Network simulation is pivotal in network modeling, assisting with tasks ranging from capacity planning to performance estimation. Traditional approaches such as Discrete Event Simulation (DES) face limitations in terms of computational cost and accuracy. This paper introduces RouteNet-Gauss, a novel integration of a testbed network with a Machine Learning (ML) model to address these challenges. By using the testbed as a hardware accelerator, RouteNet-Gauss generates training datasets rapidly and simulates network scenarios with high fidelity to real-world conditions. Experimental results show that RouteNet-Gauss significantly reduces prediction errors by up to 95% and achieves a 488x speedup in inference time compared to state-of-the-art DES-based methods. RouteNet-Gauss's modular architecture is dynamically constructed based on the specific characteristics of the network scenario, such as topology and routing. This enables it to understand and generalize to different network configurations beyond those seen during training, including networks up to 10x larger. Additionally, it supports Temporal Aggregated Performance Estimation (TAPE), providing configurable temporal granularity and maintaining high accuracy in flow performance metrics. This approach shows promise in improving both simulation efficiency and accuracy, offering a valuable tool for network operators.

翻译：网络仿真在网络建模中至关重要，可辅助完成从容量规划到性能估计等多种任务。传统方法如离散事件仿真（DES）在计算成本与精度方面存在局限。本文提出RouteNet-Gauss，通过将测试床网络与机器学习模型创新性结合以应对这些挑战。该模型利用测试床作为硬件加速器，能够快速生成训练数据集，并以高度逼近真实环境的方式模拟网络场景。实验结果表明，相较于最先进的基于DES的方法，RouteNet-Gauss将预测误差显著降低达95%，推理速度提升达488倍。RouteNet-Gauss采用模块化架构，可根据网络场景的具体特征（如拓扑结构与路由策略）动态构建模型，从而使其能够理解并泛化至训练数据未涵盖的不同网络配置，包括规模扩大10倍的网络。此外，该模型支持时序聚合性能估计（TAPE），提供可配置的时间粒度，并在流性能指标中保持高精度。该方法在提升仿真效率与精度方面展现出潜力，为网络运营商提供了有价值的工具。