信息不对称下多访问边缘计算服务配置的讨价还价方法

IF 7.7 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Bernd Simon;Paul Adrian;Patrick Weber;Patrick Felka;Oliver Hinz;Anja Klein
{"title":"信息不对称下多访问边缘计算服务配置的讨价还价方法","authors":"Bernd Simon;Paul Adrian;Patrick Weber;Patrick Felka;Oliver Hinz;Anja Klein","doi":"10.1109/TMC.2025.3533045","DOIUrl":null,"url":null,"abstract":"Multi-access edge computing (MEC) refers to deploying computation resources, known as cloudlets or edge servers, near the edge of the mobile network. Services like augmented reality (AR) benefit from MEC by service placement, which refers to installing service-specific software and allocating resources on cloudlets. Service placement in MEC improves service quality and potentially reduces costs compared to centralized cloud computing approaches. The main stakeholders in MEC are infrastructure providers (IPs), who manage the MEC infrastructure, and service providers (SPs), who offer services to users. Both have unique technical and economic perspectives, such as resource demands, resource availability, and costs. Information asymmetries exist as only IPs have access to information about their resources, and only SPs have information about service usage and resource demands. This work addresses challenges of service placement in MEC from a multi-stakeholder, techno-economic perspective. We introduce a model including the stakeholders’ technical and economic goals and information asymmetries. To solve this problem efficiently, we propose a multi-stakeholder bargaining mechanism, termed Nash Backward Induction with Linear Equilibrium Strategies (NBI-LES). In a case study with 544 users and 16 SPs, we achieve <inline-formula><tex-math>$\\text{79}{\\%}$</tex-math></inline-formula> of the optimal reduction in traffic given by a centralized optimal service placement strategy.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 6","pages":"5464-5481"},"PeriodicalIF":7.7000,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Bargaining Approach for Service Placement in Multi-Access Edge Computing With Information Asymmetries\",\"authors\":\"Bernd Simon;Paul Adrian;Patrick Weber;Patrick Felka;Oliver Hinz;Anja Klein\",\"doi\":\"10.1109/TMC.2025.3533045\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Multi-access edge computing (MEC) refers to deploying computation resources, known as cloudlets or edge servers, near the edge of the mobile network. Services like augmented reality (AR) benefit from MEC by service placement, which refers to installing service-specific software and allocating resources on cloudlets. Service placement in MEC improves service quality and potentially reduces costs compared to centralized cloud computing approaches. The main stakeholders in MEC are infrastructure providers (IPs), who manage the MEC infrastructure, and service providers (SPs), who offer services to users. Both have unique technical and economic perspectives, such as resource demands, resource availability, and costs. Information asymmetries exist as only IPs have access to information about their resources, and only SPs have information about service usage and resource demands. This work addresses challenges of service placement in MEC from a multi-stakeholder, techno-economic perspective. We introduce a model including the stakeholders’ technical and economic goals and information asymmetries. To solve this problem efficiently, we propose a multi-stakeholder bargaining mechanism, termed Nash Backward Induction with Linear Equilibrium Strategies (NBI-LES). In a case study with 544 users and 16 SPs, we achieve <inline-formula><tex-math>$\\\\text{79}{\\\\%}$</tex-math></inline-formula> of the optimal reduction in traffic given by a centralized optimal service placement strategy.\",\"PeriodicalId\":50389,\"journal\":{\"name\":\"IEEE Transactions on Mobile Computing\",\"volume\":\"24 6\",\"pages\":\"5464-5481\"},\"PeriodicalIF\":7.7000,\"publicationDate\":\"2025-01-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Mobile Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10851425/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Mobile Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10851425/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

多接入边缘计算(Multi-access edge computing, MEC)是指在移动网络边缘附近部署计算资源,即云计算或边缘服务器。像增强现实(AR)这样的服务通过服务放置从MEC中受益,服务放置指的是在云上安装特定于服务的软件并分配资源。与集中式云计算方法相比,MEC中的服务放置提高了服务质量,并可能降低成本。MEC的主要利益相关者是管理MEC基础设施的基础设施提供商(ip)和向用户提供服务的服务提供商(sp)。两者都具有独特的技术和经济观点,例如资源需求、资源可用性和成本。存在信息不对称,因为只有ip可以访问其资源的信息,而只有sp可以访问服务使用和资源需求的信息。这项工作从多方利益相关者、技术经济的角度解决了在MEC中服务安置的挑战。我们引入了一个包含利益相关者的技术经济目标和信息不对称的模型。为了有效地解决这一问题,我们提出了一种多利益相关者议价机制,称为纳什线性均衡策略逆向归纳(NBI-LES)。在一个有544个用户和16个服务提供商的案例研究中,我们通过集中式最优服务放置策略实现了流量的最优减少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Bargaining Approach for Service Placement in Multi-Access Edge Computing With Information Asymmetries
Multi-access edge computing (MEC) refers to deploying computation resources, known as cloudlets or edge servers, near the edge of the mobile network. Services like augmented reality (AR) benefit from MEC by service placement, which refers to installing service-specific software and allocating resources on cloudlets. Service placement in MEC improves service quality and potentially reduces costs compared to centralized cloud computing approaches. The main stakeholders in MEC are infrastructure providers (IPs), who manage the MEC infrastructure, and service providers (SPs), who offer services to users. Both have unique technical and economic perspectives, such as resource demands, resource availability, and costs. Information asymmetries exist as only IPs have access to information about their resources, and only SPs have information about service usage and resource demands. This work addresses challenges of service placement in MEC from a multi-stakeholder, techno-economic perspective. We introduce a model including the stakeholders’ technical and economic goals and information asymmetries. To solve this problem efficiently, we propose a multi-stakeholder bargaining mechanism, termed Nash Backward Induction with Linear Equilibrium Strategies (NBI-LES). In a case study with 544 users and 16 SPs, we achieve $\text{79}{\%}$ of the optimal reduction in traffic given by a centralized optimal service placement strategy.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
IEEE Transactions on Mobile Computing
IEEE Transactions on Mobile Computing 工程技术-电信学
CiteScore
12.90
自引率
2.50%
发文量
403
审稿时长
6.6 months
期刊介绍: IEEE Transactions on Mobile Computing addresses key technical issues related to various aspects of mobile computing. This includes (a) architectures, (b) support services, (c) algorithm/protocol design and analysis, (d) mobile environments, (e) mobile communication systems, (f) applications, and (g) emerging technologies. Topics of interest span a wide range, covering aspects like mobile networks and hosts, mobility management, multimedia, operating system support, power management, online and mobile environments, security, scalability, reliability, and emerging technologies such as wearable computers, body area networks, and wireless sensor networks. The journal serves as a comprehensive platform for advancements in mobile computing research.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信