  Network utilisation efficiency can, at least in principle, often be improved
by dynamically re-configuring routing policies to better distribute on-going
large data transfers. Unfortunately, the information necessary to decide on an
appropriate reconfiguration - details of on-going and upcoming data transfers
such as their source and destination and, most importantly, their volume and
duration - is usually lacking. Fortunately, the increased use of scheduled
transfer services, such as FTS, makes it possible to collect the necessary
information. However, the mere detection and characterisation of larger
transfers is not sufficient to predict with confidence the likelihood a network
link will become overloaded. In this paper we present the use of LSTM-based
models (CNN-LSTM and Conv-LSTM) to effectively estimate future network traffic
and so provide a solid basis for formulating a sensible network configuration
plan.
