Virne: An NFV-RA Benchmark

Virne is a simulator and benchmark for resource allocation (RA) in Network Functions Virtualisation (NFV), with unified support for traditional and 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.

Start Here

Virne provides the following core capabilities:

Highly Customizable Simulations

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

Extensive Algorithm Library

Registers exact, heuristic, meta-heuristic, and learning-based solvers behind a common interface.

Reinforcement Learning Support

Provides standardized RL pipelines and Gymnasium-compatible 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

Virne connects configurable simulations, shared feasibility checks, solver families, and experiment recording.

Note

A Virne experiment has four steps: configure the simulation, launch the event-driven system, process service requests, and record results.

Virne also provides a unified environment and training pipeline for deep RL algorithms.

Unified Gymnasium-compatible environment and RL pipeline in Virne

A shared Gymnasium-compatible environment connects observations, policies, rollouts, training, and evaluation.

Citations

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

Benchmark Paper

[ICLR, 2026] Virne Benchmark (paper)

@inproceedings{tfwang-2026-virne,
  title={Virne: A Comprehensive Benchmark for RL-based Network Resource Allocation in NFV},
  author={Wang, Tianfu and Deng, Liwei and Chen, Xi and Wang, Junyang and He, Huiguo and Hu, Zhengyu and Wu, Wei and Ding, Leilei and Fan, Qilin and Xiong, Hui},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026},
}

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}
}