Adaptive Fog Configuration for the Industrial Internet of Things

Lixing Chen, Pan Zhou, Liang Gao, Jie Xu

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Industrial fog computing deploys various industrial services, such as automatic monitoring/control and imminent failure detection, at the fog nodes (FNs) to improve the performance of industrial systems. Much effort has been made in the literature on the design of fog network architecture and computation offloading. This paper studies an equally important but much less investigated problem of service hosting where FNs are adaptively configured to host services for sensor nodes (SNs), thereby enabling corresponding tasks to be executed by the FNs. The problem of service hosting emerges because of the limited computational and storage resources at FNs, which limit the number of different types of services that can be hosted by an FN at the same time. Considering the variability of service demand in both temporal and spatial dimensions, when, where, and which services to host have to be judiciously decided to maximize the utility of the fog computing network. Our proposed fog configuration strategies are tailored to battery-powered FNs. The limited battery capacity of FNs creates a long-term energy budget constraint that significantly complicates the fog configuration problem as it introduces temporal coupling of decision making across the timeline. To address all these challenges, we propose an online distributed algorithm, called adaptive fog configuration (AFC), based on Lyapunov optimization and parallel Gibbs sampling. AFC jointly optimizes service hosting and task admission decisions, requiring only currently available system information while guaranteeing close-to-optimal performance compared to an oracle algorithm with full future information.

Original languageEnglish (US)
Article number8382290
Pages (from-to)4656-4664
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Volume14
Issue number10
DOIs
StatePublished - Oct 1 2018

Fingerprint

Fog
Internet of things
Network architecture
Sensor nodes
Parallel algorithms
Information systems
Decision making

Keywords

  • Distributed algorithms
  • energy efficiency
  • fog computing
  • industrial internet of things (IIoT)

ASJC Scopus subject areas

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

Cite this

Adaptive Fog Configuration for the Industrial Internet of Things. / Chen, Lixing; Zhou, Pan; Gao, Liang; Xu, Jie.

In: IEEE Transactions on Industrial Informatics, Vol. 14, No. 10, 8382290, 01.10.2018, p. 4656-4664.

Research output: Contribution to journalArticle

Chen, Lixing ; Zhou, Pan ; Gao, Liang ; Xu, Jie. / Adaptive Fog Configuration for the Industrial Internet of Things. In: IEEE Transactions on Industrial Informatics. 2018 ; Vol. 14, No. 10. pp. 4656-4664.
@article{1d86be65356a4f24b4c829d8085d2275,
title = "Adaptive Fog Configuration for the Industrial Internet of Things",
abstract = "Industrial fog computing deploys various industrial services, such as automatic monitoring/control and imminent failure detection, at the fog nodes (FNs) to improve the performance of industrial systems. Much effort has been made in the literature on the design of fog network architecture and computation offloading. This paper studies an equally important but much less investigated problem of service hosting where FNs are adaptively configured to host services for sensor nodes (SNs), thereby enabling corresponding tasks to be executed by the FNs. The problem of service hosting emerges because of the limited computational and storage resources at FNs, which limit the number of different types of services that can be hosted by an FN at the same time. Considering the variability of service demand in both temporal and spatial dimensions, when, where, and which services to host have to be judiciously decided to maximize the utility of the fog computing network. Our proposed fog configuration strategies are tailored to battery-powered FNs. The limited battery capacity of FNs creates a long-term energy budget constraint that significantly complicates the fog configuration problem as it introduces temporal coupling of decision making across the timeline. To address all these challenges, we propose an online distributed algorithm, called adaptive fog configuration (AFC), based on Lyapunov optimization and parallel Gibbs sampling. AFC jointly optimizes service hosting and task admission decisions, requiring only currently available system information while guaranteeing close-to-optimal performance compared to an oracle algorithm with full future information.",
keywords = "Distributed algorithms, energy efficiency, fog computing, industrial internet of things (IIoT)",
author = "Lixing Chen and Pan Zhou and Liang Gao and Jie Xu",
year = "2018",
month = "10",
day = "1",
doi = "10.1109/TII.2018.2846549",
language = "English (US)",
volume = "14",
pages = "4656--4664",
journal = "IEEE Transactions on Industrial Informatics",
issn = "1551-3203",
publisher = "IEEE Computer Society",
number = "10",

}

TY - JOUR

T1 - Adaptive Fog Configuration for the Industrial Internet of Things

AU - Chen, Lixing

AU - Zhou, Pan

AU - Gao, Liang

AU - Xu, Jie

PY - 2018/10/1

Y1 - 2018/10/1

N2 - Industrial fog computing deploys various industrial services, such as automatic monitoring/control and imminent failure detection, at the fog nodes (FNs) to improve the performance of industrial systems. Much effort has been made in the literature on the design of fog network architecture and computation offloading. This paper studies an equally important but much less investigated problem of service hosting where FNs are adaptively configured to host services for sensor nodes (SNs), thereby enabling corresponding tasks to be executed by the FNs. The problem of service hosting emerges because of the limited computational and storage resources at FNs, which limit the number of different types of services that can be hosted by an FN at the same time. Considering the variability of service demand in both temporal and spatial dimensions, when, where, and which services to host have to be judiciously decided to maximize the utility of the fog computing network. Our proposed fog configuration strategies are tailored to battery-powered FNs. The limited battery capacity of FNs creates a long-term energy budget constraint that significantly complicates the fog configuration problem as it introduces temporal coupling of decision making across the timeline. To address all these challenges, we propose an online distributed algorithm, called adaptive fog configuration (AFC), based on Lyapunov optimization and parallel Gibbs sampling. AFC jointly optimizes service hosting and task admission decisions, requiring only currently available system information while guaranteeing close-to-optimal performance compared to an oracle algorithm with full future information.

AB - Industrial fog computing deploys various industrial services, such as automatic monitoring/control and imminent failure detection, at the fog nodes (FNs) to improve the performance of industrial systems. Much effort has been made in the literature on the design of fog network architecture and computation offloading. This paper studies an equally important but much less investigated problem of service hosting where FNs are adaptively configured to host services for sensor nodes (SNs), thereby enabling corresponding tasks to be executed by the FNs. The problem of service hosting emerges because of the limited computational and storage resources at FNs, which limit the number of different types of services that can be hosted by an FN at the same time. Considering the variability of service demand in both temporal and spatial dimensions, when, where, and which services to host have to be judiciously decided to maximize the utility of the fog computing network. Our proposed fog configuration strategies are tailored to battery-powered FNs. The limited battery capacity of FNs creates a long-term energy budget constraint that significantly complicates the fog configuration problem as it introduces temporal coupling of decision making across the timeline. To address all these challenges, we propose an online distributed algorithm, called adaptive fog configuration (AFC), based on Lyapunov optimization and parallel Gibbs sampling. AFC jointly optimizes service hosting and task admission decisions, requiring only currently available system information while guaranteeing close-to-optimal performance compared to an oracle algorithm with full future information.

KW - Distributed algorithms

KW - energy efficiency

KW - fog computing

KW - industrial internet of things (IIoT)

UR - http://www.scopus.com/inward/record.url?scp=85048540938&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85048540938&partnerID=8YFLogxK

U2 - 10.1109/TII.2018.2846549

DO - 10.1109/TII.2018.2846549

M3 - Article

AN - SCOPUS:85048540938

VL - 14

SP - 4656

EP - 4664

JO - IEEE Transactions on Industrial Informatics

JF - IEEE Transactions on Industrial Informatics

SN - 1551-3203

IS - 10

M1 - 8382290

ER -