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An energy-efficient task migration scheme based on genetic algorithms for mobile applications in CloneCloud

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Abstract

The limitations of mobile devices have attracted researchers to work out an energy-efficient mechanism to enhance user experience. The emerging mobile cloud computing (MCC) provides a new approach to solve this problem. Some parts of mobile applications, i.e., heavy computational tasks, are migrated to remote servers with powerful computational resources, which can improve the performance of mobile devices. This paper focuses on a popular MCC architecture, CloneCloud, and constructs a scheduling problem of task migration as a constrained stochastic shortest path problem in a directed acyclic graph. And then it designs a scheduling algorithm based on genetic algorithm to obtain the optimal task migrations. A user flexibly makes migration decisions through its own mobile device and migrates some tasks to the clone in CloneCloud without any change of application codes. Furthermore, this scheme facilitates mobile devices to collaboratively process computational applications. Real testbed experiments in Android smartphone demonstrate that the smartphone is able to save at most 59.42% energy within a time constraint by using the proposed task migration scheme.

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Notes

  1. We use the words task migration and task offloading interchangeably.

  2. We use the words function, method and task interchangeably.

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Acknowledgements

We thank Zhuobin Xu (Xiamen University) for his valuable suggestions and help in the experiments. This work is supported by the Education Research Project for Young Teachers of Fujian Province (No. JAT191084), the Industry and University Cooperation Project of Fujian Province (No. 2018H6018), National Natural Science Foundation of China (Nos. 61771017, 61772438), the CERNET Innovation Project (No. NGII20170718), and the Science and Technology Project of Xiamen (No. 3502Z20183004).

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Authors and Affiliations

  1. School of Information Science and Technology, Xiamen University Tan Kah Kee College, Zhangzhou, China

    Yun Lin

  2. Department of Automation, School of Aerospace Engineering, Xiamen University, Xiamen, China

    Tundong Liu

  3. Fuzhou Institute for Data Technology, Tsinghua-Fuzhou Institute for Data Technology, Beijing, China

    Fufeng Chen

  4. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China

    Yi Xie

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  1. Yun Lin
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Correspondence to Yi Xie.

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Lin, Y., Liu, T., Chen, F. et al. An energy-efficient task migration scheme based on genetic algorithms for mobile applications in CloneCloud. J Supercomput 77, 5220–5236 (2021). https://doi.org/10.1007/s11227-020-03470-0

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