.. virne documentation master file, created by sphinx-quickstart on Fri Feb 24 11:24:46 2023. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. Virne: An NFV-RA Benchmark ========================== **Virne** is a comprehensive 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. .. note:: 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: .. grid:: 2 2 2 4 :gutter: 3 .. grid-item-card:: :class-item: sd-font-weight-bold :class-header: sd-bg-info sd-text-white sd-font-weight-bold :class-card: sd-outline-info sd-rounded-1 Highly Customizable Simulations ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Simulate diverse network environments (e.g., cloud, edge, 5G) with user-defined topologies, resources, and service requirements. .. grid-item-card:: :class-item: sd-font-weight-bold :class-header: sd-bg-success sd-text-white sd-font-weight-bold :class-card: sd-outline-success sd-rounded-1 Extensive Algorithm Library ^^^^^^^^^^^^^^^^^^^^^^^^^^^ Implements 30+ NFV-RA algorithms (exact, heuristics, meta-heuristics, RL-based) in a modular, extensible architecture. .. grid-item-card:: :class-item: sd-font-weight-bold :class-header: sd-bg-primary sd-text-white sd-font-weight-bold :class-card: sd-outline-primary sd-rounded-1 Reinforcement Learning Support ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Provides standardized RL pipelines and Gym-style environments for rapid development and benchmarking of RL-based solutions. .. grid-item-card:: :class-item: sd-font-weight-bold :class-header: sd-bg-warning sd-text-white sd-font-weight-bold :class-card: sd-outline-warning sd-rounded-1 In-depth Evaluation Aspects ^^^^^^^^^^^^^^^^^^^^^^^^^^^ Enables insightful analysis beyond effectiveness, covering practicality perspectives such as solvability, generalization, and scalability. The overall architecture of Virne is illustrated below: .. image:: _static/virne-architecture.png :width: 1000 :alt: Overall Architecture of Virne .. note:: Virne offers a streamlined workflow for supporting comprehensive experimentation of NFV-RA algorithms. (a) customize simulation configurations (b) launch event-driven network system (c) process service requests (d) record results for analysis. Particularly, Virne highlights the support for deep reinforcement learning (RL) algorithms, providing a unified Gym-style environment and RL pipeline. .. image:: _static/virne-rl-support.png :width: 1000 :alt: Unified Gym-style Environment and RL Pipeline in Virne .. note:: The RL pipeline in Virne is designed to be flexible and extensible, allowing researchers to easily integrate their own RL algorithms and environments. Citations --------- ❤️ If you find Virne helpful to your research, please feel free to cite our related papers. Benchmark Paper ~~~~~~~~~~~~~~~ **Virne Benchmark** (`paper `__ & `code `__) .. code-block:: bib @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}, journal={arXiv preprint arXiv:2507.19234}, year={2025}, } Algorithmic Papers ~~~~~~~~~~~~~~~~~~ **[IJCAI-2024] FlagVNE** (`paper `__ & `code `__) .. code-block:: bib @INPROCEEDINGS{tfwang-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 `__) .. code-block:: bib @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 `__) .. code-block:: bib @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}, volume={}, number={}, pages={1-6}, doi={10.1109/ICC42927.2021.9500964} } Indices and tables ------------------ * :ref:`genindex` * :ref:`modindex` * :ref:`search` .. toctree:: :hidden: :maxdepth: 3 :caption: Introduction intro/background intro/formulation intro/framework intro/rl-support .. toctree:: :hidden: :caption: Quick Start start/installation start/running start/simulation .. toctree:: :hidden: :maxdepth: 3 :caption: Solver List solver/overview solver/exact solver/heuristic solver/meta_heuristic solver/learning .. toctree:: :hidden: :caption: Evaluation evaluation/metrics evaluation/aspects .. toctree:: :hidden: :maxdepth: 3 :caption: API Reference api