mlsanityData to 14 Oct 2026

Virne: A Comprehensive Benchmark for RL-based Network Resource Allocation in NFV

arXiv 2507.19234 ↗First posted 25 Jul 2025cs.NI cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Resource allocation (RA) is critical to efficient service deployment in Network Function Virtualization (NFV), a transformative networking paradigm. Recently, deep Reinforcement Learning (RL)-based methods have been showing promising potential to address this complexity. However, the lack of a systematic benchmarking framework and thorough analysis hinders the exploration of emerging networks and the development of more robust algorithms while causing inconsistent evaluation. In this paper, we introduce Virne, a comprehensive benchmarking framework for the NFV-RA problem, with a focus on supporting deep RL-based methods. Virne provides customizable simulations for diverse network scenarios, including cloud, edge, and 5G environments. It also features a modular and extensible implementation pipeline that supports over 30 methods of various types, and includes practical evaluation perspectives beyond effectiveness, such as scalability, generalization, and scalability. Furthermore, we conduct in-depth analysis through extensive experiments to provide valuable insights into performance trade-offs for efficient implementation and offer actionable guidance for future research directions. Overall, with its diverse simulations, rich implementations, and extensive evaluation capabilities, Virne could serve as a comprehensive benchmark for advancing NFV-RA methods and deep RL applications. The code is publicly available at https://github.com/GeminiLight/virne.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 18 of its 83 references that are in the map. First posted on arXiv 25 Jul 2025.

Builds on

18 of 83 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning25 Jun
  2. 2024FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation19 Apr

Other references · 16

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
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  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  4. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  5. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  6. 2019Heterogeneous Graph Attention Networkcited by 734
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  8. 2018Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizoncited by 623
  9. 2022A Review of Safe Reinforcement Learning: Methods, Theory and Applicationscited by 181
  10. 2023Towards Omni-generalizable Neural Methods for Vehicle Routing Problemscited by 69
  11. 2020Online 3D Bin Packing with Constrained Deep Reinforcement Learningcited by 41
  12. 2024MILLION: A General Multi-Objective Framework with Controllable Risk for Portfolio Managementcited by 6
4 more
  1. 2022Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learningcited by 3
  2. 2024COMET: NFT Price Prediction with Wallet Profilingcited by 2
  3. 2024Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural Networkscited by 2
  4. 2021MUVINE: Multi-stage Virtual Network Embedding in Cloud Data Centers using Reinforcement Learning based Predictionscited by 1

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