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Task offloading for vehicular edge computing with edge-cloud cooperation

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A Correction to this article was published on 17 May 2022

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Abstract

Vehicular edge computing (VEC) is emerging as a novel computing paradigm to meet low latency demands for computation-intensive vehicular applications. However, most existing offloading schemes do not take the dynamic edge-cloud computing environment into account, resulting in high delay performance. In this paper, we propose an efficient offloading scheme based on deep reinforcement learning for VEC with edge-cloud computing cooperation, where computation-intensive tasks can be executed locally or can be offloaded to an edge server, or a cloud server. By jointly considering: i) the dynamic edge-cloud computing environment; ii) fast offloading decisions, we leverage deep reinforcement learning to minimize the average processing delay of tasks by effectively integrating the computation resources of vehicles, edge servers, and the cloud server. Specifically, a deep Q-network (DQN) is used to adaptively learn optimal offloading schemes in the dynamic environment by balancing the exploration process and the exploitation process. Furthermore, the offloading scheme can be quickly learned by speeding up the convergence of the training process of DQN, which is good for fast offloading decisions. We conduct extensive simulation experiments and the experimental results show that the proposed offloading scheme can achieve a good performance.

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Acknowledgements

This work has been supported by the Project of Key Science Foundation of Yunnan Province under Grant No. 202101AS070007, the expert workstation of Yunnan Province under Grant No.202105AF150013, the Project of National Natural Science Foundation of China under Grant No. 61862065 and 12163004, the Major Project of Science and Technology of Yunnan Province under Grant No. 202002AD080002, the Project of Science and Technology of Yunnan Province under Grant No.202001AT070135.

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Authors and Affiliations

  1. School of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, China

    Fei Dai, Guozhi Liu, WeiHeng Xu & Bi Huang

  2. School of Software, Yunnan University, Kunming, China

    Qi Mo

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  1. Fei Dai
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  2. Guozhi Liu
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This article belongs to the Topical Collection: Special Issue on Resource Management at the Edge for Future Web, Mobile, and IoT Application

Guest Editors' names: Qiang He, Fang Dong, Chenshu Wu, and Yun Yang

The original online version of this article was revised: A modification has been in the Abstract. Full information regarding the correction made can be found in the erratum/correction for this article.

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Dai, F., Liu, G., Mo, Q. et al. Task offloading for vehicular edge computing with edge-cloud cooperation. World Wide Web 25, 1999–2017 (2022). https://doi.org/10.1007/s11280-022-01011-8

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  • DOI: https://doi.org/10.1007/s11280-022-01011-8

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