Latency Estimation and Computational Task Offloading in Vehicular Mobile Edge Computing Applications

Zhang, W; Feng, M; Krunz, M

Latency Estimation and Computational Task Offloading in Vehicular Mobile Edge Computing Applications

Zhang, W Feng, M Krunz, M

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Vehicular Technology, 2024, PP, (99), pp. 1-16
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Zhang, W
dc.contributor.author	Feng, M
dc.contributor.author	Krunz, M
dc.date.accessioned	2024-05-03T05:48:14Z
dc.date.available	2024-05-03T05:48:14Z
dc.date.issued	2024
dc.identifier.citation	IEEE Transactions on Vehicular Technology, 2024, PP, (99), pp. 1-16
dc.identifier.issn	0018-9545
dc.identifier.issn	1939-9359
dc.identifier.uri	http://hdl.handle.net/10453/178622
dc.description.abstract	Mobile edge computing (MEC) is a key enabler of time-critical vehicle-to-everything (V2X) applications. Under MEC, a vehicle has the option to offload computationally intensive tasks to a nearby edge server or to a remote cloud server. Determining where to execute a task necessitates accurate estimation of the end-to-end (E2E) offloading delay. In this paper, we first conduct extensive measurements of the round-trip time (RTT) between a vehicular user and edge/cloud servers. Using these measurements, we present a latency-estimation framework for optimal task offloading. The propagation delay, measured by the RTT, is divided into two components: one that follows a trackable trend (baseline) and the other (residual) that is quasi-random. For the baseline component, we first cluster measured RTTs into several groups, depending on signal strength indicators. For each group, we develop a Long Short-Term Memory (LSTM) regression model. A statistical approach is provided for predicting the residual component, which combines the Epanechnikov Kernel and moving average functions. Predicted propagation delays are incorporated into virtual simulations to estimate the transmission, queuing, and processing delays, hence accounting for the E2E delay. Based on the estimated E2E delay, we design a task offloading scheme that minimizes the offloading latency while maintaining a low packet loss rate. Simulation results show that the proposed offloading strategy can reduce the E2E delay by approximately 60% compared to a random offloading scheme while keeping the packet loss rate below 3%.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Vehicular Technology
dc.relation.isbasedon	10.1109/TVT.2023.3334192
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Automobile Design & Engineering
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Latency Estimation and Computational Task Offloading in Vehicular Mobile Edge Computing Applications
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
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	2024-05-03T05:48:10Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Mobile edge computing (MEC) is a key enabler of time-critical vehicle-to-everything (V2X) applications. Under MEC, a vehicle has the option to offload computationally intensive tasks to a nearby edge server or to a remote cloud server. Determining where to execute a task necessitates accurate estimation of the end-to-end (E2E) offloading delay. In this paper, we first conduct extensive measurements of the round-trip time (RTT) between a vehicular user and edge/cloud servers. Using these measurements, we present a latency-estimation framework for optimal task offloading. The propagation delay, measured by the RTT, is divided into two components: one that follows a trackable trend (baseline) and the other (residual) that is quasi-random. For the baseline component, we first cluster measured RTTs into several groups, depending on signal strength indicators. For each group, we develop a Long Short-Term Memory (LSTM) regression model. A statistical approach is provided for predicting the residual component, which combines the Epanechnikov Kernel and moving average functions. Predicted propagation delays are incorporated into virtual simulations to estimate the transmission, queuing, and processing delays, hence accounting for the E2E delay. Based on the estimated E2E delay, we design a task offloading scheme that minimizes the offloading latency while maintaining a low packet loss rate. Simulation results show that the proposed offloading strategy can reduce the E2E delay by approximately 60% compared to a random offloading scheme while keeping the packet loss rate below 3%.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/178622