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

ZHOU Xiaotian; SUN Shang; ZHANG Haixia; DENG Yiqin; LU Binbin

doi:10.11999/JEIT230129

Volume 46 Issue 2

Feb. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(2): 662-670

ZHOU Xiaotian, SUN Shang, ZHANG Haixia, DENG Yiqin, LU Binbin. Real-time Task Scheduling for Multi-access Edge Computing-enabled AI Quality Inspection Systems[J]. Journal of Electronics & Information Technology, 2024, 46(2): 662-670. doi: 10.11999/JEIT230129

Citation:

ZHOU Xiaotian, SUN Shang, ZHANG Haixia, DENG Yiqin, LU Binbin. Real-time Task Scheduling for Multi-access Edge Computing-enabled AI Quality Inspection Systems[J]. Journal of Electronics & Information Technology, 2024, 46(2): 662-670. doi: 10.11999/JEIT230129

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

doi: 10.11999/JEIT230129

1.
School of Control Science and Engineering, Shandong University, Jinan 250061, China
2.
Shandong Key Laboratory of Wireless Communication Technologies, Shandong University, Jinan 250100, China

Funds: The National Natural Science Foundation of China (61860206005, U22A2003, 61971270)

Received Date: 2023-03-03
Rev Recd Date: 2023-08-15

Available Online: 2023-08-17

Publish Date: 2024-02-29

Abstract

Abstract

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.
- Multi-access Edge Computing (MEC),
- Task scheduling,
- Resource allocation,
- Deep reinforcement learning,
- AI-based quality inspection system

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References(22)

References

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