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

doi:10.11959/j.issn.2096-3750.2022.00258

您当前的位置：

首页 >

文章列表页 >

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

Theory and Technology | 更新时间：2024-06-05

- Research on elite hierarchical task offloading strategy based on reinforcement learning in edge-cloud collaboration scenario
- Chinese Journal on Internet of Things Vol. 6, Issue 1, Pages: 91-100(2022)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2022.00258
  CLC： TP391
- Published：30 March 2022，
  
  Published Online：2022-03，
- 稿件说明：
移动端阅览
JUAN FANG, ZHIYUAN YE, MENGYUAN ZHANG, et al. Research on elite hierarchical task offloading strategy based on reinforcement learning in edge-cloud collaboration scenario. [J]. Chinese journal on internet of things, 2022, 6(1): 91-100.
DOI：

JUAN FANG, ZHIYUAN YE, MENGYUAN ZHANG, et al. Research on elite hierarchical task offloading strategy based on reinforcement learning in edge-cloud collaboration scenario. [J]. Chinese journal on internet of things, 2022, 6(1): 91-100. DOI： 10.11959/j.issn.2096-3750.2022.00258.

摘要

随着5G的发展以及应用程序功能的丰富，应用程序对终端设备的计算能力提出了更高的要求，为了提高终端设备对应用程序的计算能力，降低任务的处理时间，针对移动边缘计算环境，提出了一种边云协同的任务卸载方式，并设计了基于强化学习的精英分层进化算法（RL-EHEA

elite hierarchical evolutionary algorithm combined with reinforcement learning）进行卸载决策，使多个具有依赖关系与截止时间的任务对计算资源竞争。结果表明，与遗传算法（GA

genetic algorithm）和精英遗传算法（EGA

elite genetic algorithm）相比，RL-EHEA能缩短任务的处理时间，得到更优的资源分配策略。

Abstract

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.

关键词

移动边缘计算任务卸载边云协同进化算法串行任务

Keywords

mobile edge computingtask offloadingedge-cloud collaborationevolutionary algorithmserial task

references

ZHANG K, LENG S P, HE Y J ,et al. Mobile edge computing and networking for green and low-latency Internet of Things[J]. IEEE Communications Magazine, 2018,56(5): 39-45.

SABELLA D, VAILLANT A, KUURE P ,et al. Mobile-edge computing architecture:the role of MEC in the Internet of Things[J]. IEEE Consumer Electronics Magazine, 2016,5(4): 84-91.

NING Z L, XIA F, ULLAH N ,et al. Vehicular social networks:enabling smart mobility[J]. IEEE Communications Magazine, 2017,55(5): 16-55.

TALEB T, DUTTA S, KSENTINI A ,et al. Mobile edge computing potential in making cities smarter[J]. IEEE Communications Magazine, 2017,55(3): 38-43.

施巍松, 张星洲, 王一帆 ,等. 边缘计算:现状与展望[J]. 计算机研究与发展, 2019,56(1): 69-89.

SHI W S, ZHANG X Z, WANG Y F ,et al. Edge computing:state-of-the-art and future directions[J]. Journal of Computer Research and Development, 2019,56(1): 69-89.

TANG L J, TANG B, KANG L Y ,et al. A novel task caching and migration strategy in multi-access edge computing based on the genetic algorithm[J]. Future Internet, 2019,11(8): 181.

LI Z, ZHU Q . Genetic algorithm-based optimization of offloading and resource allocation in mobile-edge computing[J]. Information, 2020,11(2): 83.

赵海涛, 张唐伟, 陈跃 ,等. 基于DQN的车载边缘网络任务分发卸载算法[J]. 通信学报, 2020,41(10): 172-178.

ZHAO H T, ZHANG T W, CHEN Y ,et al. Task distribution offloading algorithm of vehicle edge network based on DQN[J]. Journal on Communications, 2020,41(10): 172-178.

WANG Y, TAO X F, ZHANG X F ,et al. Cooperative task offloading in three-tier mobile computing networks:an ADMM framework[J]. IEEE Transactions on Vehicular Technology, 2019,68(3): 2763-2776.

葛志诚, 徐恪, 陈靓 ,等. 一种移动内容分发网络的分层协同缓存机制[J]. 计算机学报, 2018,41(12): 2769-2786.

GE Z C, XU K, CHEN L ,et al. A hierarchical cooperative caching strategy for mobile content delivery network[J]. Chinese Journal of Computers, 2018,41(12): 2769-2786.

XIAO Y, KRUNZ M . QoE and power efficiency tradeoff for fog computing networks with fog node cooperation[C]// Proceedings of IEEE INFOCOM 2017 - IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2017: 1-9.

NGUYEN P D, HA V N, LE L B . Computation offloading and resource allocation for backhaul limited cooperative MEC systems[C]// Proceedings of 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall). Piscataway:IEEE Press, 2019: 1-6.

梁冰, 纪雯 . 基于次模优化的边云协同多用户计算任务迁移方法[J]. 通信学报, 2020,41(10): 25-36.

LIANG B, JI W . Multiuser computation offloading for edge-cloud collaboration using submodular optimization[J]. Journal on Communications, 2020,41(10): 25-36.

NING Z L, DONG P R, KONG X J ,et al. A cooperative partial computation offloading scheme for mobile edge computing enabled Internet of Things[J]. IEEE Internet of Things Journal, 2019,6(3): 4804-4814.

REN J K, YU G D, HE Y H ,et al. Collaborative cloud and edge computing for latency minimization[J]. IEEE Transactions on Vehicular Technology, 2019,68(5): 5031-5044.

LI Y, ZHAO Y L, SHAO J . Empirical study on CNG refilling behaviors of different timescale based on boxplot method[J]. IOP Conference Series:Earth and Environmental Science, 2018,186: 012021.

Views

448

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on location privacy protection of mobile terminals for maritime monitoring sensor networks

D2D computation task offloading for efficient federated learning

Application of EdgeFlow mobile edge computing in Internet of things

Task offloading and resource allocation in vehicle heterogeneous networks with MEC

A sensing and control closed-loop scheduling strategy for UAV swarms

Related Author

Xin SU

Su JIANG

Yiqing ZHOU

Xiaoran CAI

Xiaopeng MO

Jie XU

Shuchang CONG

Chao YAO

Related Institution

Institute of Computing Technology, Chinese Acadeny of Sciences

University of Chinese Academy of Sciences

College of Internet of Things Engineering, Hohai University

School of Information Science and Technology, Harbin Institute of Technology

Guangdong Provincial Key Laboratory of Space-Aerial Networking and Intelligent Sensing, Harbin Institute of Technology

⁰