  Large content providers and content distribution network operators usually
connect with large Internet service providers (eyeball networks) through
dedicated private peering. The capacity of these private network interconnects
is provisioned to match the volume of the real content demand by the users.
Unfortunately, in case of a surge in traffic demand, for example due to a
content trending in a certain country, the capacity of the private interconnect
may deplete and the content provider/distributor would have to reroute the
excess traffic through transit providers. Although, such overflow events are
rare, they have significant negative impacts on content providers, Internet
service providers, and end-users. These include unexpected delays and
disruptions reducing the user experience quality, as well as direct costs paid
by the Internet service provider to the transit providers. If the traffic
overflow events could be predicted, the Internet service providers would be
able to influence the routes chosen for the excess traffic to reduce the costs
and increase user experience quality. In this article we propose a method based
on an ensemble of deep learning models to predict overflow events over a short
term horizon of 2-6 hours and predict the specific interconnections that will
ingress the overflow traffic. The method was evaluated with 2.5 years' traffic
measurement data from a large European Internet service provider resulting in a
true-positive rate of 0.8 while maintaining a 0.05 false-positive rate. The
lockdown imposed by the COVID-19 pandemic reduced the overflow prediction
accuracy. Nevertheless, starting from the end of April 2020 with the gradual
lockdown release, the old models trained before the pandemic perform equally
well.
