  The derivation of upper bounds on data flows' worst-case traversal times is
an important task in many application areas. For accurate bounds, model
simplifications should be avoided even in large networks. Network Calculus (NC)
provides a modeling framework and different analyses for delay bounding. We
investigate the analysis of feedforward networks where all queues implement
First-In First-Out (FIFO) service. Correctly considering the effect of data
flows onto each other under FIFO is already a challenging task. Yet, the
fastest available NC FIFO analysis suffers from limitations resulting in
unnecessarily loose bounds. A feature called Flow Prolongation (FP) has been
shown to improve delay bound accuracy significantly. Unfortunately, FP needs to
be executed within the NC FIFO analysis very often and each time it creates an
exponentially growing set of alternative networks with prolongations. FP
therefore does not scale and has been out of reach for the exhaustive analysis
of large networks. We introduce DeepFP, an approach to make FP scale by
predicting prolongations using machine learning. In our evaluation, we show
that DeepFP can improve results in FIFO networks considerably. Compared to the
standard NC FIFO analysis, DeepFP reduces delay bounds by 12.1% on average at
negligible additional computational cost.
