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

A Multi-objective Computation Offloading Method in Multi-cloudlet Environment

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  • @INPROCEEDINGS{10.1007/978-3-030-48513-9_2,
        author={Kai Peng and Shuaiqi Zhu and Lixin Zheng and Xiaolong Xu and Victor Leung},
        title={A Multi-objective Computation Offloading Method in Multi-cloudlet Environment},
        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 Multi-cloudlet Energy consumption Time consumption},
        doi={10.1007/978-3-030-48513-9_2}
    }
    
  • Kai Peng
    Shuaiqi Zhu
    Lixin Zheng
    Xiaolong Xu
    Victor Leung
    Year: 2020
    A Multi-objective Computation Offloading Method in Multi-cloudlet Environment
    CLOUDCOMP
    Springer
    DOI: 10.1007/978-3-030-48513-9_2
Kai Peng,*, Shuaiqi Zhu1, Lixin Zheng2, Xiaolong Xu3, Victor Leung4
  • 1: Huaqiao University
  • 2: Fujian Provincial Academic Engineering Research Centre in Industrial Intellectual Techniques and Systems
  • 3: Nanjing University of Science and Technology
  • 4: The University of British Columbia
*Contact email: pkbupt@gmail.com

Abstract

Computation offloading is becoming a promising technology that can improve quality of service for mobile users in mobile edge computing. However, it becomes much difficult when there are multi-cloudlet near to the mobile users. The resources of the cloudlet are heterogeneous and finite, and thus it is challenge to choose the best cloudlet for the multi-user. The issue for multi-user in multi-cloudlet environment is well-investigated in this study. Firstly, we establish a multi-objective optimization model with respect to time consumption and energy consumption of mobile devices. Moreover, we devise a multi-objective computation offloading method based on improved fast and elitist genetic algorithm for selecting the optimal offloading strategies. Finally, compared with other methods, numerous experiments proved that our proposed method have advantages in effectiveness and efficiency.