Real-time scheduling strategy for reasoning tasks in vehicle edge computing

被引：0
作者：
Chen Q. [1 ]
Lu Y. [2 ]
Lin B. [1 ,3 ]
Wang S. [1 ]
Shao X. [1 ]
机构：
[1] College of Physics and Energy, Fujian Normal University, Fuzhou
[2] Concord University College, Fujian Normal University, Fuzhou
[3] School of Electronics Engineering and Computer Science, Peking University, Beijing
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 10期
关键词：
autonomous driving; computation offloading; edge computing; reinforcement learning;
D O I：
10.13196/j.cims.2022.10.025
中图分类号：
学科分类号：
摘要：
To provide a reasonable real-time scheduling scheme for vehicle tasks with dependencies so that Mobile Edge Computing (MEC) can schedule tasks to be processed in edge nodes for reducing execution time effectively, a real-time scheduling strategy for reasoning tasks in vehicle edge computing was designed. The reasoning process of applications was abstracted as a model based on directed acyclic graphs ； the execution order of tasks was defined according to the priority evaluation method, and the appropriate nodes for the corresponding tasks were chosen by Deep Q-learning (DQN). The reasoning tasks were performed according to the results of resource allocation. Experimental results showed that the proposed strategy could effectively reduce the processing delay of reasoning tasks. Compared with the classic algorithms,it had better performance in convergence and response rate. © 2022 CIMS. All rights reserved.
引用
收藏
页码：3295 / 3303
页数：8
相关论文
共 16 条
  • [11] LIN K, LIN B, CHEN X, Et al., A time-driven workflow scheduling strategy for reasoning tasks of autonomous driving in edge environment, Proceedings of 2019 IEEE International Conference on Parallel &- Distributed Processing with Applications, Big Data &- Cloud Computing, Sustainable Computing &- Communications, SocialComputing &- Networking ( ISPA/BDCloud/SocialCom/SustainCom ), pp. 124-131, (2019)
  • [12] ZHAO Haitao, ZHANG Tangwei, CHEN Yue, Et al., Task distribution and offloading algorithm for vehicle edge network based on DQN[J], Journal on Communications, 41, 10, pp. 172-178, (2020)
  • [13] XIONG X, ZHENG K, LEI L, Et al., Resource allocation based on deep reinforcement learning in iot edge computing [J], IEEE Journal on Selected Areas in Communications, 38, 6, pp. 1133-1146, (2020)
  • [14] WANG C, YU F R, LIANG C, Et al., Joint computation offloading and interference management in wireless cellular networks with mobile edge computing, IEEE Transactions on Vehicular Technology, 66, 8, pp. 7432-7445, (2017)
  • [15] LIN Bing, XIANG Tao, CHEN Guolong, Et al., Data layout strategy of scientific workflow for optimization of time delay in hybrid cloud environment, Computer Integrated Manufacturing Systems, 25, 4, pp. 909-919, (2019)
  • [16] LIU Ye, LIU Linfeng, ZHENG Long, Et al., Research on downstream traffic performance of RSU unit of internet of vehicles[J], Journal of Software, 26, 7, pp. 1700-1710, (2015)
12