Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. 9th EAI International Conference, CloudComp 2019, and 4th EAI International Conference, SmartGIFT 2019, Beijing, China, December 4-5, 2019, and December 21-22, 2019

Research Article

Efficient Multi-user Computation Scheduling Strategy Based on Clustering for Mobile-Edge Computing

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  • @INPROCEEDINGS{10.1007/978-3-030-48513-9_22,
        author={Qing-Yan Lin and Guang-Shun Li and Jun-Hua Wu and Ying Zhang and JiaHe Yan},
        title={Efficient Multi-user Computation Scheduling Strategy Based on Clustering for Mobile-Edge Computing},
        proceedings={Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. 9th EAI International Conference, CloudComp 2019, and 4th EAI International Conference, SmartGIFT 2019, Beijing, China, December 4-5, 2019, and December 21-22, 2019},
        proceedings_a={CLOUDCOMP},
        year={2020},
        month={6},
        keywords={Mobile edge computing Offloading decision Node clustering Optimal strategy Nash equilibrium},
        doi={10.1007/978-3-030-48513-9_22}
    }
    
  • Qing-Yan Lin
    Guang-Shun Li
    Jun-Hua Wu
    Ying Zhang
    JiaHe Yan
    Year: 2020
    Efficient Multi-user Computation Scheduling Strategy Based on Clustering for Mobile-Edge Computing
    CLOUDCOMP
    Springer
    DOI: 10.1007/978-3-030-48513-9_22
Qing-Yan Lin1, Guang-Shun Li1, Jun-Hua Wu1,*, Ying Zhang1, JiaHe Yan1
  • 1: Qufu Normal University
*Contact email: shdwjh@163.com

Abstract

The Mobile Edge Computing (MEC) is a new paradigm that can meet the growing computing needs of mobile applications. Terminal devices can transfer tasks to MEC servers nearby to improve the quality of computing. In this paper, we investigate the multi-user computation offloading problem for mobile-edge computing. We study two different computation models, local computing and edge computing. First, we drive the expressions for time delay and energy consumption for local and edge computing. Then, we propose a server partitioning algorithm based on clustering. We propose a task scheduling and offloading algorithm in a multi-users MEC system. We formulate the tasks offloading decision problem as a multi-user game, which always has a Nash equilibrium. Our proposed algorithms are finally verified by numerical results, which show that the scheduling strategy based on clustering can significantly reduce the energy consumption and overhead.