![[2507.19234] Virne: A Comprehensive Benchmark for RL-based Network Resource Allocation in NFV](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2507.19234] Virne: A Comprehensive Benchmark for RL-based Network Resource Allocation in NFV
Summary
The paper introduces Virne, a benchmarking framework designed for Reinforcement Learning-based resource allocation in Network Function Virtualization, addressing the need for systematic evaluation in this complex domain.
Why It Matters
As NFV becomes increasingly vital for efficient network management, a comprehensive benchmarking framework like Virne is crucial for advancing research and development in RL-based resource allocation methods. It provides a structured approach to evaluate performance and scalability, which is essential for the deployment of robust networking solutions.
Key Takeaways
- Virne offers customizable simulations for various network scenarios, enhancing research in NFV.
- The framework supports over 30 RL methods, promoting diverse approaches to resource allocation.
- In-depth analysis of performance trade-offs provides actionable insights for future research.
- Virne addresses the lack of systematic benchmarking in the field, facilitating consistent evaluations.
- The open-source nature of Virne allows for community collaboration and innovation.
Computer Science > Networking and Internet Architecture arXiv:2507.19234 (cs) [Submitted on 25 Jul 2025 (v1), last revised 16 Feb 2026 (this version, v2)] Title:Virne: A Comprehensive Benchmark for RL-based Network Resource Allocation in NFV Authors:Tianfu Wang, Liwei Deng, Xi Chen, Junyang Wang, Huiguo He, Zhengyu Hu, Wei Wu, Leilei Ding, Qilin Fan, Hui Xiong View a PDF of the paper titled Virne: A Comprehensive Benchmark for RL-based Network Resource Allocation in NFV, by Tianfu Wang and 9 other authors View PDF HTML (experimental) Abstract: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, ...
