Understanding the modeling of computer network delays using neural networks

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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.

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Mestres, A., Alarcón, E., Ji, Y., & Cabellos-Aparicio, A. (2018). Understanding the modeling of computer network delays using neural networks. In Big-DAMA 2018 - Proceedings of the 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Part of SIGCOMM 2018 (pp. 46–52). Association for Computing Machinery, Inc. https://doi.org/10.1145/3229607.3229613

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