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An adaptive framework for QoS‐aware service selection optimization

An adaptive framework for QoS‐aware service selection optimization Purpose – The optimization of quality‐of‐service (QoS) aware service selection problems is a crucial issue in both grids and distributed service‐oriented systems. When several implementations per service exist, one has to be selected for each workflow step. This paper aims to address these issues. Design/methodology/approach – The authors proposed several heuristics with specific focus on blackboard and genetic algorithms. Their applicability and performance has already been assessed for static systems. In order to cover real‐world scenarios, the approaches are required to deal with dynamics of distributed systems. Findings – The proposed algorithms prove their feasibility in terms of scalability and runtime performance, taking into account their adaptability to system changes. Research limitations/implications – In this paper, the authors propose a representation of the dynamic aspects of distributed systems and enhance their algorithms to efficiently capture them. Practical implications – By combining both algorithms, the authors envision a global approach to QoS‐aware service selection applicable to static and dynamic systems. Originality/value – The authors prove the feasibility of their hybrid approach by deploying the algorithms in a cloud environment (Google App Engine), that allows simulating and evaluating different system configurations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Web Information Systems Emerald Publishing

An adaptive framework for QoS‐aware service selection optimization

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References (18)

Publisher
Emerald Publishing
Copyright
Copyright © 2013 Emerald Group Publishing Limited. All rights reserved.
ISSN
1744-0084
DOI
10.1108/17440081311316370
Publisher site
See Article on Publisher Site

Abstract

Purpose – The optimization of quality‐of‐service (QoS) aware service selection problems is a crucial issue in both grids and distributed service‐oriented systems. When several implementations per service exist, one has to be selected for each workflow step. This paper aims to address these issues. Design/methodology/approach – The authors proposed several heuristics with specific focus on blackboard and genetic algorithms. Their applicability and performance has already been assessed for static systems. In order to cover real‐world scenarios, the approaches are required to deal with dynamics of distributed systems. Findings – The proposed algorithms prove their feasibility in terms of scalability and runtime performance, taking into account their adaptability to system changes. Research limitations/implications – In this paper, the authors propose a representation of the dynamic aspects of distributed systems and enhance their algorithms to efficiently capture them. Practical implications – By combining both algorithms, the authors envision a global approach to QoS‐aware service selection applicable to static and dynamic systems. Originality/value – The authors prove the feasibility of their hybrid approach by deploying the algorithms in a cloud environment (Google App Engine), that allows simulating and evaluating different system configurations.

Journal

International Journal of Web Information SystemsEmerald Publishing

Published: Mar 29, 2013

Keywords: Quality of service; Service selection; Genetic algorithms; Blackboard; Performance levels

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