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 (called LUDB) 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 this 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 aforementioned LUDB analysis, DeepFP reduces delay bounds by$12.1 \;\%$on average at negligible additional computational cost.

Network calculus offers the means to compute worst-case traversal times based on interpreting a system as a queueing network. A major strength of network calculus is its strict separation of modeling and analysis frameworks. That is, a model is purely descriptive and can be put into multiple different analyses to derive a data flow's worst-case traversal time bound. One of the recent results in this category is the so-called flow prolongation. Flow prolongation actively manipulates the internal model of the analysis by virtually extending the path of flows, i.e., by deliberately creating a more pessimistic setting of resource contention between flows. It was shown that flow prolongation can theoretically decrease worst-case traversal time bounds under certain assumptions. Yet, due to its exhaustive search, it was also shown that flow prolongation does not scale and it might not even have an impact in larger queueing networks. In this paper we introduce DeepFP, an approach to make the analysis scale by predicting flow prolongations using a graph neural network. In our evaluation, we show that DeepFP can improve results in networks of FIFO queues considerably, where the delay bound can be reduced by 13.7% in large FIFO networks at negligible additional cost on the execution time of the analysis.