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Self-organising agent communities for autonomic resource management

Self-organising agent communities for autonomic resource management
Self-organising agent communities for autonomic resource management
The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the run-time reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions.

In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximised when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organises into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimise any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes.
autonomic computing, networks, self-organisation, community structure, decentralised control, emergence
3-28
Jacyno, Mariusz
9a75bc99-5536-47b4-9342-741e0bffcef9
Bullock, Seth
2ad576e4-56b8-4f31-84e0-51bd0b7a1cd3
Geard, Nicholas
e9933f78-10b8-4454-8c8d-c2c75e040346
Payne, Terry R.
0bb13d45-2735-45a3-b72c-472fddbd0bb4
Luck, Michael
94f6044f-6353-4730-842a-0334318e6123
Jacyno, Mariusz
9a75bc99-5536-47b4-9342-741e0bffcef9
Bullock, Seth
2ad576e4-56b8-4f31-84e0-51bd0b7a1cd3
Geard, Nicholas
e9933f78-10b8-4454-8c8d-c2c75e040346
Payne, Terry R.
0bb13d45-2735-45a3-b72c-472fddbd0bb4
Luck, Michael
94f6044f-6353-4730-842a-0334318e6123

Jacyno, Mariusz, Bullock, Seth, Geard, Nicholas, Payne, Terry R. and Luck, Michael (2013) Self-organising agent communities for autonomic resource management. Adaptive Behavior, 21 (1), 3-28. (doi:10.1177/1059712312462247).

Record type: Article

Abstract

The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the run-time reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions.

In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximised when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organises into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimise any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes.

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More information

e-pub ahead of print date: 6 November 2012
Published date: February 2013
Keywords: autonomic computing, networks, self-organisation, community structure, decentralised control, emergence
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 342217
URI: http://eprints.soton.ac.uk/id/eprint/342217
PURE UUID: 6c708772-f667-4798-b0b4-983ad1b5dd6b

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Date deposited: 16 Aug 2012 08:58
Last modified: 14 Mar 2024 11:48

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Contributors

Author: Mariusz Jacyno
Author: Seth Bullock
Author: Nicholas Geard
Author: Terry R. Payne
Author: Michael Luck

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