论文标题
将基于图的深度学习应用于现实的网络方案
Applying Graph-based Deep Learning To Realistic Network Scenarios
论文作者
论文摘要
机器学习的最新进展（ML）表现出了为众多与网络有关的问题构建数据驱动解决方案的巨大潜力。在这种情况下，构建快速准确的网络模型对于实现网络的功能优化工具至关重要。但是，用于网络建模的最先进的基于ML的技术无法提供重要的性能指标的准确估计，例如在现实的网络方案中延迟或抖动，并具有复杂的队列调度配置。本文提出了一个新的基于图的深度学习模型，能够准确估计网络中的每路平均延迟。所提出的模型可以在培训阶段未见的拓扑，路由配置，队列调度策略和交通矩阵上成功概括。
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.
