Towards a generic deep learning pipeline for traffic measurements

As networks grow bigger, traffic measurements become more and more challenging. Common practices require specialized solutions tied to specific measurements. We aim at automating the design of generic top-down measurements tools thanks to Deep Learning. To this end, we focus our study on ($i$) researching an appropriate input traffic representation and ($ii$) comparing Deep Learning pipelines for several measurements. In this paper, we propose an empirical campaign to study a variety of modeling approaches for multiple traffic metrics predictions, with a strong focus on the trade-off between performance and cost that these approaches offer.