific solutions are given for the case of pipelines and hierarchies, which reorganise to improve performance based on application-specific metrics, while preserving topology. Importantly, efficacy is only slightly diminished when topology is maintained, but at the cost of diminished autonomy.
Supervisor: Luck, Michael Mordechai ; Keppens, Jeroen ; Miles, Simon Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.745379  DOI: Not available
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