计算机工程2024，Vol.50Issue(10)：266-280,15.DOI:10.19678/j.issn.1000-3428.0068576
一种联合边缘服务器部署与服务放置的方法
A Method for Joint Edge Server Deployment and Service Placement
摘要
Abstract
Edge Computing(EC)deploys Edge Servers(ES)at the edge of the network close to the user.Services are placed on the ES to meet users'service needs.Several independent studies have been conducted on ES deployment and service placement.However,a highly coupled relationship exists between the two and they should be studied simultaneously.In addition,the economic benefit of the EC system is a consideration because paid services must be provided for the EC system to earn revenue in processing user service requests;however,the EC system incurs delays and energy costs when processing the user service requests.To maximize the benefits of the EC system under the constraint that user service requests and service prices are different,appropriate service placement solutions are required to increase the overall profit.To that end,this study considers the constraints of the location relationship between ES and base stations,coupling relationship between ES deployment and service placement,number of service replicas,and price of the service and proposes a two-step approach that includes an improved k-means algorithm and a multi-agent reinforcement learning algorithm.The goal is to maximize the benefits of EC systems.First,a joint ES deployment and service placement model is constructed.One of the ES deployments explicitly considers the location relationship between base stations,whereas service placement considers the location of ES deployments as well as different service requests and pricing.Subsequently,based on the location relationship of base stations and service request load of base stations,the k-means algorithm is used under constraints to determine the optimal deployment location and collaborative domain of ES under different constraint conditions.Finally,to maximize the benefits of the EC system,a multi-agent reinforcement learning algorithm is used to place services on the ES.The experimental results show that the proposed algorithm increases the benefits by 7％to 23％relative to the comparison algorithms.关键词
边缘计算/边缘服务器部署/服务放置/k-means聚类算法/多智能体强化学习算法Key words
Edge Computing(EC)/Edge Server(ES)deployment/service placement/k-means clustering algorithm/multi-agent reinforcement learning algorithm分类
计算机与自动化引用本文复制引用
张俊娜,韩超臣,陈家伟,赵晓焱,袁培燕..一种联合边缘服务器部署与服务放置的方法[J].计算机工程,2024,50(10):266-280,15.基金项目
科技创新2030—"新一代人工智能"重大项目(2022ZD0118502) （2022ZD0118502）
国家自然科学基金(62072159) （62072159）
河南省科技攻关资助项目(232102211061,222102210011). （232102211061,222102210011）
