  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.
