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›› 2020, Vol. 37 ›› Issue (5): 688-698.DOI: 10.7523/j.issn.2095-6134.2020.05.014

• Research Articles • Previous Articles     Next Articles

Online learning-based task offloading algorithms for dynamic fog networks

TAN Youyu1,3,4,5, CHEN Lei2, ZHOU Mingtuo1,5, WANG Kunlun4,5, YANG Yang4,5, ZHANG Wuxiong1   

  1. 1. Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China;
    2. State Grid Zhejiang Power Co Ltd, Hangzhou 310007, China;
    3. University of Chinese Academy of Sciences, Beijing 100049, China;
    4. ShanghaiTech University, Shanghai 201210, China;
    5. Shanghai Institute of Fog Computing Technology, Shanghai 201210, China
  • Received:2019-01-30 Revised:2019-03-28 Online:2020-09-15
  • Supported by:
     

Abstract: Task offloading is one of the main techniques of the fog computing, and it means that the computation-limited nodes offload the tasks to the capable nodes for help. Firstly, we propose TOD (online learning-based task offloading algorithm for the dynamic fog networks under stationary status) using the MAB (multi-armed bandit) theory, which aims at minimizing the long term offloading delay and improving the task offloading success ratio. Then, we propose TOD-N (online learning-based task offloading algorithm for the dynamic fog networks under non-stationary status) to efficiently track the changes of the sharing computing resources and the channel environment. Moreover, we analyze the performances of the two algorithms on the optimality, the computational complexity, and the memory usage. Simulation results show that the long term average offloading delays achieved by the two algorithms are almost as good as the one achieved by the Oracle algorithm, and the offloading success ratios are also efficiently promoted. Moreover, TOD-N tracks the optimal resources efficiently under non-stationary network status.

 

Key words: fog computing, task offloading, online learning, multi-armed bandit

CLC Number: