Optimization and research on resource allocation strategy of vehicle edge computing based on deep learning

Gaoming Zhang; Jianwu Dang; Xiquan Zhang; Feng Wang

doi:10.1117/12.3051283

27 September 2024 Optimization and research on resource allocation strategy of vehicle edge computing based on deep learning

Gaoming Zhang, Jianwu Dang, Xiquan Zhang, Feng Wang

Author Affiliations +

Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 132810T (2024) https://doi.org/10.1117/12.3051283
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China

Abstract

In vehicular edge computing environments, where resources are limited and task latency sensitivity is high, we propose a resource allocation method based on deep reinforcement learning. First, a vehicular edge computing system model is constructed, then an optimal resource allocation problem is derived using a gradient iteration method and KKT conditions. The resource-optimized MADDPG algorithm is employed to replace the original DDPG algorithm to reduce time delays caused by uneven resource allocation. The effectiveness of the algorithm is verified through simulation experiments, and its performance in actual traffic scenarios is analyzed. Simulation results show that our method has superior performance in reducing time delays compared to traditional methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Gaoming Zhang, Jianwu Dang, Xiquan Zhang, and Feng Wang "Optimization and research on resource allocation strategy of vehicle edge computing based on deep learning", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 132810T (27 September 2024); https://doi.org/10.1117/12.3051283

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KEYWORDS

Computer simulations

Convex optimization

Deep learning

Autonomous vehicles

Data transmission

Detection and tracking algorithms

Neural networks

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