Network slicing (NS) is a key technology in 5G networks that enables the customization and efficient sharing of network resources to support the diverse requirements of nextgeneration services. This paper proposes a resource allocation scheme for NS based on the Fisher-market model and the Trading-post mechanism. The scheme aims to achieve efficient resource utilization while ensuring multi-level fairness, dynamic load conditions, and the protection of service level agreements (SLAs) for slice tenants. In the proposed scheme, each service provider (SP) is allocated a budget representing its infrastructure share or purchasing power in the market. SPs acquire different resources by spending their budgets to offer services to different classes of users, classified based on their service needs and priorities. The scheme assumes that SPs employ the $\alpha$-fairness criteria to deliver services to their subscribers. The resource allocation problem is formulated as a convex optimization problem to find a market equilibrium (ME) solution that provides allocation and resource pricing. A privacy-preserving learning algorithm is developed to enable SPs to reach the ME in a decentralized manner. The performance of the proposed scheme is evaluated through theoretical analysis and extensive numerical simulations, comparing it with the Social Optimal and Static Proportional sharing schemes.

翻译：网络切片（NS）是5G网络中的关键技术，它支持网络资源的定制化与高效共享，以满足下一代服务的多样化需求。本文提出了一种基于Fisher市场模型与交易机制（Trading-post mechanism）的NS资源分配方案。该方案旨在实现高效资源利用的同时，确保多级公平性、动态负载条件，并保护切片租户的服务等级协议（SLA）。在所提方案中，每个服务提供商（SP）被分配一个预算，代表其在市场中的基础设施份额或购买力。SP通过消费预算获取不同资源，为按服务需求和优先级分类的各类用户提供服务。该方案假设SP采用$\alpha$-公平准则为其用户提供服务。资源分配问题被建模为凸优化问题，以求解市场均衡（ME）方案，从而提供资源分配与定价。本文开发了一种隐私保护学习算法，使SP能够以去中心化方式达成ME。通过理论分析与大量数值仿真，对所提方案的性能进行了评估，并与社会最优及静态比例共享方案进行了比较。