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

2025/07/25 by Tianfu Wang, Wang, Tianfu, Liwei Deng +14
Computer Science · #Artificial Intelligence (cs.AI) #Cloud Computing and Resource Management #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Networking and Internet Architecture (cs.NI) #Software-Defined Networks and 5G

paper · pdf · doi:10.48550/arxiv.2507.19234

openalex publication_date 2025/07/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

Abstract

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.

Citations

• MILLION: A General Multi-Objective Framework with Controllable Risk for Portfolio Management
• COMET: NFT Price Prediction with Wallet Profiling
• FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
• Towards Omni-generalizable Neural Methods for Vehicle Routing Problems
• A Review of Safe Reinforcement Learning: Methods, Theory and Applications
• Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning
• Online 3D Bin Packing with Constrained Deep Reinforcement Learning
• Automatic Virtual Network Embedding: A Deep Reinforcement Learning Approach With Graph Convolutional Networks
• Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon
• Machine learning for combinatorial optimization: A methodological tour d’horizon
• Attention, Learn to Solve Routing Problems!
• Proximal Policy Optimization Algorithms
• Semi-Supervised Classification with Graph Convolutional Networks
• Asynchronous Methods for Deep Reinforcement Learning
• Sequence to Sequence Learning with Neural Networks
• Playing Atari with Deep Reinforcement Learning
• ImageNet classification with deep convolutional neural networks
• Routing of multipoint connections
• Towards Constraint-aware Learning for Resource Allocation in NFV Networks

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