Machine learning

Network topology

Context (archaeology)

Artificial intelligence

Time delay neural network

Biology

Network packet

Probabilistic neural network

Network simulation

Network traffic control

Computer science

Distributed computing

Network traffic simulation

Nervous system network models

Computer network

Paleontology

Traffic generation model

Artificial neural network

Black box

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.

Understanding the Modeling of Computer Network Delays using Neural Networks