QoS aware service clustering to bootstrap the web service selection

BTGS Kumara, I Paik, T Siriweera… - 2017 IEEE …, 2017 - ieeexplore.ieee.org
2017 IEEE international conference on services computing (SCC), 2017ieeexplore.ieee.org
Web services have been extended to give value-added customized services to users
through service composition. Service Composition consists of four stages: planning,
discovery, selection, and execution. Web service discovery and selection are becoming
challenging and time-consuming tasks due to large number of Web services available on
the internet. Organizing Web services into clusters of similar services to aid the pruning of
the query space is one of the solutions for the issues. Web services can be clustered into …
Web services have been extended to give value-added customized services to users through service composition. Service Composition consists of four stages: planning, discovery, selection, and execution. Web service discovery and selection are becoming challenging and time-consuming tasks due to large number of Web services available on the internet. Organizing Web services into clusters of similar services to aid the pruning of the query space is one of the solutions for the issues. Web services can be clustered into similar groups by considering functional attributes to increase the performance of the service discovery. In selection stage, algorithm has to select a single service among the large number of functional equal services by considering the Quality of Service (QoS) values. In this paper, we propose QoS aware service clustering approach to increase the performance of the service selection. Here, service clusters are created using functionality as the first factor, then QoS properties being considered as secondary factors. Our approach considers QoS profit values to compute the QoS similarity. In this paper, we apply a spatial clustering technique called the Spherical Associated Keyword Space which is projected clustering result from a three-dimensional sphere to a two dimensional (2D) spherical surface for 2D visualization. Empirical study of our approach has proved the effectiveness of clustering results.
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