Virne: An NFV-RA Benchmark

Virne is a comprehensive simulator and benchmark designed to address resource allocation (RA) problems in network function virtualization (NFV), with a highlight on supporting reinforcement learning (RL)-based algorithms.

Note

In the literature, RA in NFV is often termed Virtual Network Embedding (VNE), Virtual Network Function (VNF) placement, service function chain (SFC) deployment, or network slicing in 5G.

Virne offers a unified and comprehensive framework for NFV-RA, with the following key features:

Highly Customizable Simulations

Simulate diverse network environments (e.g., cloud, edge, 5G) with user-defined topologies, resources, and service requirements.

Extensive Algorithm Library

Implements 30+ NFV-RA algorithms (exact, heuristics, meta-heuristics, RL-based) in a modular, extensible architecture.

Reinforcement Learning Support

Provides standardized RL pipelines and Gym-style environments for rapid development and benchmarking of RL-based solutions.

In-depth Evaluation Aspects

Enables insightful analysis beyond effectiveness, covering practicality perspectives such as solvability, generalization, and scalability.

The overall architecture of Virne is illustrated below:

Overall Architecture of Virne

Note

Virne offers a streamlined workflow for supporting comprehensive experimentation of NFV-RA algorithms. (a) customize simulation configurations (b) launch event-driven network system (c) process service requests (d) record results for analysis.

Particularly, Virne highlights the support for deep reinforcement learning (RL) algorithms, providing a unified Gym-style environment and RL pipeline.

Unified Gym-style Environment and RL Pipeline in Virne

Note

The RL pipeline in Virne is designed to be flexible and extensible, allowing researchers to easily integrate their own RL algorithms and environments.

Citations

❤️ If you find Virne helpful to your research, please feel free to cite our related papers.

Benchmark Paper

Virne Benchmark (paper & code)

@article{tfwang-2025-virne,
  title={Virne: A Comprehensive Benchmark for Deep RL-based Network Resource Allocation in NFV},
  author={Wang, Tianfu and Deng, Liwei and Chen, Xi and Wang, Junyang and He, Huiguo and Ding, Leilei and Wu, Wei and Fan, Qilin and Xiong, Hui},
  journal={arXiv preprint arXiv:2507.19234},
  year={2025},
}

Algorithmic Papers

[IJCAI-2024] FlagVNE (paper & code)

@INPROCEEDINGS{tfwang-ijcai-2024-flagvne,
  title={FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation},
  author={Wang, Tianfu and Fan, Qilin and Wang, Chao and Ding, Leilei and Yuan, Nicholas Jing and Xiong, Hui},
  booktitle={Proceedings of the 33rd International Joint Conference on Artificial Intelligence},
  year={2024},
}

[TSC-2023] HRL-ACRA (paper & code)

@ARTICLE{tfwang-tsc-2023-hrl-acra,
  author={Wang, Tianfu and Shen, Li and Fan, Qilin and Xu, Tong and Liu, Tongliang and Xiong, Hui},
  journal={IEEE Transactions on Services Computing},
  title={Joint Admission Control and Resource Allocation of Virtual Network Embedding Via Hierarchical Deep Reinforcement Learning},
  volume={17},
  number={03},
  pages={1001--1015},
  year={2024},
  doi={10.1109/TSC.2023.3326539}
}

[ICC-2021] DRL-SFCP (paper & code)

@INPROCEEDINGS{tfwang-icc-2021-drl-sfcp,
  author={Wang, Tianfu and Fan, Qilin and Li, Xiuhua and Zhang, Xu and Xiong, Qingyu and Fu, Shu and Gao, Min},
  booktitle={ICC 2021 - IEEE International Conference on Communications},
  title={DRL-SFCP: Adaptive Service Function Chains Placement with Deep Reinforcement Learning},
  year={2021},
  volume={},
  number={},
  pages={1-6},
  doi={10.1109/ICC42927.2021.9500964}
}

Indices and tables