A Penalty-based Genetic Algorithm for QoS-AwareWeb Service Composition with Inter-Service Dependencies and Conflicts


Autoria(s): Ai, Lifeng; Tang, Maolin
Data(s)

2008

Resumo

In Web service based systems, new value-added Web services can be constructed by integrating existing Web services. A Web service may have many implementations, which are functionally identical, but have different Quality of Service (QoS) attributes, such as response time, price, reputation, reliability, availability and so on. Thus, a significant research problem in Web service composition is how to select an implementation for each of the component Web services so that the overall QoS of the composite Web service is optimal. This is so called QoS-aware Web service composition problem. In some composite Web services there are some dependencies and conflicts between the Web service implementations. However, existing approaches cannot handle the constraints. This paper tackles the QoS-aware Web service composition problem with inter service dependencies and conflicts using a penalty-based genetic algorithm (GA). Experimental results demonstrate the effectiveness and the scalability of the penalty-based GA.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/28778/

Publicador

IEEE Computer Society

Relação

http://eprints.qut.edu.au/28778/1/c28778.pdf

http://doi.ieeecomputersociety.org/10.1109/CIMCA.2008.104

Ai, Lifeng & Tang, Maolin (2008) A Penalty-based Genetic Algorithm for QoS-AwareWeb Service Composition with Inter-Service Dependencies and Conflicts. In 2008 International Conferences on Computational Intelligence for Modelling, Control and Automation; Intelligent Agents, Web Technologies and Internet Commerce; and Innovation in Software Engineering, IEEE Computer Society, Vienna, Austria.

Direitos

Copyright 2008 Please consult the authors

Fonte

Faculty of Science and Technology; Smart Services CRC

Palavras-Chave #080100 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING #Web Service #QoS #Genetic Algorithm
Tipo

Conference Paper