Joint Optimization Strategy of Computing Offloading and Edge Caching for Intelligent Connected Vehicles

Authors

Fei Ding, School of Internet of things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; Key Laboratory of Broadband Wireless Communication and Internet of Things of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; National Engineering Research Center for Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaFollow
Yuchen Sha, School of Internet of things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; Key Laboratory of Broadband Wireless Communication and Internet of Things of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaFollow
Ying Hong, School of Internet of things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; Key Laboratory of Broadband Wireless Communication and Internet of Things of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Xiao Kuai, School of Internet of things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; Key Laboratory of Broadband Wireless Communication and Internet of Things of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Dengyin Zhang, School of Internet of things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; Key Laboratory of Broadband Wireless Communication and Internet of Things of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; National Engineering Research Center for Communication and Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Abstract

To guarantee the low-delay communication of intelligent connected vehicles, the V2X channel model and the multi-access edge computing (MEC) technology, are used to carry out the research of the joint optimization strategy of computing offloading and edge caching.An intelligent connected vehicle with task offloading and edge caching model least-deep deterministic policy gradient(L-DDPG) is developed.By integrating the vehicular local and edge computing resources, the classification processing of different computing tasks in V2X scenarios is supported.The vehicular computing request is prejudged by edge platform to ensure the rapid response of continuous homogeneous computing tasks. Combining with the least recently used strategy, the new computing tasks are efficiently managed. A joint offloading decision for computing offloading and edge caching is carried out based on deep deterministic policy gradient(DDPG) algorithm.Simulation results show that the performance of L-DDPG model is better than that of traditional models, which can effectively improve the system performance, ensure the service quality, and reduce the time delay and resource consumption.

Recommended Citation

Ding, Fei; Sha, Yuchen; Hong, Ying; Kuai, Xiao; and Zhang, Dengyin (2023) "Joint Optimization Strategy of Computing Offloading and Edge Caching for Intelligent Connected Vehicles," Journal of System Simulation: Vol. 35: Iss. 6, Article 7.
DOI: 10.16182/j.issn1004731x.joss.22-0147
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss6/7

First Page

1203

Revised Date

2022-04-25

DOI Link

http://dx.doi.org/10.16182/j.issn1004731x.joss.22-0147

Last Page

1214

CLC

TP393

Recommended Citation

Fei Ding, Yuchen Sha, Ying Hong, Xiao Kuai, Dengyin Zhang. Joint Optimization Strategy of Computing Offloading and Edge Caching for Intelligent Connected Vehicles[J]. Journal of System Simulation, 2023, 35(6): 1203-1214.

DOI

10.16182/j.issn1004731x.joss.22-0147