TAILING: Tail Distribution Forecasting of Packet Delays using Quantile Regression Neural Networks


Major building blocks of communication networks such as flow control and congestion control rely on fresh estimates of the network state to control data traffic injection into the network. These measured metrics are usually implicitly considered as estimates of the future network state until updated. In this paper, we propose to directly and explicitly estimate packet-based predictive QoS metrics from network measurements. As many applications possess strict QoS requirements, we focus here on bounding packet delay quantiles. Our approach is based on training neural networks to predict the quantile of the delay distribution observed by future packets given some observations of packet delays. We validate our approach through recovering classical closed-form delay quantiles that are obtained from analytical models of simple queueing systems. We show that our approach goes beyond these simple models in that it provides quantile estimates for complex scenarios and under various traffic patterns including empirical data traffic traces.