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.
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13 November 2017
The original version of this article unfortunately contained mistakes in the author spelling. The first author name is misspelled. The correct spelling and author list is presented above.
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Acknowledgments
The work presented in this study was supported by the NSFC (61472047, 71402097), Open Research Fund Program of Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, and Macao Science and Technology Development Fund (104/2014/A3).
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A correction to this article is available online at https://doi.org/10.1007/s10796-017-9813-8.
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Huang, L., Zhao, Q., Li, Y. et al. Reliable and efficient big service selection. Inf Syst Front 19, 1273–1282 (2017). https://doi.org/10.1007/s10796-017-9767-x
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DOI: https://doi.org/10.1007/s10796-017-9767-x