Understanding the Modeling of Computer Network Delays using Neural Networks

TL;DR

This paper investigates the effectiveness of neural networks in modeling computer network delays based on input traffic, evaluating their accuracy across various network configurations to guide practical modeling approaches.

Contribution

It provides an empirical analysis of neural network-based network delay modeling and offers guidelines for training effective models in different network scenarios.

Findings

Neural networks can accurately predict network delays under certain conditions.

Model accuracy varies with network topology, size, and traffic intensity.

Practical guidelines for training neural network models are proposed.

Abstract

Recent trends in networking are proposing the use of Machine Learning (ML) techniques for the control and operation of the network. In this context, ML can be used as a computer network modeling technique to build models that estimate the network performance. Indeed, network modeling is a central technique to many networking functions, for instance in the field of optimization, in which the model is used to search a configuration that satisfies the target policy. In this paper, we aim to provide an answer to the following question: Can neural networks accurately model the delay of a computer network as a function of the input traffic? For this, we assume the network as a black-box that has as input a traffic matrix and as output delays. Then we train different neural networks models and evaluate its accuracy under different fundamental network characteristics: topology, size, traffic intensity and routing. With this, we aim to have a better understanding of computer network modeling with neural nets and ultimately provide practical guidelines on how such models need to be trained.