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

2025-07-25

AI Summary

Existing NFV resource allocation studies lack a systematic benchmarking framework, leading to inconsistent evaluations and insufficient validation of algorithmic robustness. To address this, we propose Virne—the first deep reinforcement learning (DRL)-oriented benchmark framework specifically designed for NFV. Virne supports customizable network simulations across cloud, edge, and 5G scenarios, featuring a modular and extensible experimental pipeline. It integrates over 30 DRL and classical algorithms and introduces novel multi-dimensional evaluation metrics—including scalability, cross-scenario generalization, efficiency, stability, and deployment adaptability—for the first time. Extensive large-scale experiments uncover fundamental trade-offs among these dimensions. Virne fills a critical gap by providing an open-source, reproducible, and multi-faceted benchmark for NFV resource allocation, serving as a unified testbed and practical guideline for both algorithm development and real-world deployment.

Technology Category

• Search and Optimization: Algorithm Configuration
• Machine Learning: Deep Learning Algorithms
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Lack of systematic benchmarking for NFV resource allocation
• Inconsistent evaluation of deep RL-based RA methods
• Need customizable simulations for diverse network scenarios

Innovation

• Comprehensive benchmarking framework for NFV-RA
• Customizable simulations for diverse network scenarios
• Modular pipeline supporting 30+ methods