Towards Constraint-aware Learning for Resource Allocation in NFV-enabled Networks

Tianfu Wang, Long Yang, Chao Wang, Chuan Qin, Liwei Deng, Li Shen, Hui Xiong

Keywords: Network Resource Allocation; Combinatorial Optimization; Reinforcement Learning; Graph Neural Network

TL;DR: We study a crucial network resource allocation problem in network function virtualization, known as Virtual Network Embedding (VNE), and propose a CONstraint-Aware Learning framework to enhance constraint management, named CONAL.

Abstract: 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 \href{https://anonymous.4open.science/r/iclr25-conal}{https://anonymous.4open.science/r/iclr25-conal}.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 5553