virne

Virne is a simulator & benchmark for resource allocation (RA) problems in network function virtualization (NFV), i.e., NFV-RA, including virtual network embedding, service function chain deployment, network slicing, etc.

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A Comprehensive Simulator & Benchmark for NFV-RA

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

Virne is a 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.

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

[!IMPORTANT]
🎉 We have released the benchmark paper of Virne. Welcome to check it out!

✨ If you have any questions, please open a new issue or contact me via email (wtfly2018@gmail.com)

Citations

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

Benchmark Paper

[arXiv, 2025] Virne

@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},
  year={2025},
}

Algorithmic Papers

[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},
}

Table of Contents

Quick Start

Installation

  1. Create a new conda environment
conda create -n virne python=3.10
conda activate virne
  1. Install with script
# use cpu
bash install.sh -c 0

# use cuda (only support cuda=12.4 and torch=2.6.0)
bash install.sh -c 12.4

Running Examples

  1. Run the default example

Before running the example, you could update the configuration file in settings/ directory to set the parameters on simulation and algorithm.

python main.py
  1. Run with custom configuration

Virne is built on Hydra, which allows you to override configuration parameters directly from the command line.

python main.py CONFIG_NAME=NEW_VALUE

Some examples of command line arguments are:

# Run with a specific nfv-ra algorithm
python main.py solver.solver_name=nrm_rank

# Run with a specific physical topology
python main.py p_net_setting.topology.file_path=../../datasets/topology/Geant.gml

# Run with a specific network system
python main.py system.if_offline_system=true

Implemented Algorithms

Virne has implemented the rich heuristic-based and learning-based algorithms for NFV-RA. Some of them are listed in the following tables.

Learning-based Solvers

Name Command Type Mapping Title Publication Year Note
PG-CNN2 pg_cnn2 learning two-stage A Virtual Network EmbeddingAlgorithm Based On Double-LayerReinforcement Learning The Computer Journal 2022
A3C-G3C-Seq2Seq* a3c_gcn_seq2seq learning joint_pr DRL-SFCP: Adaptive Service Function Chains Placement with Deep Reinforcement Learning ICC 2021
PG-CNN-QoS pg_cnn_qos learning two-stage Resource Management and Security Scheme of ICPSs and IoT Based on VNE Algorithm IoTJ 2021
PG-Seq2Seq pg_seq2seq learning joint_pr A Continuous-Decision Virtual Network Embedding Scheme Relying on Reinforcement Learning TNSM 2020
GAE-Clustering gae_clustering learning bfs_trials Accelerating Virtual Network Embedding with Graph Neural Networks CNSM 2020 Clustering
PG-MLP pg_mlp learning joint_pr NFVdeep: adaptive online service function chain deployment with deep reinforcement learning. IWQOS 2019
Hopfield-Network hopfield_network learning two-stage NeuroViNE: A Neural Preprocessor for Your Virtual Network Embedding Algorithm INFOCOM 2018 Subgraph Extraction
PG-CNN pg_cnn learning two-stage A Novel Reinforcement Learning Algorithm for Virtual Network Embedding Neurocomputing 2018
MCTS mcts learning two-stage Virtual Network Embedding via Monte Carlo Tree Search TCYB 2018 MultiThreading Support

* means that the algorithm only supports chain-shape virtual networks embedding

Meta-heuristics Solvers

Name Command Type Mapping Title Publication Year Note
NodeRanking-MetaHeuristic **_** meta-heuristics joint Virtual network embedding through topology awareness and optimization CN 2012 MultiThreading Support
Genetic-Algorithm ga meta-heuristics two-stage Virtual network embedding based on modified genetic algorithm Peer-to-Peer Networking and Applications 2019 MultiThreading Support
Tabu-Search ts meta-heuristics joint Virtual network forwarding graph embedding based on Tabu Search WCSP 2017 MultiThreading Support
ParticleSwarmOptimization pso meta-heuristics two-stage Energy-Aware Virtual Network Embedding TON 2014 MultiThreading Support
Ant-Colony-Optimization aco meta-heuristics joint Link mapping-oriented ant colony system for virtual network embedding CEC 2017 MultiThreading Support
AntColony-Optimization aco meta-heuristics joint VNE-AC: Virtual Network Embedding Algorithm Based on Ant Colony Metaheuristic ICC 2011 MultiThreading Support
Simulated-Annealing sa meta-heuristics two-stage FELL: A Flexible Virtual Network Embedding Algorithm with Guaranteed Load Balancing ICC 2011 MultiThreading Support

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

Heuristics-based Solvers

Name Command Type Mapping Title Publication Year Note
PL (Priority of Location) pl_rank heuristics two-stage Efficient Virtual Network Embedding of Cloud-Based Data Center Networks into Optical Networks TPDS 2021
NRM (Node Resource Management) nrm_rank heuristics two-stage Virtual Network Embedding Based on Computing, Network, and Storage Resource Constraints IoTJ 2018
GRC (Global resource capacity) grc_rank heuristics two-stage Toward Profit-Seeking Virtual Network Embedding Algorithm via Global Resource Capacity INFOCOM 2014
RW-MaxMatch (NodeRank) rw_rank heuristics two-stage Virtual Network Embedding Through Topology-Aware Node Ranking ACM SIGCOMM Computer Communication Review 2011
RW-BFS (NodeRank) rw_rank_bfs heuristics bfs_trials Virtual Network Embedding Through Topology-Aware Node Ranking ACM SIGCOMM Computer Communication Review 2011

Exact Solvers

Name Command Type Mapping Title Publication Year Note
MIP (Mixed-Integer Programming) mip exact joint ViNEYard: Virtual Network Embedding Algorithms With Coordinated Node and Link Mapping TON 2012
D-Rounding (Deterministic Rounding) d_rounding exact joint ViNEYard: Virtual Network Embedding Algorithms With Coordinated Node and Link Mapping TON 2012
R-Rounding (Random Rounding) r_rounding exact joint ViNEYard: Virtual Network Embedding Algorithms With Coordinated Node and Link Mapping TON 2012

Simple Baseline Solvers

Name Command Mapping
Random Rank random_rank two-stage
Random Joint Place and Route random_joint_pr joint_pr
Random Rank Breath First Search random_bfs_trials bfs_trials
Order Rank order_rank two-stage
Order Joint Place and Route order_joint_pr joint_pr
Order Rank Breath First Search order_bfs_trials bfs_trials
First Fit Decreasing Rank ffd_rank two-stage
First Fit Decreasing Joint Place and Route ffd_joint_pr joint_pr
First Fit Decreasing Rank Breath First Search ffd_bfs_trials bfs_trials