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An efficient approach for multi-user multi-cloud service composition in human–land sustainable computational systems

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Abstract

The increasing social problems on population, resources and environment enable the interaction between nature and humanity to become one of the most active research fields in the world. In this paper, we propose a novel framework of human–land sustainable computational system, which fully advances the progress of the development of our society utilizing the cloud computing and big data analysis technologies. Particularly, the study on quality of land management has attracted much attention. With the proposed framework, multi-user multi-cloud environment (MUMCE) is firstly presented, and evaluation of land quality is regarded as various services, such as soil acidity and alkalinity, soil thickness, soil texture, smoothness and field layout. Then, this paper formulates the problem of formal concept analysis-based multi-cloud composition recommendation with regard to multiple users. To address this problem, this paper first adopts the collaborative filtering to obtain the services request of the target user, then the service–provider concept lattices are constructed, and finally the best multi-cloud composition is selected and further recommended for the target user. Meanwhile, the corresponding algorithm is also devised. A case study is conducted for evaluating the feasibility and effectiveness of the proposed approach.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant No. 61702317), MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2014-1-00720) supervised by the IITP (Institute for Information and Communications Technology Planning and Evaluation) and the National Research Foundation of Korea (No. 2017R1A2B1008421) and was also supported by the Natural Science Basic Research Plan in Shaanxi Province of China (2019JM-379) and the Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shaanxi Province (Grant No. 2017024).

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Authors and Affiliations

  1. Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, China

    Beibei Pang, Fei Hao & Yixuan Yang

  2. School of Computer Science, Shaanxi Normal University, Xi’an, China

    Beibei Pang, Fei Hao & Yixuan Yang

  3. Department of Computer Software Engineering, Soonchunhyang University, Asan, South Korea

    Doo-Soon Park

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  1. Beibei Pang
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  2. Fei Hao
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  3. Yixuan Yang
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  4. Doo-Soon Park
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Correspondence to Fei Hao.

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Pang, B., Hao, F., Yang, Y. et al. An efficient approach for multi-user multi-cloud service composition in human–land sustainable computational systems. J Supercomput 76, 5442–5459 (2020). https://doi.org/10.1007/s11227-019-03140-w

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