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: Contribution to journalJournal articlepeer-review

179 Citations (Scopus)


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
Issue number7
Publication statusPublished - Jul 2019

Scopus Subject Areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

User-Defined Keywords

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


Dive into the research topics of 'A Hybrid Computing Solution and Resource Scheduling Strategy for Edge Computing in Smart Manufacturing'. Together they form a unique fingerprint.

Cite this