Reducing Computation, Communication, and Storage Latency in Vehicular Edge Computing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00379319" target="_blank" >RIV/68407700:21230/24:00379319 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/10683495" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10683495</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/VTC2024-Spring62846.2024.10683495" target="_blank" >10.1109/VTC2024-Spring62846.2024.10683495</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Reducing Computation, Communication, and Storage Latency in Vehicular Edge Computing

Popis výsledku v původním jazyce

This paper addresses the challenge of optimizing communication, computation, and storage I/O caching in Vehicular Edge Computing (VEC) platforms for autonomous vehicles. The exponential data generated by the autonomous vehicles demands low-latency connectivity with nearby edge servers. However, the existing VEC platforms struggle to meet the performance requirements, especially in real-time applications like collision avoidance. This work proposes a novel algorithm for joint allocation of computing resources, storage I/O cache, and communication resources, considering the diverse priorities and demands of key vehicular services. Our approach integrates application-specific optimizations, prioritization, and joint latency reduction considering communication, computation, as well as storage. Accounting for distinct priorities and data access characteristics of various vehicular services, our proposed feasible solution, employing dual decomposition and Lagrangian relaxation, significantly reduces service latency by up to 64% compared to the current state-of-the-art resource allocation in vehicular edge computing.

Název v anglickém jazyce

Reducing Computation, Communication, and Storage Latency in Vehicular Edge Computing

Popis výsledku anglicky

This paper addresses the challenge of optimizing communication, computation, and storage I/O caching in Vehicular Edge Computing (VEC) platforms for autonomous vehicles. The exponential data generated by the autonomous vehicles demands low-latency connectivity with nearby edge servers. However, the existing VEC platforms struggle to meet the performance requirements, especially in real-time applications like collision avoidance. This work proposes a novel algorithm for joint allocation of computing resources, storage I/O cache, and communication resources, considering the diverse priorities and demands of key vehicular services. Our approach integrates application-specific optimizations, prioritization, and joint latency reduction considering communication, computation, as well as storage. Accounting for distinct priorities and data access characteristics of various vehicular services, our proposed feasible solution, employing dual decomposition and Lagrangian relaxation, significantly reduces service latency by up to 64% compared to the current state-of-the-art resource allocation in vehicular edge computing.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of The 2024 IEEE 99th Vehicular Technology Conference: VTC2024-Spring

ISBN

979-8-3503-8741-4

ISSN

2577-2465

e-ISSN

—

Počet stran výsledku

7

Strana od-do

1-7

Název nakladatele

IEEE Vehicular Technology Society

Místo vydání

New York

Místo konání akce

Singapore

Datum konání akce

24. 6. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001327706002141