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

边缘计算(EC)在靠近用户的网络边缘部署边缘服务器(ES),并将服务放置在ES上,从而可以满足用户的服务需求。独立研究ES部署和服务放置问题的成果已有很多,但两者存在高度耦合关系。考虑到EC系统的收益,有必要提供付费服务,使得EC系统处理用户服务请求时会获得相应收入。同时,EC系统处理用户服务请求时会产生时延和能耗成本,为了最大化EC系统的收益,在用户服务请求和服务价格不同的约束下,需要合适的服务放置方案来提高EC系统的收益。为此,在ES与基站之间的位置关系、ES部署和服务放置之间的耦合关系、服务副本数和服务价格等约束下,提出一种包括改进的k-means算法和多智能体强化学习算法的两步方法,使EC系统的收益最大化。首先,构建一个联合ES部署和服务放置模型,其中ES部署明确考虑了基站之间的位置关系,服务放置明确考虑了ES部署的位置,以及不同的服务请求和价格;然后,基于基站的位置关系和基站的服务请求负载,通过带约束的k-means算法,在不同约束条件下分别确定最佳的ES部署位置以及ES的协作域;最后,以最大化EC系统收益为目标,通过多智能体强化学习算法在ES上放置服务。实验结果表明,与对比方法相比,所提方法能够提高收益7%~23%。

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.