Testbeds and Research Infrastructures for the Development of Networks and Communications. 13th EAI International Conference, TridentCom 2018, Shanghai, China, December 1-3, 2018, Proceedings

Research Article

A Balanced Cloudlet Management Method for Wireless Metropolitan Area Networks

  • @INPROCEEDINGS{10.1007/978-3-030-12971-2_4,
        author={Xiaolong Xu and Yuhao Chen and Lianyong Qi and Jing He and Xuyun Zhang},
        title={A Balanced Cloudlet Management Method for Wireless Metropolitan Area Networks},
        proceedings={Testbeds and Research Infrastructures for the Development of Networks and Communications. 13th EAI International Conference, TridentCom 2018, Shanghai, China, December 1-3, 2018, Proceedings},
        proceedings_a={TRIDENTCOM},
        year={2019},
        month={2},
        keywords={Cloudlet WMAN VM migration Energy consumption Time consumption},
        doi={10.1007/978-3-030-12971-2_4}
    }
    
  • Xiaolong Xu
    Yuhao Chen
    Lianyong Qi
    Jing He
    Xuyun Zhang
    Year: 2019
    A Balanced Cloudlet Management Method for Wireless Metropolitan Area Networks
    TRIDENTCOM
    Springer
    DOI: 10.1007/978-3-030-12971-2_4
Xiaolong Xu,*, Yuhao Chen, Lianyong Qi1, Jing He, Xuyun Zhang2
  • 1: Qufu Normal University
  • 2: University of Auckland
*Contact email: njuxlxu@gmail.com

Abstract

With the rapid development of wireless communication technology, cloudlet-based wireless metropolitan area network, which provides people with more convenient network services, has become an effiective paradigm to meet the growing demand for requirements of wireless cloud computing. Currently, the energy consumption of cloudlets can be reduced by migrating tasks, but how to jointly optimize the time consumption and energy consumption in the process of migrations is still a significant problem. In this paper, a alanced loudlet anagement method, named BCM, is proposed to address the above challenge. Technically, the Simple Additive Weighting (SAW) and Multiple Criteria Decision Making (MCDM) techniques are applied to optimize virtual machine scheduling strategy. Finally, simulation results demonstrate the effectiveness of our proposed method.