Prediction of Mobile-App Network-Video-Traffic Aggregates using Multi-task Deep Learning

Traffic prediction has proven to be useful for several network management domains and represents one of the main enablers for instilling intelligence within future networks. Recent solutions have focused on predicting the behavior of traffic aggregates. Nonetheless, minimal attempts have tackled the prediction of mobile network traffic generated by different video application categories. To this end, in this work we apply Multi-task Deep Learning to predict network traffic aggregates generated by mobile video applications over short-term time scales. We investigate our approach leveraging state-of-art prediction models such as Convolutional Neural Networks, Gated Recurrent Unit, and Random Forest Regressor, showing some surprising results (e.g. NRMSE < 0.075 for upstream packet count prediction while NRMSE < 0.15 for the downstream counterpart), including some variability in prediction performance among the examined video application categories. Furthermore, we show that using smaller time intervals when predicting traffic aggregates may achieve better performances for specific traffic profiles.

Automated summary

This study applies Multi-task Deep Learning to predict network traffic aggregates generated by mobile video applications, showing variability in prediction performance among different video application categories, and demonstrating that using smaller time intervals can improve performance for specific traffic profiles.