关键词: 边缘计算, 负载均衡, 模糊C均值, 强化学习

Abstract: The existing edge server placement methods are mainly used to improve service performance in scenarios such as 5G and wireless metropolitan area networks,but cannot be directly used for the deployment of Internet of Vehicles (IoV) services.Therefore,an edge computing system model with edge-cloud collaboration for 5G-IoV was proposed,and a deployment Strategy of edge servers based on Reinforcement Learning (SRL) was designed for this system model.Specifically,load optimization was taken as the core goal,and the load balancing among edge servers was realized under the premise of low delay and consumption.According to the location information of the roadside unit,the clustering algorithm Canopy was used to calculate the initial number of clusters.The initial division of the roadside unit was obtained using fuzzy C-means,and the roadside unit attribution priority matrix was output.Through the reinforcement learning,the optimal state of the roadside unit was obtained and the cluster center was calculated as the deployment location of the edge server.Comparative experiments verified that SRL had achieved a high degree of load balancing between edge servers under the premise of low service delay and consumption,which demonstrated the superiority of SRL.

Key words: edge computing, load balance, fuzzy C-means, reinforcement learning