A Comprehensive Simulator & Benchmark for NFV-RA

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

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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
  • Running Examples
• Implemented Algorithms
  • Exact Algorithms
  • Heuristic Algorithms
  • Meta-Heuristic Algorithms
  • Learning-Based Algorithms

Quick Start

Installation

1. Create a new conda environment

conda create -n virne python=3.10
conda activate virne

2. 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

2. 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