面向下一代移动网络的数据驱动在线切片接纳控制与资源分配
Data-Driven Online Slice Admission Control and Resource Allocation in NextG Mobile Networks
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中文总结 AI 辅助
针对下一代移动网络的切片接纳与资源分配问题，提出OPA框架及数据驱动指数定价方法，可显著提升收入并降低计算成本。
中文摘要 AI 辅助
5G及后续网络中的虚拟化技术可创建适配不同应用需求的虚拟网络（即网络切片）。为在有限基础设施资源下最大化收入，基础设施提供商（InPs）必须实时根据切片请求（SRs）的资源需求与提供价值，结合稀缺资源的机会成本，决定是否接纳新的切片请求。为应对这一挑战，本文提出基于在线定价的切片接纳控制与资源分配（OPA）框架，该框架为资源动态分配反映长期稀缺性与预期跨期机会成本的伪价格，各切片请求的短期接纳与资源分配决策由这些价格引导。此外，本文设计了指数定价策略，可保证有界的最坏情况性能；为提升实际性能，进一步开发了从历史数据中学习的数据驱动指数定价方法。在真实网络拓扑上的评估显示，该方法相比最先进的深度强化学习（DRL）方法和基于优化的方法，分别将平均收入提升了32.2%和26.7%，同时计算成本相比后者降低了一个数量级。
英文摘要
Virtualization in 5G and beyond networks enables the creation of virtual networks (i.e., network slices) tailored to the needs of different applications. To maximize revenue under limited infrastructure resources, InPs must decide in real time whether to admit incoming slice requests (SRs) based on their resource demands and offered values, while accounting for the opportunity cost of consuming scarce resources. To address this challenge, we introduce Online Pricing-based Slice Admission Control and Resource Allocation (OPA) framework. This framework dynamically assigns pseudo-prices to resources that capture long-term scarcity and anticipated inter-temporal opportunity costs. The short-term admission and resource allocation decisions for each SR are then guided by these prices. Additionally, we design an exponential pricing strategy that guarantees bounded worst-case performance. To improve practical performance, we further develop a data-driven exponential pricing approach that learns from historical data. Evaluations on a real-world network topology show that it improves mean revenue by 32.2% and 26.7% over state-of-the-art DRL and optimization-based approaches, respectively, while reducing computational cost by an order of magnitude relative to the latter.
