It is now commonly agreed that future 5G Networks will build upon the network slicing concept. The ability to provide virtual, logically independent “slices” of the network will also have an impact on the models that will sustain the business ecosystem. Network slicing will open the door to new players: the infrastructure provider, which is the owner of the infrastructure, and the tenants, which may acquire a network slice from the infrastructure provider to deliver a specific service to their customers. In this new context, how to correctly handle resource allocation among tenants and how to maximize the monetization of the infrastructure become fundamental problems that need to be solved. In this paper, we address this issue by designing a network slice admission control algorithm that ($i$i) autonomously learns the best acceptance policy while ($ii$ii) it ensures that the service guarantees provided to tenants are always satisfied. The contributions of this paper include: ($i$i) an analytical model for the admissibility region of a network slicing-capable 5G Network, ($ii$ii) the analysis of the system (modeled as a Semi-Markov Decision Process) and the optimization of the infrastructure providers revenue, and ($iii$iii) the design of a machine learning algorithm that can be deployed in practical settings and achieves close to optimal performance.

中文翻译：

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用于 5G 基础设施市场优化的机器学习方法

现在普遍认为，未来的 5G 网络将建立在网络切片概念的基础上。提供虚拟的、逻辑上独立的网络“切片”的能力也将对维持业务生态系统的模型产生影响。网络切片将为新参与者打开大门：基础设施提供商，即基础设施的所有者，以及租户，他们可能会从基础设施提供商那里获得网络切片，以向其客户提供特定服务。在新的背景下，如何正确处理租户之间的资源分配，如何最大化基础设施的货币化成为亟待解决的根本问题。在本文中，我们通过设计一种网络切片准入控制算法来解决这个问题，$i$一世) 自主学习最佳接受策略，而 ($ii$一世一世) 它确保向租户提供的服务保证始终得到满足。本文的贡献包括：（$i$一世) 具有网络切片能力的 5G 网络的可接受区域的分析模型，($ii$一世一世) 系统分析（建模为半马尔可夫决策过程）和基础设施提供商收入的优化，以及（$iii$一世一世一世) 设计一种机器学习算法，可以在实际环境中部署并实现接近最佳性能。