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山东大学学报 (工学版) ›› 2024, Vol. 54 ›› Issue (1): 83-90.doi: 10.6040/j.issn.1672-3961.0.2022.344

• 机器学习与数据挖掘 • 上一篇    

基于深度强化学习的物联网服务协同卸载方法

曹宇慧,黄昱泽*,冯北鹏,张淼,郭珍珍   

  1. 重庆交通大学信息科学与工程学院, 重庆 400074
  • 发布日期:2024-02-01
  • 作者简介:曹宇慧(1998— ),女,安徽蚌埠人,硕士研究生,主要研究方向为边缘计算. E-mail:caoyuhui10@163.com. *通信作者简介:黄昱泽(1983— ),男,四川甘洛人,讲师,硕士生导师,博士,主要研究方向为边缘计算、服务计算. E-mail:huangyz@cqjtu.edu.cn
  • 基金资助:
    重庆市自然科学基金资助项目(CSTB2022NSCQ-MSX0368);重庆市教育委员会科学技术研究计划青年项目(KJQN202200702,KJQN201900708,KJQN202100738);国家自然科学基金资助项目(62101080)

A collaborative service offloading approach for Internet of Things based on deep reinforcement learning

CAO Yuhui, HUANG Yuze*, FENG Beipeng, ZHANG Miao, GUO Zhenzhen   

  1. School of Information Science &
    Engineering, Chongqing Jiaotong University, Chongqing 400074, China
  • Published:2024-02-01

摘要: 针对边缘计算中终端算力不足、资源有限和时延较大的问题,提出一种基于深度强化学习的物联网服务协同卸载方法。通过3种不同的卸载方式建立时延模型,挖掘服务之间的关联关系,对关联服务进行协同卸载,加入关联服务的通信时延以建立完善的卸载时延模型,结合整体模型考虑卸载率的取值以及关联服务如何协同卸载使时延最小,从而实现服务调用时延和服务间通信时延的最小化。试验结果表明,与其他算法相比,该算法在获取最优服务卸载策略的同时,系统总服务时延能降低20%左右。

关键词: 边缘计算, 服务卸载, 关联服务, 协同卸载, 深度强化学习

中图分类号: 

  • TP491.8
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