A Resource Recommendation Model for Heterogeneous Workloads in Fog-Based Smart Factory Environment

Lulu Chen, Zhihui Lu, Ai Xiao, Qiang Duan, Jie Wu, Patrick C.K. Hung

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The wide deployment of advanced robots with industrial IoT (IIoT) technologies in smart factories generates a large volume of data during production and a wide variety of data processing workloads are launched to maintain productivity and safety of smart manufacture. The emerging fog computing paradigm offers a promising solution to enhancing data processing performance in a smart factory environment while on the other hand brings in new challenges to resource management, which call for a more effective approach for recommending resource configurations to heterogeneous workloads. In this paper, we propose an Optimized Recommendations of Heterogeneous Resource Configurations (ORHRC) model that employs machine learning techniques to provide resource configuration recommendations for the heterogeneous workloads in a fog computing-based smart factory environment. ORHRC learns a recommendation model by leveraging the operating characteristics and execution time of workloads on fog servers with different configurations. We also design a decision model in ORHRC to further improve prediction accuracy and reduce operational overheads. Experiment results show that ORHRC outperforms the state of art configuration recommendation methods in terms of average prediction accuracy.

Original languageEnglish (US)
Pages (from-to)1731-1743
Number of pages13
JournalIEEE Transactions on Automation Science and Engineering
Volume19
Issue number3
DOIs
StatePublished - Jul 1 2022

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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