用真实硬件加速机器学习，让网络模拟快100倍且更准
RouteNet-Gauss: Hardware-Enhanced Network Modeling with Machine Learning
- 用真实网络设备生成训练数据，结合机器学习快速建模
- 预测误差降低95%，推理速度提升488倍，支持超大网络
- 模块化设计可适配不同拓扑，适合网络规划与性能评估
网络仿真在容量规划和性能评估中至关重要。传统离散事件仿真（DES）存在计算成本高、精度不足的问题。本文提出RouteNet-Gauss，将真实测试床网络与机器学习模型结合，利用硬件加速快速生成训练数据，并实现高保真度的网络场景仿真。实验表明，该方法预测误差最多降低95%，推理时间相比最先进的基于DES的方法提速488倍。其模块化架构可根据网络拓扑和路由特性动态构建，能泛化到训练中未见的配置，包括规模达10倍更大的网络。此外，支持时间聚合性能估计（TAPE），可灵活配置时间粒度，同时保持流性能指标的高精度。该方法显著提升了仿真效率与准确性，为网络运营商提供有力工具。
原文摘要 · Abstract (English)
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.
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