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

被引：0
|
作者：
Zhou X. [1 ]
Sun S. [1 ]
Zhang H. [1 ]
Deng Y. [1 ]
Lu B. [1 ]
机构：
[1] (School of Control Science and Engineering, Shandong University, Jinan 250061, China) (Shandong Key Laboratory of Wireless Communication Technologies, Shandong University
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2024年 / 46卷 / 02期
基金：
中国国家自然科学基金;
关键词：
AI-based quality inspection system; Deep reinforcement learning; Multi-access Edge Computing (MEC); Resource allocation; Task scheduling;
D O I：
10.11999/JEIT230129
中图分类号：
学科分类号：
摘要：
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. © 2024 Science Press. All rights reserved.
引用
收藏
页码：662 / 670
页数：8
相关论文
共 22 条
  • [1] ZHOU Hua, ZHENG Rong, XIAO Rong, Research on key technology and platform architecture of AI quality inspection under industrial scene[J], Modern Information Technology, 6, 5, pp. 149-151, (2022)
  • [2] JIANG Yin, Deep learning offers a new paradigm of quality inspection supported by industrial AI, Big Data Time, 11, pp. 38-48, (2022)
  • [3] DAI Yueyue, ZHANG Ke, MAHARJAN S, Et al., Deep reinforcement learning for stochastic computation offloading in digital twin networks[J], IEEE Transactions on Industrial Informatics, 17, 7, pp. 4968-4977, (2021)
  • [4] HU Zhiyuan, HU Wenqian, LI Xiang, Et al., Research on wide area industrial internet scheduling algorithm based on service reachability[J], Journal of Electronics & Information Technology, 43, 9, pp. 2608-2616, (2021)
  • [5] BAHRAMI M., Cloud computing for emerging mobile cloud apps[C], 2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, pp. 4-5, (2015)
  • [6] ZHANG Fan, HAN Guanjie, LIU Li, Et al., Deep reinforcement learning based cooperative partial task offloading and resource allocation for IIoT applications, IEEE Transactions on Network Science and Engineering, (2022)
  • [7] Yuyi MAO, YOU Changsheng, ZHANG Jun, Et al., A survey on mobile edge computing: The communication perspective[J], IEEE Communications Surveys & Tutorials, 19, 4, pp. 2322-2358, (2017)
  • [8] LI Yiqian, LIU Liu, LI Huiting, Et al., Research on characteristics of industrial IoT wireless channel[J], Chinese Journal on Internet of Things, 3, 4, pp. 34-47, (2019)
  • [9] ZHANG Ke, LIU Liu, YUAN Ze, Et al., Wireless channel and noise characteristics in industrial internet of things[J], Telecommunications Science, 34, 8, pp. 87-97, (2018)
  • [10] GUO Kai, YANG Mingcong, ZHANG Yongbing, Et al., Joint computation offloading and bandwidth assignment in cloud-assisted edge computing[J], IEEE Transactions on Cloud Computing, 10, 1, pp. 451-460, (2022)
123