virne

Virne is a simulator for resource allocation problems in network virtualization, mainly for virtual network embedding (VNE). It also is adaptable to VNE's variants, such as service function chain deployment (SFC Deployment), network slicing, etc.

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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)

    • [x] Position (Node level)
    • [x] Latency (Graph, Node and Link level)
    • [x] Security (Graph, Node and Link level)
    • [ ] Congestion (Graph, Node and Link level)
    • [ ] Energy (Graph, Node and Link level)
    • [x] 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
  • [x] ADD Environment Check Attributes of p_net and v_net
  • [x] ADD Environment Latency Constraint
  • [x] ADD Controller Check graph constraints
  • [x] ADD Controller Multi-commodity flow
  • [x] ADD Environment QoS level Constraints
  • [x] ADD Recorder Count partial solutions’ information
  • [x] ADD Enironment Early rejection (Admission control)
  • [x] ADD Environment Multi-Resources Attributes
  • [x] ADD Environment Position Constraint
  • [x] 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