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Aphto: a task offloading strategy for autonomous driving under mobile edge

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Abstract

With the increasing complexity of autonomous driving tasks, the computational demands on single vehicular computing units have escalated, more and more tasks need to be offloaded to the edge. These tasks vary in latency sensitivity: real-time tasks, critical for passenger safety, require strict deadline adherence, whereas the latency of standard tasks mainly affects the user experience and has more flexible constraints. Addressing the challenge of selecting suitable edge computing nodes to enhance the offloading success rate of real-time tasks amidst a vast and heterogeneous cluster becomes crucial. This paper introduces the adaptive priority-based hierarchical task offloading (APHTO) algorithm, which optimizes task offloading strategies by accounting for the diverse latency constraints of different task types. Experiments demonstrate that under optimal performance conditions, APHTO significantly outperforms existing algorithms such as Min–Min, Max–Min, CUS, and FMS in reducing task latency by 20.31%, increasing offloading success rates by 35.83%, and improving resource utilization by 30.21%, marking a substantial advancement in task offloading strategies for autonomous driving integrated with MEC.

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All data were collected by the authors and are therefore available for use. JiaCheng Lin, HuanLe Rao, SongSong Liang, YuMiao Zhao, Qing Ren, GangYong Jia will make every effort to provide complete access to all data and materials used in this study upon request. We acknowledge that the availability of these resources can help to facilitate scientific progress and promote transparency and openness in researchers’ work.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China under Grant No. U20A20386, Zhejiang Key Research and Development Program under Grant No. 2023C03194 and No. 2023C03090, Zhejiang Natural Science Foundation under Grant No. QY19E050003, the Key Laboratory fund general project under Grant No. 6142110190406, the key open project of 32 CETC under Grant No. 22060207026, and ZheJiang Education Department General Scientific Research Project under Grant No. Y201738340.

Funding

This research was funded by National Natural Science Foundation of China, Zhejiang Key Research and Development Program, Zhejiang Natural Science Foundation, the Key Laboratory fund general project, the key open project of 32 CETC and ZheJiang Education Department General Scientific Research Project. The funders played no role in the design of the study, data collection, analysis and interpretation, or writing of the manuscript. JiaCheng Lin, HuanLe Rao, SongSong Liang, YuMiao Zhao, Qing Ren, GangYong Jia acknowledge the support of funders for this research. We would also like to express our gratitude to all other supporters who have contributed to this study.

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Authors and Affiliations

  1. The School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China

    JiaCheng Lin, SongSong Liang, YuMiao Zhao, Qing Ren & GangYong Jia

  2. The School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China

    HuanLe Rao

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  1. JiaCheng Lin
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Contributions

JiaCheng Lin, HuanLe Rao, SongSong Liang, YuMiao Zhao, Qing Ren, GangYong Jia contributed to this study in the following ways: JiaCheng Lin and SongSong Lin were involved in the conceptualization, methodology, formal analysis, investigation, and writing—original draft preparation. HuanLe Rao, YuMiao Zhao, and Qing Ren contributed to the methodology, data curation, formal analysis, and writing—review and editing. GangYong Jia and HuanLe Rao assisted in the supervision, project administration, funding acquisition, and writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to HuanLe Rao or GangYong Jia.

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Conflict of interest

The authors declare no Conflict of interest. JiaCheng Lin declare that no person or entity has, within the past two years, had in any significant financial, professional or personal conflicts or otherwise influenced the conduct or findings of this research. We declare that there are no other Conflict of interest related to this study.

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Appendix

Appendix

1.1 Model and system environments

To facilitate replication by researchers, we detail the specific hardware parameters and system versions used in our simulation environment. This environment includes mobile nodes, labeled as num 1, 2, 3, 4, and 8, and MEC (Mobile Edge Computing) nodes, identified as num 6 and 7. Notably, nodes num 1 and 2 are each equipped with two devices, underscoring their unique configuration within the simulation setup (Table 4).

Table 4 Simulation Experiment Hardware Parameters
Full size table

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Lin, J., Rao, H., Liang, S. et al. Aphto: a task offloading strategy for autonomous driving under mobile edge. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06054-4

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