{"ID":2890497,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.19234","arxiv_id":"2507.19234","title":"Virne: A Comprehensive Benchmark for RL-based Network Resource Allocation in NFV","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, 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.","short_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 f...","url_abs":"https://arxiv.org/abs/2507.19234","url_pdf":"https://arxiv.org/pdf/2507.19234v2","authors":"[\"Tianfu Wang\",\"Liwei Deng\",\"Xi Chen\",\"Junyang Wang\",\"Huiguo He\",\"Zhengyu Hu\",\"Wei Wu\",\"Leilei Ding\",\"Qilin Fan\",\"Hui Xiong\"]","published":"2025-07-25T12:58:32Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.AI\"]","methods":"[\"Reinforcement Learning\",\"LoRA\"]","has_code":false,"code_links":[{"ID":611789,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2890497,"paper_url":"https://arxiv.org/abs/2507.19234","paper_title":"Virne: A Comprehensive Benchmark for RL-based Network Resource Allocation in NFV","repo_url":"https://github.com/GeminiLight/virne","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}