Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (2): 328-334.DOI: 10.11772/j.issn.1001-9081.2019081367

• DPCS 2019 • Previous Articles     Next Articles

Computing task offloading based on multi-cloudlet collaboration

Qingyong WANG, Yingchi MAO(), Yichao WANG, Longbao WANG   

  1. College of Computer and Information,Hohai University,Nanjing Jiangsu 211100,China
  • Received:2019-07-31 Revised:2019-08-20 Accepted:2019-09-17 Online:2019-09-29 Published:2020-02-10
  • Contact: Yingchi MAO
  • About author:WANG Qingyong, born in 1995, M. S. candidate. His research interests include cloud computing, edge computing.
    WANG Yichao, born in 1994, M. S. candidate. His research interests include cloud computing, edge computing.
    WANG Longbao, born in 1977, Ph. D., lecturer. His research interests include big data, cloud computing, management informationization.
  • Supported by:
    the National Key Research and Development Program of China(2018YFC0407905);the Key Research and Development Project of China Huaneng Group(HNKJ17-21)


王庆永, 毛莺池(), 王绎超, 王龙宝   

  1. 河海大学 计算机与信息学院,南京 211100
  • 通讯作者: 毛莺池
  • 作者简介:王庆永(1995—),男,河南固始人,硕士研究生,CCF会员,主要研究方向:云计算、边缘计算
  • 基金资助:


Focusing on the problems of complex process and long response time of task offloading in multi-cloudlet mode, a computing task offloading model based on multi-cloudlet collaboration was constructed, and a Weighted self-Adaptive Inertia Weight Particle Swarm Optimization (WAIW-PSO) algorithm was proposed to solve the optimal offloading scheme quickly. Firstly, the task execution process of mobile terminal-cloudlet-remote cloud was modeled. Secondly, considering the competition of computing resources by multiple users, the task offloading model based on multi-cloudlet collaboration was constructed. Finally, since the complexity of solving the optimal offloading scheme was excessively high, the WAIW-PSO was proposed to solve the offloading problem. Simulation results show that compared with the standard Particle Swarm Optimization (PSO) algorithm and the PSO algorithm with Decreasing Inertia Weight based on Gaussian function (GDIWPSO), WAIW-PSO algorithm can adjust the inertia weight according to evolutionary generation and individual fitness, and it has the better optimization ability and the shortest time for finding the optimal offloading scheme. Experimental results on different task unloading schemes with different numbers of equipments and tasks show that the WAIW-PSO algorithm based offloading schemes can significantly shorten the total task completion time.

Key words: mobile cloud computing, cloudlet, task offloading, multi-cloudlet collaboration, Particle Swarm Optimization (PSO)



关键词: 移动云计算, 微云, 任务卸载, 多微云协作, 粒子群优化

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