Task distribution offloading algorithm of vehicle edge network based on DQN

Haitao ZHAO; Tangwei ZHANG; Yue CHEN; Houlin ZHAO; Hongbo ZHU

doi:10.11959/j.issn.1000-436x.2020160

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Task distribution offloading algorithm of vehicle edge network based on DQN

Papers | 更新时间：2024-06-05

- Task distribution offloading algorithm of vehicle edge network based on DQN
- Journal on Communications Vol. 41, Issue 10, Pages: 172-178(2020)
- 作者机构：
  
  1. 南京邮电大学教育部泛在网络健康服务系统工程研究中心，江苏南京 210003
  2. 南京邮电大学江苏省无线通信重点实验室，江苏南京 210003
  3. 南京邮电大学通信与信息工程学院，江苏南京 210003
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61771252);The Natural Science Foundation of Jiangsu Province(BK20171444);The Major Natural Science Research Project of Jiangsu Higher Education Institutions(18KJA510005);Jiangsu Province Special Fund Project for Transformation of Scientific and Technological Achievements(BA2019058);The Object of Jiangsu Province “333 High-level Talent Training Project”(JSCX17_0224)
- DOI：10.11959/j.issn.1000-436x.2020160
  CLC： TP399
- Online First：2020-10，
  
  Published：25 October 2020
- 稿件说明：
移动端阅览
Haitao ZHAO, Tangwei ZHANG, Yue CHEN, et al. Task distribution offloading algorithm of vehicle edge network based on DQN[J]. Journal on Communications, 2020, 41(10): 172-178.
DOI：

Haitao ZHAO, Tangwei ZHANG, Yue CHEN, et al. Task distribution offloading algorithm of vehicle edge network based on DQN[J]. Journal on Communications, 2020, 41(10): 172-178. DOI： 10.11959/j.issn.1000-436x.2020160.

摘要

为实现车辆终端用户任务执行时延与处理速率、能耗的最佳均衡关系，针对车联网的边缘接入环境，提出了一种基于深度 Q 网络（DQN）的计算任务分发卸载算法。首先根据层次分析法对不同车辆终端的计算任务进行优先级划分，从而为计算任务处理速率赋予不同的权重建立关系模型；然后引入基于深度Q网络的边缘计算方法，以计算任务处理速率加权和为优化目标建立任务卸载模型；最后建立基于 DQN 的车辆终端自主最优任务卸载策略，最大化卸载决策制定模型的长期效用。仿真结果表明，相比Q学习算法，所提算法有效提高了任务执行效率。

Abstract

In order to achieve the best balance between latency

computational rate and energy consumption

for a edge access network of IoV

a distribution offloading algorithm based on deep Q network (DQN) was considered.Firstly

these tasks of different vehicles were prioritized according to the analytic hierarchy process (AHP)

so as to give different weights to the task processing rate to establish a relationship model.Secondly

by introducing edge computing based on DQN

the task offloading model was established by making weighted sum of task processing rate as optimization goal

which realized the long-term utility of strategies for offloading decisions.The performance evaluation results show that

compared with the Q-learning algorithm

the average task processing delay of the proposed method can effectively improve the task offload efficiency.

关键词

Keywords

references

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