Reliable and efficient big service selection

Ling Huang (), Qinglin Zhao (), Yan Li (), Shangguang Wang (), Lei Sun () and Wu Chou ()
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Ling Huang: Beijing University of Posts and Telecommunications
Qinglin Zhao: Macau University of Science and Technology
Yan Li: Shanghai International Studies University
Shangguang Wang: Beijing University of Posts and Telecommunications
Lei Sun: Beijing University of Posts and Telecommunications
Wu Chou: Huawei Technologies Co., Ltd.

Information Systems Frontiers, 2017, vol. 19, issue 6, No 5, 1273-1282

Abstract: Abstract Big services, both virtual (e.g., cloud services) and physical (e.g., public transportation), are evolving rapidly to handle and deal with big data. By aggregating services from various domains, big services adopt selection schemes to produce composite service solutions that meet customer requirements. However, unlike traditional service selection, a huge number of big services require some lengthy selection processes to improve the service reliability. In this paper, we propose an efficient big service selection approach based on the coefficient of variation and mixed integer programming that improves the solution in two senses: 1) minimizing the time cost and 2) maximizing the reliability. We tested our approach on real-world datasets, and the experimental results indicated that our approach is superior to others.

Keywords: Service computing; Big service; Service selection; QoS (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10796-017-9767-x

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