Machine learning is gaining growing momentum in various recent models for the dynamic analysis of information flows in data communications networks. These preliminary models often rely on off-the-shelf learning models to predict from historical statistics while disregarding the physics governing the generating behaviors of these flows. This paper instead introduces Flow Neural Network (FlowNN) to improve the feature representation with learned physical bias. This is implemented by an induction layer, working upon the embedding layer, to impose the physics connected data correlations, and a self-supervised learning strategy with stop-gradient to make the learned physics universal. For the short-timescale network prediction tasks, FlowNN achieves 17% - 71% of loss decrease than the state-of-the-art baselines on both synthetic and real-world networking datasets, which shows the strength of this new approach.
翻译:机器学习在数据通信网络的动态信息分析中越来越流行。这些初步模型通常依赖于现成的学习模型,以历史统计量预测,而不考虑生成这些流的物理学规律。本文介绍了流神经网络(FlowNN),通过学习到的物理偏差改进特征表示。这通过感应层在嵌入层上工作实现,以强加物理相关的数据相关性,并采用停梯度的自监督学习策略使所学习的物理普遍适用。对于短时网络预测任务,FlowNN在合成和真实世界的网络数据集上的损失下降率比最先进的基准线提高了17%-71%,显示了这种新方法的优势。