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

• URL: http://arxiv.org/abs/2112.12321v3
• Date: Sun, 2 Apr 2023 09:55:37 GMT
• Title: Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks
• Authors: Xiangle Cheng, James He, Shihan Xiao, Yingxue Zhang, Zhitang Chen, Pascal Poupart, Fenglin Li
• Abstract summary: This paper introduces Flow Neural Network (FlowNN) to improve the feature representation with learned physical bias. FlowNN achieves 17% - 71% of loss decrease than the state-of-the-art baselines on both synthetic and real-world networking datasets.
• Score: 31.85361736992165
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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