FLOW NEURAL NETWORK FOR TRAFFIC FLOW MOD-ELLING IN IP NETWORKS

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.

1. INTRODUCTION

Deep Learning (DL) has gained substantial popularity in light of its applicability to real-world tasks across computer vision, natural language processing (Goodfellow et al., 2016) , protein structure prediction (Senior et al., 2020) and challenging games such as Go (Silver et al., 2017) . Typically, the data for these learning tasks takes the form of either grids, sequences, graphs or their combinations. The tremendous efforts on customizing neural network structures (Krizhevsky et al., 2012; Kiros et al., 2015; Hochreiter & Schmidhuber, 1997) and learning strategies (Sermanet et al., 2018; Oord et al., 2019) to explore the data-specific properties underpin the success of modern DL in these domains. Following the same design philosophy, we wish to capitalize on these advancements to develop a customized neural network and self-supervised learning strategy to tackle the crucial and timely challenge of traffic flow modelling in IP networks.

1.1. TRAFFIC FLOW MODELLING IN IP NETWORKS

An IP network is a communication network that uses Internet Protocol (IP) to send and receive messages between one or more devices such as computers, mobile phones. The messages could be general application data such as video, emails or control signals of any connected devices. When sending the messages from a source to a destination, the source device encapsulates the bit chunks of encoded messages into a set of IP packets. The packets then travel through communications links and routers or switches in a given routing path sequentially, thus forming the traffic flows in an IP network (Hunt, 1992) . As one of the most commonly used global networks, the IP network provides the majority of such data transmission services to support today's Internet applications such as video streaming, voice-over-IP, and Internet of Things. Therefore, a good understanding of the behaviorial patterns of the underlying traffic flows plays a crucial role in network planning, traffic management, as well as optimizing Quality of Service (QoS, e.g., transmission rate, delay). This challenge is termed as traffic flow modelling and is fundamental to IP networking research and practice. However, the high nonlinearity, randomness and complicated self similarity (Leland et al., 1994) of these traffic thwart extensive traditional analytical and learning models, particularly at fine-grained time scales, such as traffic flow modelling at a sub-second level. Consider the illustrative example in Fig. 1 , which depicts multiple packet flows with shared forwarding nodes and links in their routing paths. The sender of each flows streams data packets to the 1

