用分层强化学习动态选择虚拟网络拓扑，提升资源利用率和请求接纳率。
Exploiting the Alternatives: Coordinated Learning via Hierarchical RL for Dynamic VNEAP
- 分层强化学习：高层选拓扑，底层负责部署。
- 相比现有方法，请求接纳率最高提升22%，净收益提升20%。
- 适合需要灵活应对动态网络请求的运营商和云服务商。
虚拟网络嵌入（VNE）是网络切片的关键技术，但传统方法假设每个虚拟网络请求（VNR）具有固定拓扑。近期提出的带可选方案的VNEAP（VNEAP）允许每个请求使用多个功能等价但资源消耗不同的拓扑，提升嵌入可行性。然而，只有在编排器能协同选择合适拓扑并处理动态到达请求时，这种灵活性才能发挥作用。本文提出HRL-VNEAP，一种用于动态VNEAP的分层强化学习方法：高层策略选择最合适的替代拓扑或拒绝请求，低层策略将选定拓扑嵌入到基础网络中。在真实基础拓扑和不同到达率下的实验表明，简单策略仅带来有限收益，而HRL-VNEAP优于现有先进方法，请求接纳率最高提升22%，净收益最高提升20%。同时，对可求解实例使用离线混合整数线性规划（MILP）上界，量化了剩余最优性差距。
原文摘要 · Abstract (English)
Virtual Network Embedding (VNE) is a key enabler of network slicing, yet most formulations assume that each Virtual Network Request (VNR) has a fixed topology. Recently, VNE with Alternatives (VNEAP) was introduced to capture malleable VNRs, where each request can be instantiated using one of several functionally equivalent topologies that trade resources differently. This flexibility can improve embedding feasibility, but only if the orchestrator can jointly select suitable alternatives and embed them under dynamic arrivals. This paper proposes HRL-VNEAP, a hierarchical reinforcement learning approach for dynamic VNEAP. A high-level policy selects the most suitable alternative topology (or rejects the request), and a low-level policy embeds the chosen topology onto the substrate network. Experiments on realistic substrate topologies under varying arrival rates show that naive exploitation strategies provide only modest gains, whereas HRL-VNEAP outperforms state of the art approaches, improving acceptance ratio by up to 22%, and net profit by up to 20%. An offline MILP upper bound is also used on tractable instances to quantify the remaining optimality gap.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。