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

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A Hybrid Computing Solution and Resource Scheduling Strategy for Edge Computing in Smart Manufacturing. / Li, Xiaomin; Wan, Jiafu; Dai, Hong Ning et al.
In: IEEE Transactions on Industrial Informatics, Vol. 15, No. 7, 8643392, 01.07.2019, p. 4225-4234.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Li, X, Wan, J, Dai, HN, Imran, M, Xia, M & Celesti, A 2019, 'A Hybrid Computing Solution and Resource Scheduling Strategy for Edge Computing in Smart Manufacturing', IEEE Transactions on Industrial Informatics, vol. 15, no. 7, 8643392, pp. 4225-4234. https://doi.org/10.1109/TII.2019.2899679

APA

Li, X., Wan, J., Dai, H. N., Imran, M., Xia, M., & Celesti, A. (2019). A Hybrid Computing Solution and Resource Scheduling Strategy for Edge Computing in Smart Manufacturing. IEEE Transactions on Industrial Informatics, 15(7), 4225-4234. Article 8643392. https://doi.org/10.1109/TII.2019.2899679

Vancouver

Li X, Wan J, Dai HN, Imran M, Xia M, Celesti A. A Hybrid Computing Solution and Resource Scheduling Strategy for Edge Computing in Smart Manufacturing. IEEE Transactions on Industrial Informatics. 2019 Jul 1;15(7):4225-4234. 8643392. Epub 2019 Feb 18. doi: 10.1109/TII.2019.2899679

Author

Li, Xiaomin ; Wan, Jiafu ; Dai, Hong Ning et al. / A Hybrid Computing Solution and Resource Scheduling Strategy for Edge Computing in Smart Manufacturing. In: IEEE Transactions on Industrial Informatics. 2019 ; Vol. 15, No. 7. pp. 4225-4234.

Bibtex

@article{e60cbf9c24cc4199952ab2975f2fcf89,
title = "A Hybrid Computing Solution and Resource Scheduling Strategy for Edge Computing in Smart Manufacturing",
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.",
keywords = "Edge computing, industry 4.0, resource scheduling, smart manufacturing",
author = "Xiaomin Li and Jiafu Wan and Dai, {Hong Ning} and Muhammad Imran and Min Xia and Antonio Celesti",
year = "2019",
month = jul,
day = "1",
doi = "10.1109/TII.2019.2899679",
language = "English",
volume = "15",
pages = "4225--4234",
journal = "IEEE Transactions on Industrial Informatics",
issn = "1551-3203",
publisher = "IEEE Computer Society",
number = "7",

}

RIS

TY - JOUR

T1 - A Hybrid Computing Solution and Resource Scheduling Strategy for Edge Computing in Smart Manufacturing

AU - Li, Xiaomin

AU - Wan, Jiafu

AU - Dai, Hong Ning

AU - Imran, Muhammad

AU - Xia, Min

AU - Celesti, Antonio

PY - 2019/7/1

Y1 - 2019/7/1

N2 - 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.

AB - 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.

KW - Edge computing

KW - industry 4.0

KW - resource scheduling

KW - smart manufacturing

U2 - 10.1109/TII.2019.2899679

DO - 10.1109/TII.2019.2899679

M3 - Journal article

AN - SCOPUS:85063689140

VL - 15

SP - 4225

EP - 4234

JO - IEEE Transactions on Industrial Informatics

JF - IEEE Transactions on Industrial Informatics

SN - 1551-3203

IS - 7

M1 - 8643392

ER -