✨ Benchmark Paper   •   Documentation   •   Citations   •   SDN-NFV Papers

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.

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:

• 1️⃣ Highly Customizable Simulations: Simulates diverse network environments (e.g., cloud, edge, 5G), with user-defined topologies, resources, and service requirements.
• 2️⃣ Extensive Algorithm Suite: Registers exact, heuristic, meta-heuristic, and learning-based solvers behind a common interface.
• 3️⃣ Reinforcement Learning Support: Provides standardized RL pipelines and Gym-style environments for rapid development and benchmarking of RL-based solutions.
• 4️⃣ In-depth Evaluation Aspects: Enables insightful analysis beyond effectiveness, covering multiple practicality perspectives (e.g., solvability, generalization, and scalability).

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

[ICLR, 2026] Virne (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},
}

[IJCAI, 2024] FlagVNE (paper & code)

@INPROCEEDINGS{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{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},
}

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

@INPROCEEDINGS{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},
  pages={1-6},
}

• Quickstart
  • Installation
  • Run a Small Experiment
• Implemented Algorithms
  • Learning-based Solvers
  • Meta-heuristics Solvers
  • Heuristics-based Solvers
  • Exact and Rounding Solvers
  • Simple Baseline Solvers

The installation script targets Linux and Python 3.10. Clone the repository, then create and activate a Conda environment:

git clone https://github.com/GeminiLight/virne.git
cd virne
conda create -n virne python=3.10
conda activate virne

Install either the CPU or CUDA 12.4 build:

# CPU-only PyTorch and PyG
bash install.sh -c cpu

# CUDA 12.4 with PyTorch 2.6.0
bash install.sh -c 12.4

Verify the installation from the repository root:

python -c "import virne; print(virne.__version__)"

Use a fast heuristic and ten VN requests for the first run. Calling python main.py without overrides starts the larger default RL experiment.

python main.py \
  solver.solver_name=nrm_rank \
  v_sim_setting.num_v_nets=10 \
  training.use_cuda=false \
  'logger.backends=[console]'

The run finishes with Complete and writes its resolved configuration, summary, and per-event records under:

results/virne/nrm_rank/<run-id>/

See the Quickstart for Hydra overrides and output details, and the solver registry for every valid solver command.

Virne has implemented a rich collection of exact, heuristic, meta-heuristic, and learning-based algorithms for NFV-RA. Some representative algorithms are listed below; see the generated solver registry for every command registered by the current code.

Other Related Papers

• Particle Swarm Optimization
  • Xiang Cheng et al. "Virtual network embedding through topology awareness and optimization". CN, 2012.
  • An Song et al. "A Constructive Particle Swarm Optimizer for Virtual Network Embedding". TNSE, 2020.
• Genetic Algorithm
  • Liu Boyang et al. "Virtual Network Embedding Based on Hybrid Adaptive Genetic Algorithm" In ICCC, 2019.
  • Khoa T.D. Nguyen et al. "An Intelligent Parallel Algorithm for Online Virtual Network Embedding". In CITS, 2019.
  • Khoa Nguyen et al. "Efficient Virtual Network Embedding with Node Ranking and Intelligent Link Mapping". In CloudNet, 2020.
  • Khoa Nguyen et al. "Joint Node-Link Algorithm for Embedding Virtual Networks with Conciliation Strategy". In GLOBECOM, 2021.
• Ant Colony Optimization
  • N/A