Understanding the Modeling of Computer Network Delays using Neural Networks
This work addresses the problem of network performance modeling for network operators and researchers, offering incremental improvements by applying existing neural network methods to a new domain with practical guidelines.
The paper investigates whether neural networks can accurately model computer network delays as a function of input traffic, finding that they achieve high accuracy under various network conditions, with specific models showing errors below 5% in certain scenarios.
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.