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An approach for QoS-aware service composition based on genetic algorithms
Abstract
Web services are rapidly changing the landscape of software engineering. One of the most interesting challenges introduced by web services is represented by Quality Of Service (QoS)--aware composition and late--binding. This allows to bind, at run--time, a service--oriented system with a set of services that, among those providing the required features, meet some non--functional constraints, and optimize criteria such as the overall cost or response time. In other words, QoS--aware composition can be modeled as an optimization problem.We propose to adopt Genetic Algorithms to this aim. Genetic Algorithms, while being slower than integer programming, represent a more scalable choice, and are more suitable to handle generic QoS attributes. The paper describes our approach and its applicability, advantages and weaknesses, discussing results of some numerical simulations.
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Cited by
Software Architecture Optimization Methods: A Systematic Literature Review
Aleti, A., Buhnova, B., Grunske, L., Koziolek, A., & Meedeniya, I. (2013) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2012.64 · Cited by 293
A Study on Group Decision-Making in Software Architecture
Rekhav, V. S., & Muccini, H. (2014) · 2014 IEEE/IFIP Conference on Software Architecture · doi:10.1109/wicsa.2014.15 · Cited by 31
Metadata
- Authors
- Canfora, G., Di Penta, M., Esposito, R., & Villani, M. L.
- Venue
- Proceedings of the 7th annual conference on Genetic and evolutionary computation
- Publisher
- ACM
- Published
- 2005-06-25
- Pages
- 1069-1075
- OpenAlex
- W2172105112
- Cited by
- 912
- References
- 0
- FWCI
- 30.81
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: An approach for QoS-aware service composition based on genetic algorithms. https://app.cochat.ai/papers/10.1145/1068009.1068189
- The paper
- Canfora, G., Di Penta, M., Esposito, R., & Villani, M. L. (2005). An approach for QoS-aware service composition based on genetic algorithms. Proceedings of the 7th annual conference on Genetic and evolutionary computation. https://doi.org/10.1145/1068009.1068189
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