Virne: A Comprehensive Benchmark for RL-based Network Resource Allocation in NFV
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911450471071744 |
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| author | Wang, Tianfu Deng, Liwei Chen, Xi Wang, Junyang He, Huiguo Hu, Zhengyu Wu, Wei Ding, Leilei Fan, Qilin Xiong, Hui |
| author_facet | Wang, Tianfu Deng, Liwei Chen, Xi Wang, Junyang He, Huiguo Hu, Zhengyu Wu, Wei Ding, Leilei Fan, Qilin Xiong, Hui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19234 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Virne: A Comprehensive Benchmark for RL-based Network Resource Allocation in NFV Wang, Tianfu Deng, Liwei Chen, Xi Wang, Junyang He, Huiguo Hu, Zhengyu Wu, Wei Ding, Leilei Fan, Qilin Xiong, Hui Networking and Internet Architecture Artificial Intelligence 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. |
| title | Virne: A Comprehensive Benchmark for RL-based Network Resource Allocation in NFV |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2507.19234 |