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

Xiaomin LI, Jiafu WAN*, Hong Ning DAI, Muhammad IMRAN, Min XIA, Antonio CELESTI

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

60 Citations (Scopus)

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.

Original languageEnglish
Article number8643392
Pages (from-to)4225-4234
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Volume15
Issue number7
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by the National Key Research and Development Project of China under Grant 2017YFE0101000, in part by the Joint Fund of the National Natural Science Foundation of China and Guangdong Province under Grant U1801264, in part by the Key Program of Natural Science Foundation of Guangdong Province under Grant 2017B030311008, and in part by the Science and Technology Programof Guangzhou, China, under Grant 201802030005. The work of M. Imran was supported by the Deanship of Scientific Research, King Saud University through research group number RG-1435-051. Paper no. TII-18-3244.R1.

Funding Information:
Manuscript received December 1, 2018; revised January 23, 2019; accepted February 11, 2019. Date of publication February 18, 2019; date of current version July 3, 2019. This work was supported in part by the National Key Research and Development Project of China under Grant 2017YFE0101000, in part by the Joint Fund of the National Natural Science Foundation of China and Guangdong Province under Grant U1801264, in part by the Key Program of Natural Science Foundation of Guangdong Province under Grant 2017B030311008, and in part by the Science and Technology Program of Guangzhou, China, under Grant 201802030005. The work of M. Imran was supported by the Deanship of Scientific Research, King Saud University through research group number RG-1435-051. Paper no. TII-18-3244.R1. (Corresponding author: Jiafu Wan.) X. Li is with the School of Mechanical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China (e-mail:, lixiaomin@zhku.edu.cn).

Publisher Copyright:
© 2005-2012 IEEE.

Keywords

  • Edge computing
  • industry 4.0
  • resource scheduling
  • smart manufacturing

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