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Deep Learning-Based Task Offloading for Vehicular Edge Computing

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Wireless Algorithms, Systems, and Applications (WASA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12939))

Abstract

With widely used of computation intensive vehicular applications, Vehicular Edge Computing (VEC) plays an increasing important role in providing computation service for vehicles, and task scheduling has direct impact on task offloading performance. In this paper, we analyze the scheduling of tasks in VEC, propose a deep learning-based task scheduling mechanism, and design a model suitable for the prediction of task scheduling success probability and offloading delay. The performance of the model is evaluated with a large amount of data compared to the SVM-based task scheduling algorithm, the proposed mechanism has the offloading failure rate decreased by 40%.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Central South University, Changsha, 410083, Hunan, China

    Feng Zeng, Chengsheng Liu & Junzhe Tangjiang

  2. Department of Computer Science, New York Institute of Technology, New York, NY, 10023, USA

    Wenjia Li

Authors
  1. Feng Zeng
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  2. Chengsheng Liu
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  3. Junzhe Tangjiang
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  4. Wenjia Li
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Corresponding author

Correspondence to Feng Zeng .

Editor information

Editors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Zhe Liu

  2. Shanghai Jiao Tong University, Shanghai, China

    Fan Wu

  3. Missouri University of Science and Technology, Rolla, MO, USA

    Sajal K. Das

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Zeng, F., Liu, C., Tangjiang, J., Li, W. (2021). Deep Learning-Based Task Offloading for Vehicular Edge Computing. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12939. Springer, Cham. https://doi.org/10.1007/978-3-030-86137-7_32

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  • DOI: https://doi.org/10.1007/978-3-030-86137-7_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86136-0

  • Online ISBN: 978-3-030-86137-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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