北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2019, Vol. 42 ›› Issue (6): 64-69,104.doi: 10.13190/j.jbupt.2019-155

• 论文 • 上一篇    下一篇

基于DRL的MEC任务卸载与资源调度算法

薛宁1, 霍如1,2, 曾诗钦3, 汪硕2,3, 黄韬2,3   

  1. 1. 北京工业大学 北京未来网络科技高精尖创新中心, 北京 100124;
    2. 网络通信与安全紫金山实验室, 南京 211111;
    3. 北京邮电大学 网络与交换技术国家重点实验室, 北京 100876
  • 收稿日期:2019-07-11 出版日期:2019-12-28 发布日期:2019-11-15
  • 通讯作者: 霍如(1988-),女,讲师,E-mail:huoru@bjut.edu.cn. E-mail:huoru@bjut.edu.cn
  • 作者简介:薛宁(1994-),男,硕士生.
  • 基金资助:
    国家自然科学基金项目(61902033);未来网络操作系统发展战略研究(2019-XY-5)

Tasks Offloading and Resource Scheduling Algorithm Based on Deep Reinforcement Learning in MEC

XUE Ning1, HUO Ru1,2, ZENG Shi-qing3, WANG Shuo2,3, HUANG Tao2,3   

  1. 1. Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing 100124, China;
    2. Purple Mountain Laboratories, Nanjing 211111, China;
    3. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2019-07-11 Online:2019-12-28 Published:2019-11-15

摘要: 为提高多接入边缘计算(MEC)任务卸载效率,提出了一个任务卸载和异构资源调度的联合优化模型.考虑异构的通信资源和计算资源,联合最小化用户的设备能耗、任务执行时延和付费,并利用深度强化学习(DRL)算法对该模型求最优的任务卸载算法.仿真结果表明,该优化算法比银行家算法的设备能耗、时延和付费的综合指标提升了27.6%.

关键词: 多接入边缘计算, 任务卸载, 异构资源调度, 深度强化学习

Abstract: In order to improve the task offloading efficiency in multi-access edge computing (MEC), a joint optimization model for task offloading and heterogeneous resource scheduling was proposed, considering the heterogeneous communication resources and computing resources, jointly minimizing the energy consumption of user equipment, task execution delay, and the payment. A deep reinforcement learning method is adopted in the model to obtain the optimal offloading algorithm. Simulations show that the proposed algorithm improves the comprehensive indexes of equipment energy consumption, delay, and payment by 27.6%, compared to the Banker's algorithm.

Key words: multi-access edge computing, task offloading, heterogeneous resource scheduling, deep reinforcement learning

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