Dependency-aware online task offloading based on deep reinforcement learning for IoV

Chunhong Liu, Huaichen Wang, Mengdi Zhao, Jialei Liu, Xiaoyan Zhao, Peiyan Yuan
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Abstract

The convergence of artificial intelligence and in-vehicle wireless communication technologies, promises to fulfill the pressing communication needs of the Internet of Vehicles (IoV) while promoting the development of vehicle applications. However, making real-time dependency-aware task offloading decisions is difficult due to the high mobility of vehicles and the dynamic nature of the network environment. This leads to additional application computation time and energy consumption, increasing the risk of offloading failures for computationally intensive and latency-sensitive applications. In this paper, an offloading strategy for vehicle applications that jointly considers latency and energy consumption in the base station cooperative computing model is proposed. Firstly, we establish a collaborative offloading model involving multiple vehicles, multiple base stations, and multiple edge servers. Transferring vehicular applications to the application queue of edge servers and prioritizing them based on their completion deadlines. Secondly, each vehicular application is modeled as a directed acyclic graph (DAG) task with data dependency relationships. Subsequently, we propose a task offloading method based on task dependency awareness in deep reinforcement learning (DAG-DQN). Tasks are assigned to edge servers at different base stations, and edge servers collaborate to process tasks, minimizing vehicle application completion time and reducing edge server energy consumption. Finally, simulation results show that compared with the heuristic method, our proposed DAG-DQN method reduces task completion time by 16%, reduces system energy consumption by 19%, and improves decision-making efficiency by 70%.
基于深度强化学习的物联网车依赖感知在线任务卸载
人工智能与车载无线通信技术的融合有望满足车联网(IoV)的迫切通信需求,同时促进车辆应用的发展。然而,由于车辆的高流动性和网络环境的动态性质,很难做出实时依赖感知任务卸载决策。这会导致额外的应用计算时间和能耗,增加计算密集型和延迟敏感型应用卸载失败的风险。本文提出了一种在基站协同计算模型中联合考虑延迟和能耗的车辆应用卸载策略。首先,我们建立了一个涉及多个车辆、多个基站和多个边缘服务器的协作卸载模型。将车辆应用转移到边缘服务器的应用队列中,并根据其完成期限确定优先级。其次,将每个车辆应用建模为具有数据依赖关系的有向无环图(DAG)任务。随后,我们在深度强化学习(DAG-DQN)中提出了一种基于任务依赖意识的任务卸载方法。将任务分配给不同基站的边缘服务器,边缘服务器协同处理任务,从而最大限度地缩短车辆应用的完成时间,降低边缘服务器的能耗。最后,仿真结果表明,与启发式方法相比,我们提出的 DAG-DQN 方法缩短了 16% 的任务完成时间,降低了 19% 的系统能耗,并提高了 70% 的决策效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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