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Virne

Documentation | Citations | SDN-NFV Papers

Virne is a simulator designed to address resource allocation problems in network virtualization. This category of problems is often referred to by various names, including:

  • Virtual Network Embedding (VNE)
  • Virtual Network Function Placement (VNF Placement)
  • Service Function Chain Deployment (SFC Deployment)
  • Network Slicing

The main goal of Virne is to provide a unified and flexible framework for solving these problems. Its main characteristics are as follows.

  • Rich Implementations: Provide 20+ solvers, including exact, heuristic, meta-heuristic, and machine learning-based algorithms.
  • Extensible Development: Provide a variety of network topologies, network attributes, and RL environments, which can be easily extended.
  • Light-Weight: Implement concisely with less necessary dependencies, and can be extended easily for specific algorithms.

Virne is still under development. If you have any questions, please open a new issue or contact me via email ([email protected])

  • Completing the documentation
  • Implementing more VNE algorithms

Citations

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

[IJCAI, 2024] FlagVNE (paper & code)

@INPROCEEDINGS{ijcai-2024-flagvne,
  title={FlagVNE: A Flexible and Generalizable RL 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},
  pages={1-6},
  doi={10.1109/ICC42927.2021.9500964}
}

Table of Contents

Quick Start

Installation

Install with pip

pip install virne

Install with script

# only cpu
bash install.sh -c 0

# use cuda (optional version: 10.2, 11.3)
bash install.sh -c 11.3

Minimal Example

from virne.base import BasicScenario
from virne import Config, REGISTRY, Generator, update_simulation_setting


def run(config):
    print(f"\n{'-' * 20}    Start     {'-' * 20}\n")
    # Load solver info: environment and solver class
    solver_info = REGISTRY.get(config.solver_name)
    Env, Solver = solver_info['env'], solver_info['solver']
    print(f'Use {config.solver_name} Solver (Type = {solver_info["type"]})...\n')

    scenario = BasicScenario.from_config(Env, Solver, config)
    scenario.run()

    print(f"\n{'-' * 20}   Complete   {'-' * 20}\n")


if __name__ == '__main__':
    config = Config(
        solver_name='nrm_rank',
        # p_net_setting_path='customized_p_net_setting_file_path',
        # v_sim_setting_path='customized_v_sim_setting_file_path',
    )
    Generator.generate_dataset(config, p_net=False, v_nets=False, save=False)
    run(config)

Supported Features

  • Diverse Network Topologies for Simulation

    • Simple Network Structures: e.g. Star for Centralized Network, Path for Chain-style Network, etc.
    • Random Network Topologies: e.g. Waxman Graph, Edge Probabilistic Connection Graph, etc.
    • Real-world Network Topologies: e.g. Abilene, Geant, etc.

  • Multiple level Attributes for QoS:

    • Graph Level: e.g. the global requirements of user service requests, etc.
    • Node level: e.g. computing resource, server position, energy consumption, etc.
    • Link level: e.g. bandwidth resource, communication delay, etc.

  • Unified Reinforcement Learning Interface for Extension

    • Provide serval RL Environments in gym.Env-style.
    • Implement the many RL training algorithms, including MCTS, PPO, A2C, etc.
    • Support the integration of RL algorithms from other libraries.

  • Various Simulation Scenarios

    • Admission control: Early Reject some not cost-effective service requests.
    • Cloud-Edge: Heteregenous infrastructure with different QoS provision.
    • Time window: Globally process the a batch service requests in a time window.

  • Predefined QoS Awarenesses (Additional Constraints/ Objectives)

    • Position (Node level)
    • Latency (Graph, Node and Link level)
    • Security (Graph, Node and Link level)
    • Congestion (Graph, Node and Link level)
    • Energy (Graph, Node and Link level)
    • Reliability (Graph, Node and Link level)
    • Dynamic (Graph, Node and Link level)
    • Parallelization
    • Privacy

Implemented Algorithms

Virne has implemented the following heuristic-based and learning-based algorithms:

Mapping Strategies

  • Two-Stage
    • In this fromework, the VNE solving process are composed of Node mapping and Edge Mapping.
    • Firstly, the node mapping solution is generate with node mapping algorithm, i.e., Node Ranking
    • Secondly, the BFS algorithm is employed to route the physical link pairs obtained from the node mapping solution.
  • Joint Place and Route
    • The solution of node mapping consists of a sequential placement decision.
    • Simultaneously, the available physical link pairs are routed by BFS algorithm.
  • BFS Trails
    • Based on breadth-first search, it expands the search space by exploiting the awareness of restarts.

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

To-do List

Environment Modeling

  • ADD Scenario Window Batch Processing
  • ADD Environment Check Attributes of p_net and v_net
  • ADD Environment Latency Constraint
  • ADD Controller Check graph constraints
  • ADD Controller Multi-commodity flow
  • ADD Environment QoS level Constraints
  • ADD Recorder Count partial solutions' information
  • ADD Enironment Early rejection (Admission control)
  • ADD Environment Multi-Resources Attributes
  • ADD Environment Position Constraint
  • ADD Recorder Count Running physical network nodes

Algorithm Implementations

Name Command Type Mapping Title Publication Year Note
PG-Conv-QoS-Security pg_cnn_qs learning joint VNE Solution for Network Differentiated QoS and Security Requirements: From the Perspective of Deep Reinforcement Learning arXiv Security
DDPG-Attention* ddpg_attention learning joint A-DDPG: Attention Mechanism-based Deep Reinforcement Learning for NFV IWQOS 2021
MUVINE mu learning joint MUVINE: Multi-stage Virtual Network Embedding in Cloud Data Centers using Reinforcement Learning based Predictions JSAC 2020 Admission Control
TD td learning two-stage VNE-TD: A virtual network embedding algorithm based on temporal-difference learning CN 2019
RNN rnn learning two-stage Boost Online Virtual Network Embedding: Using Neural Networks for Admission Control CNSM 2016 Admission Control