Edge server deployment strategy based on multi-agent reinforcement learning in the internet of vehicles

被引：0
|
作者：
Li, Chuang [1 ]
Ji, Jianqiao [1 ]
Hu, Zhigang [2 ]
Zhou, Zhou [3 ]
机构：
[1] School of Computing, Hunan University of Technology and Business, Changsha
[2] School of Computer Science and Engineering, Central South University, Changsha
[3] School of Computer Science and Engineering, Changsha University, Changsha
来源：
Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | 2024年 / 55卷 / 07期
基金：
中国国家自然科学基金;
关键词：
edge computing; load balancing; reinforcement learning; server deployment; vehicle networking;
D O I：
10.11817/j.issn.1672-7207.2024.07.011
中图分类号：
学科分类号：
摘要：
To solve the hard problem of edge server deployment in internet of vehicle environments, an edge server deployment strategy based on multi-agent reinforcement learning(CKM−MAPPO) was proposed. It focuses on optimizing the load balancing among edge servers and minimizing edge servers' delay and energy consumption. Firstly, the Canopy and K−means algorithms were used to determine the number and initial location of edge server deployment. Then, the multi-agent reinforcement learning algorithm was leveraged to determine the optimal deployment location of the edge server. Finally, the accuracy and effectiveness of the proposed algorithm were evaluated through a series of experiments. The results show that compared with the benchmark algorithm, the proposed method improves load balancing by 26.5%, and the time delay and energy consumption are reduced by 12.4% and 17.9%, respectively. © 2024 Central South University of Technology. All rights reserved.
引用
收藏
页码：2567 / 2577
页数：10
相关论文
共 20 条
  • [1] LIU Hui, ZHANG Xinyu, YANG Yuxiang, Et al., Hourly traffic flow forecasting using a new hybrid modelling method[J], Journal of Central South University, 29, 4, (2022)
  • [2] XU Chuan, DING Yingyi, LUO Li, Et al., Personalized location privacy protection for location-based services in vehicular networks, Journal of Software, 33, 2, (2022)
  • [3] Guangjun GAO, ZHUO Tianyu, GUAN Weiyuan, Recent research development of energy-absorption structure and application for railway vehicles[J], Journal of Central South University, 27, 4, (2020)
  • [4] LI Bo, SHI Yuhao, YUAN Ye, Suitability-based edge server placement strategy in 5G ultra-dense networks[C], 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design(CSCWD), (2022)
  • [5] GONG Yadong, Optimal edge server and service placement in mobile edge computing[C], 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference(ITAIC), pp. 688-691, (2020)
  • [6] SONG Zhiyuan, MA Ruijiang, XIE Yong, A collaborative task offloading strategy for mobile edge computing in Internet of vehicles[C], 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 1379-1384, (2021)
  • [7] ZHANG Jie, LU Jiawei, YAN Xuan, Et al., Quantified edge server placement with quantum encoding in Internet of vehicles[J], IEEE Transactions on Intelligent Transportation Systems, 23, 7, pp. 9370-9379, (2022)
  • [8] HUANG Pingchun, CHIN T L, CHUANG T Y., Server placement and task allocation for load balancing in edge-computing networks[J], IEEE Access, 9, pp. 138200-138208, (2021)
  • [9] Yin KE, Bipartite graph based edge server placement algorithm in mobile edge computing[C], 2021 International Conference on Electronic Information Engineering and Computer Science(EIECS), pp. 493-496, (2021)
  • [10] KONG Weibo, DU Haohua, PAN Kai, Et al., Edge server quantification and placement in vehicle ad hoc networks[C], 2022 8th International Conference on Big Data Computing and Communications(BigCom), (2022)
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