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Large content providers and content distribution network operators usuallyconnect with large Internet service providers (eyeball networks) throughdedicated private peering. The capacity of these private network interconnectsis 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 acontent trending in a certain country, the capacity of the private interconnectmay deplete and the content provider/distributor would have to reroute theexcess traffic through transit providers. Although, such overflow events arerare, they have significant negative impacts on content providers, Internetservice providers, and end-users. These include unexpected delays anddisruptions reducing the user experience quality, as well as direct costs paidby the Internet service provider to the transit providers. If the trafficoverflow events could be predicted, the Internet service providers would beable to influence the routes chosen for the excess traffic to reduce the costsand increase user experience quality. In this article we propose a method basedon an ensemble of deep learning models to predict overflow events over a shortterm horizon of 2-6 hours and predict the specific interconnections that willingress the overflow traffic. The method was evaluated with 2.5 years' trafficmeasurement data from a large European Internet service provider resulting in atrue-positive rate of 0.8 while maintaining a 0.05 false-positive rate. Thelockdown imposed by the COVID-19 pandemic reduced the overflow predictionaccuracy. Nevertheless, starting from the end of April 2020 with the graduallockdown release, the old models trained before the pandemic perform equallywell.

Predicting traffic overflows on private peering