SM@RMFFOG: sensor mining at resource management framework of fog computing

Masoudi, S; Safi-Esfahani, F

SM@RMFFOG: sensor mining at resource management framework of fog computing

Masoudi, S Safi-Esfahani, F

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Journal of Supercomputing, 2022, 78, (17), pp. 19188-19227
Issue Date:: 2022-11-01

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Field	Value	Language
dc.contributor.author	Masoudi, S
dc.contributor.author	Safi-Esfahani, F
dc.date.accessioned	2025-01-16T02:03:23Z
dc.date.available	2025-01-16T02:03:23Z
dc.date.issued	2022-11-01
dc.identifier.citation	Journal of Supercomputing, 2022, 78, (17), pp. 19188-19227
dc.identifier.issn	0920-8542
dc.identifier.issn	1573-0484
dc.identifier.uri	http://hdl.handle.net/10453/183730
dc.description.abstract	Due to the increasing use of sensors/devices in smart cities, IoT/cloud data centers must provide adequate computing resources. Efficient resource management is of the biggest challenges in distributed computing. This research proposes a solution to use the activity log of sensors to extract their activity patterns. These patterns contribute to the resource management to predict future resource requirements and act accordingly. In this framework, called sensor mining at resource management framework, the pattern extraction can be performed by the sensor mining algorithm at cloud-only data centers (SM@RMFCLOUD) or cloud/fog servers (SM@RMFFOG). Experiments apply both CityPulse and ideal datasets to evaluate the presented frameworks. The sensor mining in both cloud/fog and cloud-only frameworks improves throughput, response time, and execution delay without increasing costs, energy, and bandwidth consumption compared to the frameworks with no sensor mining.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Journal of Supercomputing
dc.relation.isbasedon	10.1007/s11227-022-04592-3
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing
dc.subject.classification	Distributed Computing
dc.subject.classification	4606 Distributed computing and systems software
dc.title	SM@RMFFOG: sensor mining at resource management framework of fog computing
dc.type	Journal Article
utslib.citation.volume	78
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
pubs.organisational-group	University of Technology Sydney
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 Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2025-01-16T02:03:21Z
pubs.issue	17
pubs.publication-status	Published
pubs.volume	78
utslib.citation.issue	17

Abstract:

Due to the increasing use of sensors/devices in smart cities, IoT/cloud data centers must provide adequate computing resources. Efficient resource management is of the biggest challenges in distributed computing. This research proposes a solution to use the activity log of sensors to extract their activity patterns. These patterns contribute to the resource management to predict future resource requirements and act accordingly. In this framework, called sensor mining at resource management framework, the pattern extraction can be performed by the sensor mining algorithm at cloud-only data centers (SM@RMFCLOUD) or cloud/fog servers (SM@RMFFOG). Experiments apply both CityPulse and ideal datasets to evaluate the presented frameworks. The sensor mining in both cloud/fog and cloud-only frameworks improves throughput, response time, and execution delay without increasing costs, energy, and bandwidth consumption compared to the frameworks with no sensor mining.

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http://hdl.handle.net/10453/183730