Abstract
Machine Learning (ML)-based network models provide fast and accurate predictions for complex network behaviors but require substantial training data. Collecting such data from real networks is often costly and limited, especially for critical scenarios like failures. As a result, researchers commonly rely on simulated data, which reduces accuracy when models are deployed in real environments. We propose a hybrid approach leveraging transfer learning to combine simulated and real-world data. Using RouteNet-Fermi, we show that fine-tuning a pre-trained model with a small real dataset significantly improves performance. Our experiments with OMNeT++ and a custom testbed reduce the Mean Absolute Percentage Error (MAPE) in packet delay prediction by up to 88%. With just 10 real scenarios, MAPE drops by 37%, and with 50 scenarios, by 48%.
Abstract (translated)
基于机器学习（ML）的网络模型能够提供复杂网络行为的快速且准确预测，但需要大量的训练数据。从实际网络中收集这些数据通常成本高昂并且受限，尤其是在像故障这样的关键场景下更是如此。因此，研究人员常常依赖于模拟数据，然而当这些模型部署到真实环境中时，其准确性会有所降低。我们提出了一种混合方法，利用迁移学习结合模拟和现实世界的数据。通过RouteNet-Fermi，我们展示了使用少量的真实数据集对预训练模型进行微调可以显著提高性能。我们在OMNeT++和自定义测试平台上进行的实验表明，在包延迟预测中平均绝对百分比误差（MAPE）最多可降低88%。仅用10个真实场景，MAPE就降低了37%，而使用50个场景则下降了48%。
URL
https://arxiv.org/abs/2510.00956
