Recent advances in Machine Learning (ML) have shown a great potential to build data-driven solutions for a plethora of network-related problems. In this context, building fast and accurate network models is essential to achieve functional optimization tools for networking. However, state-of-the-art ML-based techniques for network modelling are not able to provide accurate estimates of important performance metrics such as delay or jitter in realistic network scenarios with sophisticated queue scheduling configurations. This paper presents a new Graph-based deep learning model able to estimate accurately the per-path mean delay in networks. The proposed model can generalize successfully over topologies, routing configurations, queue scheduling policies and traffic matrices unseen during the training phase.

翻译：机器学习(ML)的最新进展显示,为大量与网络有关的问题建立数据驱动的解决方案具有巨大潜力,在这方面,建立快速准确的网络模型对于实现网络化功能优化工具至关重要,然而,基于网络建模的最新ML技术无法准确估计重要性能指标,如在具有复杂的排队排队安排配置的现实网络情景中出现延迟或紧张等。本文展示了一个新的基于图表的深层次学习模型,能够准确估计网络的人均平均延迟。拟议的模型可以成功地概括在培训阶段所见的地形、路由配置、排队排队排队政策和交通信息总库。