Computation Placement Orchestrator for Mobile-Edge Computing in Heterogeneous Vehicular Networks

Wang, L; Deng, X; Gui, J; Zhang, H; Yu, S

Computation Placement Orchestrator for Mobile-Edge Computing in Heterogeneous Vehicular Networks

Wang, L Deng, X Gui, J Zhang, H Yu, S

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2023, 10, (24), pp. 22686-22702
Issue Date:: 2023-12-15

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Field	Value	Language
dc.contributor.author	Wang, L
dc.contributor.author	Deng, X
dc.contributor.author	Gui, J
dc.contributor.author	Zhang, H
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2024-03-12T00:55:12Z
dc.date.available	2024-03-12T00:55:12Z
dc.date.issued	2023-12-15
dc.identifier.citation	IEEE Internet of Things Journal, 2023, 10, (24), pp. 22686-22702
dc.identifier.issn	2372-2541
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/176519
dc.description.abstract	The vision of heterogeneous vehicle networks (HetVNETs) embraces various highly dynamic scenarios with urgent requirements for delay-sensitive and reliability-guaranteed computation placement. Incorporating mobile-edge computing (MEC) technology into computation placement has a significant potential to reduce computational delay and enhance communication reliability. However, vehicle mobility and resource constraints make the multivehicle scramble for communication and computational resources challenging. This article intends to investigate collaborative computing by comprehensively considering vehicle mobility, channel condition, and computational resources with two goals: 1) high-reliability transmission (HRT) and 2) computational delay minimization (CDM). Specifically, we develop a hybrid MEC-enabled computation placement orchestrator for HetVNET, where the HRT and CDM are formulated as mixed-integer programming and nonconvex optimization problems, respectively. To ensure high-reliability communication, we leverage the conditional value at risk theory to tackle the nonsmooth HRT problem. To solve the CDM problem, we transform it into two subproblems: resource allocation and task offloading problems, aiming at reducing computational delay and improving resource utilization. Furthermore, we construct an iterative optimization algorithm to capture the optimal computation placement scheme in closed form for the HRT and CDM problems. Performance evaluations show that the proposed methods can significantly improve communication reliability and reduce computational delay.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2023.3304304
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Computation Placement Orchestrator for Mobile-Edge Computing in Heterogeneous Vehicular Networks
dc.type	Journal Article
utslib.citation.volume	10
utslib.for	0805 Distributed Computing
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 Computer Science
pubs.organisational-group	University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-12T00:55:11Z
pubs.issue	24
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	24

Abstract:

The vision of heterogeneous vehicle networks (HetVNETs) embraces various highly dynamic scenarios with urgent requirements for delay-sensitive and reliability-guaranteed computation placement. Incorporating mobile-edge computing (MEC) technology into computation placement has a significant potential to reduce computational delay and enhance communication reliability. However, vehicle mobility and resource constraints make the multivehicle scramble for communication and computational resources challenging. This article intends to investigate collaborative computing by comprehensively considering vehicle mobility, channel condition, and computational resources with two goals: 1) high-reliability transmission (HRT) and 2) computational delay minimization (CDM). Specifically, we develop a hybrid MEC-enabled computation placement orchestrator for HetVNET, where the HRT and CDM are formulated as mixed-integer programming and nonconvex optimization problems, respectively. To ensure high-reliability communication, we leverage the conditional value at risk theory to tackle the nonsmooth HRT problem. To solve the CDM problem, we transform it into two subproblems: resource allocation and task offloading problems, aiming at reducing computational delay and improving resource utilization. Furthermore, we construct an iterative optimization algorithm to capture the optimal computation placement scheme in closed form for the HRT and CDM problems. Performance evaluations show that the proposed methods can significantly improve communication reliability and reduce computational delay.

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http://hdl.handle.net/10453/176519