Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks

TL;DR

This paper introduces FlowNN, a physics-constrained neural network model that improves short-term network flow predictions by embedding physical principles, outperforming existing models on synthetic and real data.

Contribution

FlowNN incorporates physics-based biases into neural network architecture for enhanced short-term network flow prediction, a novel approach in this domain.

Findings

FlowNN reduces prediction loss by 17%-71% compared to baselines.

It effectively captures physical correlations in network data.

Demonstrates superior performance on both synthetic and real datasets.

Abstract

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.