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A heuristic placement selection approach of partitions of mobile applications in mobile cloud computing model based on community collaboration

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Abstract

Mobile cloud computing has become a research hotspot in the fields of cloud computing and mobile computing. At present, many researches on offloading computation to cloud resource and their related work have been done and some achievements have been obtained in the field of mobile cloud computing. However, some problems of the current researches have also been found such as the separation between computation and data, limited network resource, and lack of location-aware, etc. In this paper, we have presented a novel mobile cloud computing model and proposed a heuristic approach MCC-particle swarm optimization (PSO) of placement selection of mobile applications’ partitions for minimizing the overall processing time and energy saving. The algorithm of the proposed MCC-PSO approach includes two parts. One is that it combines the PSO idea with the simulated annealing (SA) idea to achieve an improved PSO-based approach with the better global search’s ability. The other one is that it uses the probability theory and mathematical statistics and once again utilizes the SA idea to deal with the data obtained from the improved PSO-based process to get the final solution. And thus the whole approach achieves a long-term optimization of mobile cloud computing. The experimental results demonstrate that MCC-PSO evidently reduces the overall processing time of mobile applications and energy consumption of mobile devices while better guaranteeing the performance of executing mobile applications. MCC-PSO better achieves the idea of mobile cloud computing and makes the mobile cloud computing model more high-effective and meaningful.

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Acknowledgements

This work is supported by the National Science-Technology Support Project (2014BAH02F02), the Jilin Provincial Education Office [the 13th Five-Year Plan Science and Technology Research Project (2016) No. 347], Graduate Innovation Fund (Project 2016029) of Jilin University, a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme under MONICA Project (Grant No. PIRSES-GA-2011-295222), the National Natural Science Foundation of China (61472158, 61572228), the Premier Discipline Enhancement Scheme from Zhuhai Government, and the Premier Key-Discipline Enhancement Scheme from Guangdong Government.

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Authors and Affiliations

  1. School of Computer Science and Engineering, ChangChun University of Technology, Changchun, 130012, People’s Republic of China

    Jia Zhao

  2. High-Assurance Software Laboratory, INESC TEC & University of Minho, Braga, Portugal

    Jia Zhao

  3. Department of Computing and Communication Technologies, Oxford Brookes University, Wheatley, Oxford, OX33 1HX, UK

    Shumao Ou

  4. College of Computer Science and Technology and the Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, No. 2699 Qianjin Street, Changchun, 130012, People’s Republic of China

    Liang Hu, Yan Ding & Gaochao Xu

  5. Zhuhai College of Jilin University, Zhuhai, 519041, People’s Republic of China

    Yan Ding

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  1. Jia Zhao
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  2. Shumao Ou
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  3. Liang Hu
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  4. Yan Ding
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  5. Gaochao Xu
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Correspondence to Gaochao Xu.

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Zhao, J., Ou, S., Hu, L. et al. A heuristic placement selection approach of partitions of mobile applications in mobile cloud computing model based on community collaboration. Cluster Comput 20, 3131–3146 (2017). https://doi.org/10.1007/s10586-017-1011-4

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