Abstract
In cloud computing domain, often service providers offer services with same functionalities, but with varying quality metrics. A suitable service selection method finds the most appropriate solution among the alternatives. The challenge is to deliver a solution satisfying the requirement (quality and other) of a consumer with minimum possible execution time. Many conflicting QoS objectives increase the complexity of the problem. In fact, the problem may be formulated as a multi-objective, NP-hard optimization problem. Most of the existing solutions either satisfies the QoS demands of consumer or only reduces execution time by considering a sub-set of required QoS metrics. Consumer’s feedback on the choice of required QoS metrics not only shall help increasing user satisfaction, but also may reduce the complexity effectively. However, this depends on the domain knowledge of a consumer. In this work, we have proposed a goodness measure that replaces all QoS metrics by a single one. The new technique using dimension reduction is proposed to offer significant improvement compared to the existing works in terms of execution time. Moreover, the solution satisfies all the QoS requirements of a consumer in most of the cases. The proposed data driven selection approach has been implemented and the experimental results substantiate the claims as mentioned.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Al-Masri, E., Mahmoud, Q.H.: QoS-based discovery and ranking of web services. In: Proceedings of 16th International Conference on Computer Communications and Networks. ICCCN 2007, pp. 529–534. IEEE (2007)
Barkat Ullah, A.S., Sarker, R., Cornforth, D.: Search space reduction technique for constrained optimization with tiny feasible space. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 881–888. ACM (2008)
Bhattacharya, A., Choudhury, S.: An efficient service selection approach through a goodness measure of the participating QoS. In: Proceedings of the International Conference on Informatics and Analytics, ICIA 2016, pp. 94:1–94:6, New York, NY, USA. ACM (2016). https://doi.org/10.1145/2980258.2980451. http://doi.acm.org/10.1145/2980258.2980451
Chen, L., Wu, J., Jian, H., Deng, H., Wu, Z.: Instant recommendation for web services composition. IEEE Trans. Serv. Comput. 7(4), 586–598 (2014)
Dastjerdi, A.V., Garg, S.K., Rana, O.F., Buyya, R.: CloudPick: a framework for QoS-aware and ontology-based service deployment across clouds. Softw. Pract. Exp. 45(2), 197–231 (2015)
Dou, W., Zhang, X., Liu, J., Chen, J.: Hiresome-II: towards privacy-aware cross-cloud service composition for big data applications. IEEE Trans. Parallel Distrib. Syst. 26(2), 455–466 (2015)
Elshater, Y., Elgazzar, K., Martin, P.: goDiscovery: web service discovery made efficient. In: 2015 IEEE International Conference on Web Services (ICWS), pp. 711–716. IEEE (2015)
Fodor, I.K.: A survey of dimension reduction techniques. Technical report, Lawrence Livermore National Laboratory, CA (US) (2002)
Jatoth, C., Gangadharan, G., Fiore, U., Buyya, R.: QoS-aware big service composition using mapreduce based evolutionary algorithm with guided mutation. Future Gener. Comput. Syst. 86, 1008–1018 (2018)
Jurca, R., Faltings, B., Binder, W.: Reliable QoS monitoring based on client feedback. In: Proceedings of the 16th International Conference on World Wide Web, pp. 1003–1012. ACM (2007)
Karim, R., Ding, C., Miri, A.: An end-to-end QoS mapping approach for cloud service selection. In: 2013 IEEE Ninth World Congress on Services, pp. 341–348. IEEE (2013)
Klein, A., Ishikawa, F., Honiden, S.: Towards network-aware service composition in the cloud. In: Proceedings of the 21st International Conference on World Wide Web, pp. 959–968. ACM (2012)
Lee, K.S., Geem, Z.W.: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput. Methods Appl. Mech. Eng. 194(36), 3902–3933 (2005)
Ludwig, S.A.: Clonal selection based genetic algorithm for workflow service selection. In: 2012 IEEE Congress on Evolutionary Computation, pp. 1–7. IEEE (2012)
Tao, F., LaiLi, Y., Xu, L., Zhang, L.: FC-PACO-RM: a parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Trans. Ind. Inform. 9(4), 2023–2033 (2013)
Tao, F., Zhao, D., Hu, Y., Zhou, Z.: Resource service composition and its optimal-selection based on particle swarm optimization in manufacturing grid system. IEEE Trans. Ind. Inform. 4(4), 315–327 (2008)
Ye, Z., Zhou, X., Bouguettaya, A.: Genetic algorithm based QoS-aware service compositions in cloud computing. In: Yu, J.X., Kim, M.H., Unland, R. (eds.) DASFAA 2011. LNCS, vol. 6588, pp. 321–334. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20152-3_24
Yu, Q., Bouguettaya, A.: Computing service skyline from uncertain QoWS. IEEE Trans. Serv. Comput. 3(1), 16–29 (2010)
Zhang, J., Liu, X.: Evaluation and optimization of QoS-aware network management framework based on process synergy and resource allocation. J. Ambient Intell. Hum. Comput., 1–9 (2018)
Zhang, X., Dou, W.: Preference-aware QoS evaluation for cloud web service composition based on artificial neural networks. In: Wang, F.L., Gong, Z., Luo, X., Lei, J. (eds.) WISM 2010. LNCS, vol. 6318, pp. 410–417. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16515-3_51
Zhao, X., Wen, Z., Li, X.: QoS-aware web service selection with negative selection algorithm. Knowl. Inf. Syst. 40(2), 349–373 (2014)
Zheng, Z., Ma, H., Lyu, M.R., King, I.: QoS-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4(2), 140–152 (2011)
Acknowledgements
This publication is an outcome of the R&D work undertaken in the ITRA project of Media Lab Asia entitled Remote Health: A Framework for Healthcare Services using Mobile and Sensor-Cloud Technologies.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer-Verlag GmbH Germany, part of Springer Nature
About this chapter
Cite this chapter
Bhattacharya, A., Choudhury, S. (2019). QoS Preservation in Web Service Selection. In: Nguyen, N., Kowalczyk, R., Xhafa, F. (eds) Transactions on Computational Collective Intelligence XXXIII. Lecture Notes in Computer Science(), vol 11610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-59540-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-662-59540-4_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-59539-8
Online ISBN: 978-3-662-59540-4
eBook Packages: Computer ScienceComputer Science (R0)