  Wide area networking infrastructures (WANs), particularly science and
research WANs, are the backbone for moving large volumes of scientific data
between experimental facilities and data centers. With demands growing at
exponential rates, these networks are struggling to cope with large data
volumes, real-time responses, and overall network performance. Network
operators are increasingly looking for innovative ways to manage the limited
underlying network resources. Forecasting network traffic is a critical
capability for proactive resource management, congestion mitigation, and
dedicated transfer provisioning. To this end, we propose a nonautoregressive
graph-based neural network for multistep network traffic forecasting.
Specifically, we develop a dynamic variant of diffusion convolutional recurrent
neural networks to forecast traffic in research WANs. We evaluate the efficacy
of our approach on real traffic from ESnet, the U.S. Department of Energy's
dedicated science network. Our results show that compared to classical
forecasting methods, our approach explicitly learns the dynamic nature of
spatiotemporal traffic patterns, showing significant improvements in
forecasting accuracy. Our technique can surpass existing statistical and deep
learning approaches by achieving approximately 20% mean absolute percentage
error for multiple hours of forecasts despite dynamic network traffic settings.
