Flow Neural Network for Traffic Flow Modelling in IP Networks

Xiangle Cheng, Yuchen He, Feifei Long, Shihan Xiao, Fenglin Li

Keywords: Flow neural network, contrastive induction learning, representation learning, spatio-temporal induction

Abstract: This paper presents and investigates a novel and timely application domain for deep learning: sub-second traffic flow modelling in IP networks. Traffic flows are the most fundamental components in an IP based networking system. The accurate modelling of the generative patterns of these flows is crucial for many practical network applications. However, the high nonlinearity and dynamics of both the traffic and network conditions make this task challenging, particularly at the time granularity of sub-second. In this paper, we cast this problem as a representation learning task to model the intricate patterns in data traffic according to the IP network structure and working mechanism. Accordingly, we propose a customized Flow Neural Network, which works in a self-supervised way to extract the domain-specific data correlations. We report the state-of-the-art performances on both synthetic and realistic traffic patterns on multiple practical network applications, which provides a good testament to the strength of our approach.

One-sentence Summary: We propose a customised Flow Neural Network for the subsecond traffic flow modelling in IP networks by exploiting the domain-specific data properties according to the IP network structure and working machenism.

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