北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2023, Vol. 46 ›› Issue (2): 15-21.

• 算力网络与分布式云 • 上一篇    下一篇

基于SMDP模型的车路协同任务智能卸载算法研究

李晓辉,苏家楠,吕思婷,张鹏   

  1. 西安电子科技大学
  • 收稿日期:2022-03-23 修回日期:2022-07-10 出版日期:2023-04-28 发布日期:2023-05-14
  • 通讯作者: 苏家楠 E-mail:20181214253@stu.xidian.edu.cn

Research on Intelligent Unloading Algorithm for Road Collaborative Tasks Based on SMDP Model

  • Received:2022-03-23 Revised:2022-07-10 Online:2023-04-28 Published:2023-05-14
  • Contact: 家楠 苏 E-mail:20181214253@stu.xidian.edu.cn

摘要: 针对车路协同系统中车辆的高机动性产生边缘节点难以控制计算时延的问题,提出基于半马尔科夫过程的任务卸载策略。定义了道路服务节点的优先级队列,状态空间,行为空间,系统收益和转移概率来建模任务等待队列,在服务节点覆盖范围内通过增加车载任务完成率提高整体收益。而后使用贝尔曼方程进行迭代,使得状态空间达到系统最优。数值表明,所提出的任务卸载策略可有效提高车路协同系统的整体收益。

关键词: 边缘计算, 车路协同, 任务卸载, 半马尔可夫决策过程

Abstract: Aiming at the problem that the high mobility of vehicles in the vehicle-road coordination system makes it difficult for the edge computing node to control the delay, a task unloading strategy based on the semi-Markov (SMDP) process is proposed. The state space, behavior space, system yield and transfer probability of the road service node are defined to model the task wait queue, and the overall benefit is improved by increasing the vehicle task completion rate within the coverage of the service node, and the Bellman equation is used to iterate to make the state space reach the optimal system. The numerical value shows that the task unloading decision of the algorithm can effectively improve the overall benefit of the vehicle-road collaboration system.

Key words: Edge computing, vehicle-road cooperation, task unloading,, semi-Markov decision process

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