Chinese Journal on Internet of Things ›› 2022, Vol. 6 ›› Issue (1): 91-100.doi: 10.11959/j.issn.2096-3750.2022.00258

• Theory and Technology • Previous Articles     Next Articles

Research on elite hierarchical task offloading strategy based on reinforcement learning in edge-cloud collaboration scenario

Juan FANG, Zhiyuan YE, Mengyuan ZHANG, Jiamei SHI, Ziyi TENG   

  1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
  • Revised:2021-12-29 Online:2022-03-30 Published:2022-03-01
  • Supported by:
    The National Natural Science Foundation of China(61202076);The Natural Science Foundation of Beijing(4192007)


With the development of 5G and the enrichment of application functions, applications have put forward higher requirements on the computing capabilities of terminal devices.In order to improve the computing capabilities of terminal devices on applications and reduce the processing time of tasks, it is aimed at mobile edge computing environments, a task offloading method for edge-cloud collaboration was proposed,and an elite hierarchical evolutionary algorithm combined with reinforcement learning (RL-EHEA) was designed to perform offloading decisions, so that multiple tasks with dependencies and deadlines compete for computing resources.The simulation experiment results show that, compared with genetic algorithm (GA) and elite genetic algorithm (EGA), RL-EHEA can shorten task processing time and obtain better resource allocation strategy.

Key words: mobile edge computing, task offloading, edge-cloud collaboration, evolutionary algorithm, serial task

CLC Number: 

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