Network Calculus with Flow Prolongation -- A Feedforward FIFO Analysis enabled by ML
This work addresses a bottleneck in network performance analysis for applications requiring precise delay bounds, such as real-time systems, by making an existing improvement technique scalable.
The paper tackles the problem of deriving accurate worst-case traversal time bounds in feedforward FIFO networks by addressing the scalability issue of Flow Prolongation (FP), which improves accuracy but is computationally expensive. The result is DeepFP, a machine learning-based approach that reduces delay bounds by 12.1% on average with negligible additional computational cost.
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.