Towards a Management Paradigm with a Constrained Benchmark for Autonomic Communications

Chiang, F; Braun, RM

Towards a Management Paradigm with a Constrained Benchmark for Autonomic Communications

Chiang, F Braun, RM

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Computational Intelligence and Security: International Conference - CIS 06, 2006, pp. 520 - 523
Issue Date:: 2006-01

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Field	Value	Language
dc.contributor.author	Chiang, F	en_US
dc.contributor.author	Braun, RM https://orcid.org/0000-0002-4153-8769	en_US
dc.contributor.editor	Cheung, YM	en_US
dc.contributor.editor	Wang, Y	en_US
dc.contributor.editor	Liu, H	en_US
dc.date	2006-11-03	en_US
dc.date.issued	2006-01	en_US
dc.identifier.citation	Computational Intelligence and Security: International Conference - CIS 06, 2006, pp. 520 - 523	en_US
dc.identifier.isbn	1-4244-0604-8	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12689
dc.description.abstract	This paper describes a management paradigm to give effect to autonomic activation, monitoring and control of services or products in the future converged telecommunications networks. It suggests an architecture that places the various management functions into a structure that can then be used to select those functions which may yield to autonomic management, as well as guiding the design of the algorithms. The validation of this architecture, with particular focus on service configuration, is done via a genetic algorithm -- Population Based Incremental Learning (PBIL). Even with this centralized adaptation strategy, the simulation results show that the proposed architecture and benchmark can be applied to this constrained benchmark, produces effective convergence performance in terms of finding nearly optimal configurations under multiple constraints.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Computational Intelligence and Security: International Conference - CIS 06	en_US
dc.relation.ispartof	International Conference on Computational Intelligence and Security	en_US
dc.relation.isbasedon	10.1007/978-3-540-74377-4_27	en_US
dc.rights	© 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Towards a Management Paradigm with a Constrained Benchmark for Autonomic Communications	en_US
dc.type	Conference Proceeding
utslib.location	Guangzhou, China	en_US
utslib.location.activity	Guangzhou, China	en_US
utslib.for	0803 Computer Software	en_US
dc.location.activity	Guangzhou, China	en_US
dc.location.activity	Auckland, New Zealand
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Guangzhou, China	en_US
pubs.start-date	2006-11-03	en_US

Abstract:

This paper describes a management paradigm to give effect to autonomic activation, monitoring and control of services or products in the future converged telecommunications networks. It suggests an architecture that places the various management functions into a structure that can then be used to select those functions which may yield to autonomic management, as well as guiding the design of the algorithms. The validation of this architecture, with particular focus on service configuration, is done via a genetic algorithm -- Population Based Incremental Learning (PBIL). Even with this centralized adaptation strategy, the simulation results show that the proposed architecture and benchmark can be applied to this constrained benchmark, produces effective convergence performance in terms of finding nearly optimal configurations under multiple constraints.

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http://hdl.handle.net/10453/12689