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.
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页码：3295 / 3303
页数：8
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