多层分布式车联网边缘计算任务动态卸载策略  

A Multi-layer Distributed Edge Computing Task Dynamic Offloading Strategy in Internet of Vehicles

作　　者：巨涛 张宇斐 马雅玲 火久元[1] JU Tao;ZHANG Yufei;MA Yaling;HUO Jiuyuan(School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学电子与信息工程学院,甘肃兰州730070

出　　处：《湖南大学学报(自然科学版)》2025年第4期79-90,共12页Journal of Hunan University:Natural Sciences

基　　金：国家自然科学基金资助项目(61862037,62262038);甘肃省科技计划项目(23CXGA0028);兰州市人才创新创业项目(2021-RC-40)。

摘　　要：针对车联网计算任务动态卸载成功率和数据传输效率低的问题,基于多智能体深度强化学习设计了多层分布式车联网边缘计算任务动态卸载策略.首先融合软件定义网络和移动边缘计算设计了多层分布式车联网边缘计算系统模型,实现在不同层次上的协同调度优化,更好地满足移动车辆资源动态分配和任务实时处理的需求;之后从车辆计算任务卸载成功率和数据卸载速率两方面考虑,提出了一种多智能体深度强化学习算法框架,利用多智能体系统的协作学习,使车载边缘系统自主选择最优任务卸载决策;同时引入动作空间搜索优化和优先经验回放机制,进一步提升动作空间的有效搜索,提高任务卸载决策的稳定性和准确性;最终在以上算法框架和优化机制的基础上,设计了多层分布式车辆任务卸载决策优化算法,保证车辆能根据当前网络状态和任务大小,以最小的任务传输时间和高效的卸载成功率完成计算任务卸载.仿真结果表明,与已有的卸载方法相比,本文所提方法在计算任务卸载成功率方面提高了5%~20%,在数据传输效率方面平均提高了17.8%.To address the challenges of low offloading success rates and inefficient data transmission in the internet of vehicles(IoV),this paper proposes a multi-layer distributed dynamic offloading strategy for edge computing tasks in IoV based on multi-agent deep reinforcement learning.Firstly,a multi-layer distributed internet of vehicles edge computing system model is designed by integrating software defined network and mobile edge computing.The system model can realize collaborative scheduling optimization at different levels,which can better meet the needs of dynamic allocation of mobile vehicle resources and real-time processing of tasks.Then,considering the success rate of offloading and data transmission rate of vehicle computing tasks,a multi-agent deep reinforcement learning algorithm framework is proposed.The algorithm framework uses collaborative learning of multi-agent systems to enable the vehicle edge system to independently select the optimal task offloading decision.At the same time,the optimization of the action space search and the priority experience replay mechanism were introduced to further improve the effective search of the action space and the stability and accuracy of the task offloading decision.Finally,based on the above algorithm framework and optimization mechanism,a multi-layer distributed vehicle task offloading decision optimization algorithm is proposed.The algorithm can ensure that the vehicle can complete the computing task offloading with the minimum task transmission time and effective offloading success rate according to the current network status and task size.Simulation results show that,compared with the existing offloading methods,the proposed method improves the success rate of computing task offloading by 5%~20%and the efficiency of data transmission by 17.8%on average.

关 键 词：车联网 移动边缘计算 任务卸载 深度强化学习 软件定义网络 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]