Abstract
The proliferation of mobile computing and smartphone technologies has resulted in an increasing number and range of services from myriad service providers. These mobile service providers support numerous emerging services with differing quality metrics but similar functionality. Facilitating an automated service workflow requires fast selection and composition of services from the services pool. The mobile environment is ambient and dynamic in nature, requiring more efficient techniques to deliver the required service composition promptly to users. Selecting the optimum required services in a minimal time from the numerous sets of dynamic services is a challenge. This work addresses the challenge as an optimization problem. An algorithm is developed by combining particle swarm optimization and k-means clustering. It runs in parallel using MapReduce in the Hadoop platform. By using parallel processing, the optimum service composition is obtained in significantly less time than alternative algorithms. This is essential for handling large amounts of heterogeneous data and services from various sources in the mobile environment. The suitability of this proposed approach for big data-driven service composition is validated through modeling and simulation.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
