从协作设备边缘云网络部署用户按需驱动的MEC服务器

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Transactions on Services Computing Pub Date : 2026-03-01 Epub Date: 2025-08-25 DOI:10.1109/TSC.2025.3602408

Jine Tang;Jiahao Jin;Wentao Zhao;Song Yang;Yong Xiang;Zhangbing Zhou

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引用次数: 0

摘要

随着5g通信技术和物联网（IoT）的快速发展，移动边缘计算（MEC）被认为是为移动用户提供低延迟、高质量服务的有效范例。在物联网设备-边缘云网络中，MEC服务器的优化部署是实现更好的任务卸载的前提，而移动用户任务卸载性能的提升也表明部署方案是最优的。目前的MEC服务器部署研究大多着眼于降低延迟和部署成本，而忽略了具有相似任务类型和合作关系到达同一社区的移动用户的卸载需求。本文研究在当前社区移动用户的任务卸载需求驱动下的MEC服务器部署，利用社区移动用户社会合作关系的稳定性，在未来的任务卸载中最大化所有社区移动用户的服务满意度。首先，测量移动用户之间的合作关系强度，根据交互概率、移动轨迹和信用强度形成资源请求者群体；然后，利用空间索引实现基站的最优搜索，然后利用基站与社区群资源请求者之间的一对多匹配理论，平衡基站的负载，降低基站间的通信延迟。最后，我们利用TD（$\lambda$）算法和协作用户之间的任务相似度，在BSs周围部署合适资源的MEC服务器，使得该部署方案能够显著提高所有社区移动用户未来的任务卸载性能。基于上海电信提供的真实数据集，验证了所提方案在提高所有社区移动用户服务满意度方面具有显著优势，与基线相比平均提高18.49%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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User on-Demand Driven MEC Servers Deployment From Collaborative Device-Edge-Cloud Network

With the rapid development of 6 G communication technology and the Internet of Things (IoT), mobile edge computing (MEC) is regarded as an effective paradigm of providing low-delay, high-quality services to mobile users. In the IoT device-edge-cloud network, the optimal deployment of MEC servers is a prerequisite for a better task offloading, while the improved performance of mobile users task offloading also indicates the deployment scheme is optimal. Most of current MEC servers deployment studies focus on reducing delay and deployment costs, but ignore the offloading requirements of mobile users with similar task type and cooperative relationship arriving at the same community. In this paper, we study the MEC servers deployment driven by the task offloading requirements of community mobile users in current period by utilizing the stability of their social cooperative relationships to maximize the service satisfaction of all community mobile users in the future task offloading. First, the cooperative relationship strength between mobile users is measured to form a group of resource requesters based on interaction probability, movement trajectory and credit strength. Then, we implement the optimal search of base stations (BSs) using spatial index, followed by the one-to-many matching theory between BSs and community group resource requesters, to balance the load of BSs and reduce the communication delay between them. Finally, we use TD(

$\lambda$

) algorithm and task similarity between cooperative users to deploy MEC servers with suitable resources around BSs so that the deployment scheme can significantly improve the future task offloading performance of all community mobile users. Based on the real data set provided by Shanghai Telecom, it is confirmed that the proposed scheme has significant advantages in improving all community mobile users service satisfaction, with an average improvement of 18.49% compared with the baselines.

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来源期刊

IEEE Transactions on Services Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

11.50

自引率

6.20%

发文量

278

审稿时长

>12 weeks

期刊介绍： IEEE Transactions on Services Computing encompasses the computing and software aspects of the science and technology of services innovation research and development. It places emphasis on algorithmic, mathematical, statistical, and computational methods central to services computing. Topics covered include Service Oriented Architecture, Web Services, Business Process Integration, Solution Performance Management, and Services Operations and Management. The transactions address mathematical foundations, security, privacy, agreement, contract, discovery, negotiation, collaboration, and quality of service for web services. It also covers areas like composite web service creation, business and scientific applications, standards, utility models, business process modeling, integration, collaboration, and more in the realm of Services Computing.