CeCO: Cost-efficient Computation Offloading of IoT Applications in Green Industrial Fog Networks

Fog computing is one of the promising technology that could reduce the execution cost and energy consumption of smart IIoT devices via offloading. However, designing an intelligent offloading strategy for large-scale industrial applications becomes challenging. To address this issue, we design a nov...

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Veröffentlicht in:IEEE transactions on industrial informatics Jg. 18; H. 9; S. 1
Hauptverfasser: Hazra, Abhishek, Amgoth, Tarachand
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Cloud computing
Computation offloading
Computational efficiency
Computational modeling
Controllers
cost optimization
Costs
Design optimization
Electronic devices
Energy consumption
energy management
fog controller
Frequency control
Green products
Industrial applications
Industrial Internet of Things
Industrial IoT
Informatics
Internet of Things
Q-learning
Servers
Shortest-path problems
Task analysis
ISSN:1551-3203, 1941-0050
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Zusammenfassung:Fog computing is one of the promising technology that could reduce the execution cost and energy consumption of smart IIoT devices via offloading. However, designing an intelligent offloading strategy for large-scale industrial applications becomes challenging. To address this issue, we design a novel fog federation, a computation offloading framework for industrial networks called CeCO, where a master fog controller regulates the network and distributes the IIoT data among the fog devices. In particular, we design cost optimization function as the sum of weighted energy-delay cost of devices while reaching several constraints. To determine this optimization problem, we first design a frequency control mechanism for IIoT devices. Then we introduce a controller-based device adaptation strategy and a policy-based Reinforcement Learning technique for efficiently controlling emergency-based service demands and accordingly route to devices following the shortest path. Experimental results demonstrate the effectiveness of the CeCO strategy as compared to the baseline algorithms.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2021.3130255