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Efficient approaches for task offloading in point-of-interest based vehicular fog computing

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Abstract

Vehicular fog computing (VFC), as the extension of the mobile cloud, has been widely investigated. However, in most existing VFC works, the scenario with point-of-interests (PoIs) is rarely considered, which can lead to underutilization of vehicle computing resources and overloading of the computing load for roadside units (RSUs). Therefore, this paper focuses on improving the quality of services for PoI-based VFC by utilizing the computation capabilities of vehicles to assist the RSU. An NP-hard problem is formulated to maximize the number of tasks completed within the duration of delay constraints. To solve the problem, a delay-aware task offloading algorithm, named Delay-Aware Task Offloading (DTO), is proposed. DTO preferentially operates on tasks with tighter delay constraints. To minimize the completion delay for each offloaded task, DTO selects the set of service vehicles to perform tasks. Moreover, a vehicle-resource-aware task offloading algorithm, named Vehicle-Resources-Aware Task Offloading (VRTO), is proposed to maximize the number of tasks completed within the duration of delay constraints by efficiently utilizing the available resource of the vehicle. Specifically, VRTO allocates the data to each service vehicle according to the service configuration of vehicles and the delay constraints of tasks. Finally, extensive experiments are carried out to evaluate the effectiveness of the proposed algorithms. The performance is successfully improved by more than 30% compared with the baselines in terms of the number of completed tasks.

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Acknowledgements

This work is supported in part by National Natural Science Foundation of China under Grant Nos. 62106052, 62072118, 62202108 and U1911401, National Key Research and Development Project under Grant 2018YFB1802400, Key-Area Research and Development Program of Guangdong Province under Grant 2020B0101130001, Huangpu International Sci &Tech Cooperation Fundation of Guangzhou, China under Grant 2021GH12.

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Authors and Affiliations

  1. Computer Science and Technology, Guangdong University of Technology, Guangzhou, China

    Yifei Sun, Jigang Wu & Long Chen

  2. School of Integrated Circuits, Guangdong University of Technology, Guangzhou, China

    Yalan Wu

  3. School of Automation, Guangdong University of Technology, Guangzhou, China

    Weijun Sun

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  1. Yifei Sun
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  4. Long Chen
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Contributions

YS, LC, and JW conceived of the presented idea. JW and YW encouraged YS to investigate the algorithms for the proposed problem. YS carried out the experiment and wrote the main manuscript text. WS prepared figures 1–2. All authors discussed the results and revised the manuscript.

Corresponding author

Correspondence to Jigang Wu.

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Sun, Y., Wu, J., Wu, Y. et al. Efficient approaches for task offloading in point-of-interest based vehicular fog computing. J Supercomput 80, 6285–6310 (2024). https://doi.org/10.1007/s11227-023-05698-y

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