Abstract
Web services are independent functionality modules that can be used as building blocks for applications that accomplish more specific tasks. The large and ever-growing number of Web services means that performing this type of Web service composition manually is unfeasible, which leads to the exploration of automated techniques to achieve this objective. Evolutionary Computation (EC) approaches, in particular, are a popular choice because they are capable of efficiently handling the complex search space involved in this problem. Therefore, we propose the use of a Genetic Programming (GP) technique for Web service composition, building upon previous work that combines the identification of functionally correct solutions with the consideration of the Quality of Service (QoS) properties for each atomic service. The proposed GP technique is compared with two PSO composition techniques using the same QoS-aware objective function, and results show that the solution fitness values and execution times of the GP approach are inferior to those of both PSO approaches, failing to converge for larger datasets. This is because the fitness function employed by the GP technique does not have complete smoothness, thus leading to unreliable behaviour during the evolution process. Multi-objective GP and the use of functional correctness constraints should be considered as alternatives to overcome this in the future.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Al-Masri, E., Mahmoud, Q.H.: Qos-based discovery and ranking of web services. In: 16th Int. Conf. Computer Comm. Networks, pp. 529–534. IEEE (2007)
Alrifai, M., Risse, T.: Combining global optimization with local selection for efficient QoS-aware service composition. In: 18th Int. Conf. World Wide Web, pp. 881–890. ACM (2009)
Amiri, M.A., Serajzadeh, H.: Effective web service composition using particle swarm optimization algorithm. In: 6th Int. Symposium Telecommunications, pp. 1190–1194. IEEE (2012)
Aversano, L., Di Penta, M., Taneja, K.: A genetic programming approach to support the design of service compositions (2006)
Cardoso, J., Sheth, A., Miller, J., Arnold, J., Kochut, K.: Quality of service for workflows and web service processes. Web Semantics 1(3), 281–308 (2004)
Cramer, N.L.: A representation for the adaptive generation of simple sequential programs. In: 1st Int. Conf. Genetic Algorithms, pp. 183–187 (1985)
Jaeger, M.C., Mühl, G.: Qos-based selection of services: The implementation of a genetic algorithm. In: ITG-GI Conf. Comm. Distributed Systems, pp. 1–12 (2007)
Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer (2010)
Ludwig, S.A.: Applying particle swarm optimization to quality-of-service-driven web service composition. In: IEEE 26th Int. Conf. Advanced Information Networking and Applications, pp. 613–620 (2012)
Menascé, D.A.: Qos issues in web services. IEEE Internet Comp. 6(6), 72–75 (2002)
Milanovic, N., Malek, M.: Current solutions for web service composition. IEEE Internet Comp. 8(6), 51–59 (2004)
Mucientes, M., Lama, M., Couto, M.I.: A genetic programming-based algorithm for composing web services. In: 9th Int. Conf. Intelligent Systems Design and Applications, pp. 379–384. IEEE (2009)
Rao, J., Su, X.: A survey of automated web service composition methods. In: Cardoso, J., Sheth, A.P. (eds.) SWSWPC 2004. LNCS, vol. 3387, pp. 43–54. Springer, Heidelberg (2005)
Rezaie, H., NematBaksh, N., Mardukhi, F.: A multi-objective particle swarm optimization for web service composition. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds.) NDT 2010. CCIS, vol. 88, pp. 112–122. Springer, Heidelberg (2010)
Rodriguez-Mier, P., Mucientes, M., Lama, M., Couto, M.I.: Composition of web services through genetic programming. Evolut. Intell. 3(3-4), 171–186 (2010)
Sawczuk da Silva, A., Ma, H., Zhang, M.: A graph-based particle swarm optimisation approach to qos-aware web service composition. In: IEEE Congress on Evolutionary Computation (CEC) (2014)
Wang, A., Ma, H., Zhang, M.: Genetic programming with greedy search for web service composition. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds.) DEXA 2013, Part II. LNCS, vol. 8056, pp. 9–17. Springer, Heidelberg (2013)
Xia, H., Chen, Y., Li, Z., Gao, H., Chen, Y.: Web service selection algorithm based on particle swarm optimization. In: 8th IEEE Int. Conf. Dependable, Autonomic and Secure Computing, pp. 467–472 (2009)
Xiao, L., Chang, C.K., Yang, H.-I., Lu, K.-S., Jiang, H.-Y.: Automated web service composition using genetic programming. In: IEEE 36th Annual Conf. Computer Software and Applications, pp. 7–12 (2012)
Yu, Y., Ma, H., Zhang, M.: An adaptive genetic programming approach to qos-aware web services composition. In: IEEE Congress Evolutionary Computation (CEC), pp. 1740–1747 (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
da Silva, A.S., Ma, H., Zhang, M. (2014). A GP Approach to QoS-Aware Web Service Composition and Selection. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-13563-2_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13562-5
Online ISBN: 978-3-319-13563-2
eBook Packages: Computer ScienceComputer Science (R0)