Deep Learning-Based Traffic Prediction for Network Optimization

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Abstract
In recent years, researchers realized that the analysis of traffic datasets can reveal valuable information for the management of mobile and metro-core networks. That is getting more and more true with the increase in the use of social media and Internet applications on mobile devices. In this work, we focus on deep learning methods to make prediction of traffic matrices that allow us to proactively optimize the resource allocations of optical backbone networks. Recurrent Neural Networks (RNNs) are designed for sequence prediction problems and they achieved great results in the past years in tasks like speech recognition, handwriting recognition and prediction of time series data. We investigated a particular type of RNN, the Gated Recurrent Units (GRU), able to achieve great accuracy (<; 7.4 of mean absolute error). Then, we used the predictions to dynamically and proactively allocate the resources of an optical network. Comparing numerical results of static vs. dynamic allocation based on predictions, we can estimate a saving of 66.3% of the available capacity in the network, managing unexpected traffic peaks.