Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
IEEE Internet Computing
Web Services and Service-Oriented Architecture: The Savvy Manager's Guide
Web Services and Service-Oriented Architecture: The Savvy Manager's Guide
QoS-Aware Middleware for Web Services Composition
IEEE Transactions on Software Engineering
Constraint Driven Web Service Composition in METEOR-S
SCC '04 Proceedings of the 2004 IEEE International Conference on Services Computing
Efficient Access to Web Services
IEEE Internet Computing
An approach for QoS-aware service composition based on genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Optimizing Dynamic Web Service Component Composition by Using Evolutionary Algorithms
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Model-driven methodology for building QoS-optimised web service compositions
DAIS'05 Proceedings of the 5th IFIP WG 6.1 international conference on Distributed Applications and Interoperable Systems
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In service composition, quality of service is a major criterion for selecting services to collaborate in a process flow to satisfy a certain quality goal. This paper presents an approach for service composition which considers QoS-based service provision schemes and variability of the QoS when planning. The QoS of a service can be stated in terms of complex service provision schemes, e.g. its service cost is offered at different rates for different classes of processing time, or its partnership with another service gives a special class of QoS when they operate in the same plan. We also address that it is desirable for service planning to result in a plan that is durable and reusable since change in the plan, e.g. by deviation of the actual QoS, would incur overheads. Our planning approach takes into account these dynamic situations and is demonstrated by using the Estimation of Distribution Algorithm.