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车联网中基于多智能体强化学习的边缘服务器选址策略

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为解决车联网环境下边缘服务器选址难的问题，提出一种基于多智能体强化学习的边缘服务器部署策略(记为CKM-MAPPO)，重点优化边缘服务器间的负载均衡，同时最小化边缘服务器的时延和能耗。首先，使用Canopy和K-means算法确定边缘服务器部署的数量和初始位置;然后，基于多智能体强化学习算法确定边缘服务器的最优部署位置;最后，通过一系列实验评估所提出算法的准确性和有效性。研究结果表明:与基准算法相比，本文提出的方法的负载均衡度提升了26。5%，时延和能耗分别降低了12。4%和17。9%。
Edge server deployment strategy based on multi-agent reinforcement learning in the internet of vehicles
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.

edge computingserver deploymentvehicle networkingload balancingreinforcement learning

李闯、纪剑桥、胡志刚、周舟

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湖南工商大学计算机学院,湖南长沙,410205

中南大学计算机学院,湖南长沙,410075

长沙学院计算机科学与工程学院,湖南长沙,410022

边缘计算 服务器部署 车联网 负载均衡 强化学习

国家自然科学基金资助项目国家自然科学基金资助项目国家自然科学基金资助项目湘江实验室重大项目湘江实验室重大项目湖南省教育厅青年项目湖南省重点研发计划项目长沙市杰出创新青年培养计划项目湖南省自然科学基金资助项目Natural Science Foundation of Hunan Province

62172442620021156237206823XJ0100222XJ0100121B07792021NK2020kq21070202022JJ40128

2024

10.11817/j.issn.1672-7207.2024.07.011

中南大学学报(自然科学版)
中南大学

中南大学学报(自然科学版)

CSTPCD北大核心
影响因子：0.938
ISSN：1672-7207
年,卷(期)：2024.55(7)