  We present a study of deep learning applied to the domain of network traffic
data forecasting. This is a very important ingredient for network traffic
engineering, e.g., intelligent routing, which can optimize network performance,
especially in large networks. In a nutshell, we wish to predict, in advance,
the bit rate for a transmission, based on low-dimensional connection metadata
("flows") that is available whenever a communication is initiated. Our study
has several genuinely new points: First, it is performed on a large dataset
(~50 million flows), which requires a new training scheme that operates on
successive blocks of data since the whole dataset is too large for in-memory
processing. Additionally, we are the first to propose and perform a more
fine-grained prediction that distinguishes between low, medium and high bit
rates instead of just "mice" and "elephant" flows. Lastly, we apply
state-of-the-art visualization and clustering techniques to flow data and show
that visualizations are insightful despite the heterogeneous and non-metric
nature of the data. We developed a processing pipeline to handle the highly
non-trivial acquisition process and allow for proper data preprocessing to be
able to apply DNNs to network traffic data. We conduct DNN hyper-parameter
optimization as well as feature selection experiments, which clearly show that
fine-grained network traffic forecasting is feasible, and that domain-dependent
data enrichment and augmentation strategies can improve results. An outlook
about the fundamental challenges presented by network traffic analysis (high
data throughput, unbalanced and dynamic classes, changing statistics, outlier
detection) concludes the article.
