A Hybrid Computing Solution and Resource Scheduling Strategy for Edge Computing in Smart Manufacturing

Li, Xiaomin and Wan, Jiafu and Dai, Hong Ning and Imran, Muhammad and Xia, Min and Celesti, Antonio (2019) A Hybrid Computing Solution and Resource Scheduling Strategy for Edge Computing in Smart Manufacturing. IEEE Transactions on Industrial Informatics, 15 (7): 8643392. pp. 4225-4234. ISSN 1551-3203

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Abstract

At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Industrial Informatics
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2207
Subjects:
?? edge computingindustry 4.0resource schedulingsmart manufacturingcontrol and systems engineeringinformation systemscomputer science applicationselectrical and electronic engineering ??
ID Code:
138933
Deposited By:
Deposited On:
15 Nov 2019 10:15
Refereed?:
Yes
Published?:
Published
Last Modified:
15 Jul 2024 20:07