Service Popularity-Based Smart Resources Partitioning for Fog Computing-Enabled Industrial Internet of Things

Gaolei Li*, Jun Wu, Jianhua Li, Kuan Wang, Tianpeng Ye

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

129 Citations (Scopus)


Recently, fog computing has gained increasing attention in processing the computing tasks of the industrial Internet of things (IIoT) with different service popularity. In task-diversified fog computing-enabled IIoT (F-IIoT), the mismatch between expected computing efficiency and partitioned resources on fog nodes (FNs) may pose serious traffic congestion even large-scale industrial service interruptions. The existing works mainly studied offloading which type of computing tasks into FNs, but few studies enabled smart resource partitioning of FNs. In this paper, a service popularity-based smart resources partitioning (SPSRP) scheme is proposed for fog computing-enabled IIoT. We first exploit Zipf's law to model the relationship between popularity ranks and computing costs of IIoT services. Moreover, we propose an implementation architecture of the SPSRP scheme for F-IIoT, which decouples the computing control layer from data processing layer of IIoT through a specified SPSRP controller. Besides, a mobility and heterogeneity-Aware partitioning algorithm is presented for extending SPSRP scheme to seamlessly support cross-domain resources partitioning. The simulations demonstrate that the SPSRP scheme can bring notable performance improvements on delay time, successful response rate and fault tolerance for fog computing to deal with the large-scale IIoT services.

Original languageEnglish
Article number8377998
Pages (from-to)4702-4711
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Issue number10
Publication statusPublished - 2018 Oct
Externally publishedYes


  • Fog computing
  • Industrial Internet of Thing (IIoT)
  • resources partitioning
  • service popularity
  • Zipf's law

ASJC Scopus subject areas

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


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