Frequency-Reconfigurable Cloud Versus Fog Computing: An Energy-Efficiency Aspect

Hou, S; Ni, W; Zhao, S; Cheng, B; Chen, S; Chen, J

Frequency-Reconfigurable Cloud Versus Fog Computing: An Energy-Efficiency Aspect

Hou, S Ni, W

Zhao, S Cheng, B Chen, S Chen, J

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Green Communications and Networking, 2020, 4, (1), pp. 221-235
Issue Date:: 2020-03-01

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Field	Value	Language
dc.contributor.author	Hou, S
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Zhao, S
dc.contributor.author	Cheng, B
dc.contributor.author	Chen, S
dc.contributor.author	Chen, J
dc.date.accessioned	2021-01-08T00:51:01Z
dc.date.available	2021-01-08T00:51:01Z
dc.date.issued	2020-03-01
dc.identifier.citation	IEEE Transactions on Green Communications and Networking, 2020, 4, (1), pp. 221-235
dc.identifier.issn	2473-2400
dc.identifier.issn	2473-2400
dc.identifier.uri	http://hdl.handle.net/10453/145208
dc.description.abstract	© 2017 IEEE. Cloud and fog computing are two emerging Internet-based collaborative technologies for big data analytics. An interesting question arising is whether the two technologies can resonate with significant gains of energy efficiency, especially in the case where advanced cloud platforms with Dynamic Voltage and Frequency Scaling (DVFS) are considered. This paper answers the question by formulating the optimization of a cloud with and without the assistance of fog, and deriving asymptotically optimal distributed solutions for the two cases. We also identify the critical condition under which fog computing helps the cloud to reduce the time-averaged queue lengths. The condition depends on the configurations of the fog, and the configurations of the connections between the fog and cloud. Extensive simulations exhibit good consistency with our analysis of the conditional benefits of fog computing. Evident from experimental datasets, the proposed fog-assisted cloud platform is able to increase the time-averaged energy efficiency by about 32.2%, and decrease the time-averaged queue length by around 37.0%, compared to a fog-coordinated counterpart where fog nodes only dispatch data and do not process the data.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Green Communications and Networking
dc.relation.isbasedon	10.1109/TGCN.2019.2953891
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Frequency-Reconfigurable Cloud Versus Fog Computing: An Energy-Efficiency Aspect
dc.type	Journal Article
utslib.citation.volume	4
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-01-08T00:50:47Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	4
utslib.citation.issue	1

Abstract:

© 2017 IEEE. Cloud and fog computing are two emerging Internet-based collaborative technologies for big data analytics. An interesting question arising is whether the two technologies can resonate with significant gains of energy efficiency, especially in the case where advanced cloud platforms with Dynamic Voltage and Frequency Scaling (DVFS) are considered. This paper answers the question by formulating the optimization of a cloud with and without the assistance of fog, and deriving asymptotically optimal distributed solutions for the two cases. We also identify the critical condition under which fog computing helps the cloud to reduce the time-averaged queue lengths. The condition depends on the configurations of the fog, and the configurations of the connections between the fog and cloud. Extensive simulations exhibit good consistency with our analysis of the conditional benefits of fog computing. Evident from experimental datasets, the proposed fog-assisted cloud platform is able to increase the time-averaged energy efficiency by about 32.2%, and decrease the time-averaged queue length by around 37.0%, compared to a fog-coordinated counterpart where fog nodes only dispatch data and do not process the data.

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http://hdl.handle.net/10453/145208