Abstract
Cloud computing uses service-oriented architecture principles to design a web service which enables fast, high-performance software application services, and infrastructural services (for example, servers, networks, middleware, etc.). Cloud computing provides scalable and on-demand storage, middleware, and application as a service. To achieve high availability of cloud computing services such as software, platform, and infrastructural services, it must be scalable and extensible. Web services can be accessed via Internet, and its performance (response time) gets reduced as the network traffic and congestion increase. But cloud users prefer to access the cloud servers with high availability with low response time, while it chooses the best server among the many available. To improve the system performance with respect to a specific quality of service parameter. We proposed a model that classifies the cloud-based web applications into four categorical values. The web services enable to use shared resources. This paper explains how to choose quality parameters to design a web service, which employs QWS dataset with nine quality parameters and 2507 records and data mining techniques such data envelopment analysis, K-nearest neighbor, decision tree, fuzzy multi-attribute decision-making analysis, PNN, and BPNN classifier models. Experimental results concluded that the proposed method FMADM has better performance 91.78% than the existing methods. In future, we can extend this model to design a cloud service based on mixed QoS parameters.
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Acknowledgements
Author would like to personally thank Dr. Eyhab Al-Masri, Assistant Professor, University of Washington for providing the QWS dataset, and also to Dr. Ramakanta Mohanty, Professor, Department of IT, KMIT, Hyderabad for his timely suggestions to carry out this work.
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Swami Das, M., Govardhan, A., Vijaya Lakshmi, D. (2019). Web Services Classification Across Cloud-Based Applications. In: Ray, K., Sharma, T., Rawat, S., Saini, R., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 742. Springer, Singapore. https://doi.org/10.1007/978-981-13-0589-4_23
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DOI: https://doi.org/10.1007/978-981-13-0589-4_23
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