AdaptiveFog: A Modelling and Optimization Framework for Fog Computing in Intelligent Transportation Systems

Xiao, Y; Krunz, M

AdaptiveFog: A Modelling and Optimization Framework for Fog Computing in Intelligent Transportation Systems

Xiao, Y Krunz, M

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Mobile Computing, 2022, 21, (12), pp. 4187-4200
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Xiao, Y
dc.contributor.author	Krunz, M
dc.date.accessioned	2023-03-20T00:25:40Z
dc.date.available	2023-03-20T00:25:40Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Mobile Computing, 2022, 21, (12), pp. 4187-4200
dc.identifier.issn	1536-1233
dc.identifier.issn	1558-0660
dc.identifier.uri	http://hdl.handle.net/10453/167656
dc.description.abstract	Fog computing has been advocated as an enabling technology for computationally intensive services in connected smart vehicles. Most existing works focus on analyzing and optimizing queueing and workload processing latencies, ignoring the fact that the access latency between vehicles and fog/cloud servers can sometimes dominate the overall latency. This motivates the work in this paper, where we report on a five-month urban measurement study of the wireless access latency between connected vehicles and fog computing system supported by commercially available LTE networks. We propose AdaptiveFog, a novel framework for autonomous and dynamic switching between different LTE networks that implement fog/cloud infrastructure. The main objective here is to maximize the service confidence level, defined as the probability that a tolerable latency threshold for each supported type of service can be guaranteed. To quantify the performance gap between different LTE networks, we introduce a novel statistical distance metric, called weighted Kantorovich-Rubinstein (K-R) distance. Two practical scenarios with finite- and infinite-horizon decision making processes for optimizing the short-term and long-term confidence are investigated. Extensive analysis and simulations are performed based on our latency measurements. Our results show that AdaptiveFog achieves around 30% to 50% improvement in the confidence level of fog/cloud latency.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Transactions on Mobile Computing
dc.relation.isbasedon	10.1109/TMC.2021.3080397
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	AdaptiveFog: A Modelling and Optimization Framework for Fog Computing in Intelligent Transportation Systems
dc.type	Journal Article
utslib.citation.volume	21
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
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 Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-20T00:25:38Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	21
utslib.citation.issue	12

Abstract:

Fog computing has been advocated as an enabling technology for computationally intensive services in connected smart vehicles. Most existing works focus on analyzing and optimizing queueing and workload processing latencies, ignoring the fact that the access latency between vehicles and fog/cloud servers can sometimes dominate the overall latency. This motivates the work in this paper, where we report on a five-month urban measurement study of the wireless access latency between connected vehicles and fog computing system supported by commercially available LTE networks. We propose AdaptiveFog, a novel framework for autonomous and dynamic switching between different LTE networks that implement fog/cloud infrastructure. The main objective here is to maximize the service confidence level, defined as the probability that a tolerable latency threshold for each supported type of service can be guaranteed. To quantify the performance gap between different LTE networks, we introduce a novel statistical distance metric, called weighted Kantorovich-Rubinstein (K-R) distance. Two practical scenarios with finite- and infinite-horizon decision making processes for optimizing the short-term and long-term confidence are investigated. Extensive analysis and simulations are performed based on our latency measurements. Our results show that AdaptiveFog achieves around 30% to 50% improvement in the confidence level of fog/cloud latency.

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http://hdl.handle.net/10453/167656