多接入边缘计算赋能的AI质检系统任务实时调度策略  

Real-time Task Scheduling for Multi-access Edge Computing-enabled AI Quality Inspection Systems

作　　者：周晓天 孙上 张海霞 邓伊琴 鲁彬彬 ZHOU Xiaotian;SUN Shang;ZHANG Haixia;DENG Yiqin;LU Binbin(School of Control Science and Engineering,Shandong University,Jinan 250061,China;Shandong Key Laboratory of Wireless Communication Technologies,Shandong University,Jinan 250100,China)

机构地区：[1]山东大学控制科学与工程学院,济南250061 [2]山东省无线通信技术重点实验室,济南250100

出　　处：《电子与信息学报》2024年第2期662-670,共9页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61860206005,U22A2003,61971270)。

摘　　要：AI质检是智能制造的重要环节,其设备在进行产品质量检测时会产生大量计算密集型和时延敏感型任务。由于设备计算能力不足,执行检测任务时延较大,极大影响生产效率。多接入边缘计算(MEC)通过将任务卸载至边缘服务器为设备提供就近算力,提升任务执行效率。然而,系统中存在信道变化和任务随机到达等动态因素,极大影响卸载效率,给任务调度带来了挑战。该文面向多接入边缘计算赋能的AI质检任务调度系统,研究了联合任务调度与资源分配的长期时延最小化问题。由于该问题状态空间大、动作空间包含连续变量,该文提出运用深度确定性策略梯度(DDPG)进行实时任务调度算法设计。所设计算法可基于系统实时状态信息给出最优决策。仿真结果表明,与基准算法相比,该文所提算法具有更好的性能表现和更小的任务执行时延。AI-based quality inspection is an important part of intelligent manufacturing,where the devices produce a large amount of computation-intensive and time-sensitive tasks.Owing to the insufficient computation capability of end devices,the latency to execute these inspection tasks is large,which greatly affects manufacturing efficiency.To this end,Multi-access Edge Computing(MEC)is proposed to provide computation resources through offloading tasks to the edge servers deployed nearby.The execution efficiency is therefore improved.However,the dynamic channel state and random task arrival greatly impact the task offloading efficiency and consequently bring challenges to task scheduling.In this paper,the joint task scheduling and resource allocation problem with the purpose of minimizing the long-term delay of MEC-enabled system is studied.As the state space of the problem is large and the action space contains continuous variables,a Deep Deterministic Policy Gradient(DDPG)based real-time task scheduling algorithm is proposed.The proposed algorithm can make optimal decision with real-time system state information.Simulation results confirm the promising performance of the proposed algorithm,which achieves lower task execution latency than that of the benchmark algorithm.

关 键 词：多接入边缘计算 任务调度 资源分配 深度强化学习 AI质检系统 

分 类 号：TN929.5[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]