research-article

Deep Reinforcement Learning for Vehicular Edge Computing: An Intelligent Offloading System

Authors:

Joel J. P. C. Rodrigues,

Feng XiaAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 10, Issue 6

Article No.: 60, Pages 1 - 24

https://doi.org/10.1145/3317572

Published: 18 October 2019 Publication History

Abstract

The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users’ traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users’ Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.

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Cited By

Wu HLu YMa HXing LDeng KLu X(2025)A survey on task type-based computation offloading in mobile edge networksAd Hoc Networks10.1016/j.adhoc.2025.103754169(103754)Online publication date: Mar-2025
https://doi.org/10.1016/j.adhoc.2025.103754
Han SWang MChen G(2025)Overview of Vehicle Edge Computing and Its SecuritySecurity and Privacy in New Computing Environments10.1007/978-3-031-73699-5_8(105-121)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-73699-5_8
Peng XZhang YZhang XZhang CYang W(2024)Collaborative Optimization Strategy for Dependent Task Offloading in Vehicular Edge ComputingMathematics10.3390/math1223382012:23(3820)Online publication date: 2-Dec-2024
https://doi.org/10.3390/math12233820
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Index Terms

Deep Reinforcement Learning for Vehicular Edge Computing: An Intelligent Offloading System
1. Networks
  1. Network services
    1. Network management

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 10, Issue 6

Special Section on Intelligent Edge Computing for Cyber Physical and Cloud Systems and Regular Papers

November 2019

267 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3368406

Editor:
Yu Zheng
JD Finance, China

Issue’s Table of Contents

Copyright © 2019 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 October 2019

Accepted: 01 March 2019

Revised: 01 February 2019

Received: 01 December 2018

Published in TIST Volume 10, Issue 6

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Funding Sources

State Key Laboratory of Integrated Services Networks, Xidian University (ISN20-01)
National Natural Science Foundation of China
China Postdoctoral Science Foundation
State Key Laboratory for Novel Software Technology, Nanjing University
Dalian Science and Technology Innovation Fund
National Natural Science Foundation of Chongqing
Brazilian National Council for Research and Development (CNPq)
National Funding from the FCT—Fundação para a Ciência e a Tecnologia
RNP, with resources from MCTIC
Centro de Referência em Radiocomunicações—CRR project of the Instituto Nacional de Telecomunicações (Inatel), Brazil

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Wu HLu YMa HXing LDeng KLu X(2025)A survey on task type-based computation offloading in mobile edge networksAd Hoc Networks10.1016/j.adhoc.2025.103754169(103754)Online publication date: Mar-2025
https://doi.org/10.1016/j.adhoc.2025.103754
Han SWang MChen G(2025)Overview of Vehicle Edge Computing and Its SecuritySecurity and Privacy in New Computing Environments10.1007/978-3-031-73699-5_8(105-121)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-73699-5_8
Peng XZhang YZhang XZhang CYang W(2024)Collaborative Optimization Strategy for Dependent Task Offloading in Vehicular Edge ComputingMathematics10.3390/math1223382012:23(3820)Online publication date: 2-Dec-2024
https://doi.org/10.3390/math12233820
Holzer SFrangoudis PTsigkanos CDustdar S(2024)SMT-as-a-Service for Fog-Supported Cyber-Physical SystemsProceedings of the 25th International Conference on Distributed Computing and Networking10.1145/3631461.3631562(154-163)Online publication date: 4-Jan-2024
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Lin PNing ZZhang ZLiu YYu FLeung V(2024)Joint Optimization of Preference-Aware Caching and Content Migration in Cost-Efficient Mobile Edge NetworksIEEE Transactions on Wireless Communications10.1109/TWC.2023.332346423:5(4918-4931)Online publication date: May-2024
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Guo YMa DShe HGui GYuen CSari HAdachi F(2024)Deep Deterministic Policy Gradient-Based Intelligent Task Offloading for Vehicular Computing With Priority Experience PlaybackIEEE Transactions on Vehicular Technology10.1109/TVT.2024.337891973:7(10655-10667)Online publication date: Jul-2024
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