Resource allocation (RA) is critical to efficient service deployment in Network Function Virtualization (NFV), a transformative networking paradigm. Recently, deep Reinforcement Learning (RL)-based methods have been showing promising potential to address this complexity. However, the lack of a systematic benchmarking framework and thorough analysis hinders the exploration of emerging networks and the development of more robust algorithms while causing inconsistent evaluation. In this paper, we introduce Virne, a comprehensive benchmarking framework for the NFV-RA problem, with a focus on supporting deep RL-based methods. Virne provides customizable simulations for diverse network scenarios, including cloud, edge, and 5G environments. It also features a modular and extensible implementation pipeline that supports over 30 methods of various types, and includes practical evaluation perspectives beyond effectiveness, such as scalability, generalization, and scalability. Furthermore, we conduct in-depth analysis through extensive experiments to provide valuable insights into performance trade-offs for efficient implementation and offer actionable guidance for future research directions. Overall, with its diverse simulations, rich implementations, and extensive evaluation capabilities, Virne could serve as a comprehensive benchmark for advancing NFV-RA methods and deep RL applications. The code is publicly available at https://github.com/GeminiLight/virne.

翻译：资源分配（RA）是网络功能虚拟化（NFV）这一变革性网络范式中实现高效服务部署的关键。近年来，基于深度强化学习（RL）的方法在应对这一复杂性方面展现出巨大潜力。然而，由于缺乏系统化的基准测试框架和深入分析，不仅阻碍了对新兴网络的探索和更鲁棒算法的开发，还导致了评估结果的不一致。本文介绍了Virne，一个针对NFV-RA问题的综合性基准测试框架，重点支持基于深度RL的方法。Virne为多样化网络场景（包括云、边缘和5G环境）提供可定制的模拟。它还具备模块化、可扩展的实现流程，支持超过30种不同类型的方法，并包含超越有效性的实用评估视角，如可扩展性、泛化能力和效率。此外，我们通过大量实验进行深入分析，为高效实现提供有关性能权衡的宝贵见解，并为未来研究方向提供可操作的指导。总体而言，凭借其多样化的模拟、丰富的实现和广泛的评估能力，Virne可作为推进NFV-RA方法和深度RL应用的综合基准。代码公开于 https://github.com/GeminiLight/virne。