DocumentCode
2664499
Title
A Penalty-Based Genetic Algorithm for QoS-Aware Web Service Composition with Inter-service Dependencies and Conflicts
Author
Ai, Lifeng ; Tang, Maolin
Author_Institution
Queensland Univ. of Technol., Brisbane, QLD, Australia
fYear
2008
fDate
10-12 Dec. 2008
Firstpage
738
Lastpage
743
Abstract
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.
Keywords
Web services; genetic algorithms; quality of service; software quality; QoS-aware Web service composition; composite Web service; interservice conflicts; interservice dependencies; penalty-based genetic algorithm; quality of service attributes; value-added Web service; Australia; Availability; Collaboration; Delay; Genetic algorithms; Quality of service; Scalability; Simple object access protocol; Web services; XML; QoS; Web service composition; genetic algorithm;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence for Modelling Control & Automation, 2008 International Conference on
Conference_Location
Vienna
Print_ISBN
978-0-7695-3514-2
Type
conf
DOI
10.1109/CIMCA.2008.104
Filename
5172717
Link To Document