Virtual Network Embedding (VNE) is a challenging combinatorial optimization problem that refers to resource allocation associated with hard and multifaceted constraints in network function virtualization (NFV). Existing works for VNE struggle to handle such complex constraints, leading to compromised system performance and stability. In this paper, we propose a \textbf{CON}straint-\textbf{A}ware \textbf{L}earning framework for VNE, named \textbf{CONAL}, to achieve efficient constraint management. Concretely, we formulate the VNE problem as a constrained Markov decision process with violation tolerance. This modeling approach aims to improve both resource utilization and solution feasibility by precisely evaluating solution quality and the degree of constraint violation. We also propose a reachability-guided optimization with an adaptive reachability budget method that dynamically assigns budget values. This method achieves persistent zero violation to guarantee the feasibility of VNE solutions and more stable policy optimization by handling instances without any feasible solution. Furthermore, we propose a constraint-aware graph representation method to efficiently learn cross-graph relations and constrained path connectivity in VNE. Finally, extensive experimental results demonstrate the superiority of our proposed method over state-of-the-art baselines. Our code is available at https://github.com/GeminiLight/conal-vne.

翻译：虚拟网络嵌入(VNE)是网络功能虚拟化(NFV)中一个具有挑战性的组合优化问题，涉及处理与硬性及多面性约束相关的资源分配。现有VNE方法难以有效处理此类复杂约束，导致系统性能与稳定性受损。本文提出一种面向VNE的约束感知学习框架（CONAL），以实现高效的约束管理。具体而言，我们将VNE问题建模为具有违规容忍度的约束马尔可夫决策过程。该建模方法通过精确评估解的质量与约束违反程度，旨在同时提升资源利用率和解的可行性。我们还提出了一种基于可达性引导的自适应预算优化方法，该方法动态分配预算值，通过处理无可行解实例实现持续零违规，从而保证VNE解的可行性并获得更稳定的策略优化效果。此外，我们提出了一种约束感知图表示方法，用于高效学习VNE中的跨图关系与约束路径连通性。最终，大量实验结果表明，我们所提方法在性能上显著优于现有先进基线方法。代码已开源：https://github.com/GeminiLight/conal-vne。