Analysis of Flow Prolongation Using Graph Neural Network in FIFO Multiplexing System

Abstract: Network Calculus views a network system as a queuing framework and provides a series of mathematical functions for finding an upper bound of an end-to-end delay. It is crucial for the design of networks and applications with a hard delay guarantee, such as the emerging Time Sensitive Network. Even though several approaches in Network Calculus can be used directly to find bounds on the worst-case delay, these bounds are usually not tight, and making them tight is a hard problem due to the extremely intensive computing requirements. This problem has also been proven as NP-Hard. One newly introduced solution to tighten the delay bound is the so-called Flow Prolongation. It extends the paths of cross flows to new sink servers, which naturally increases the worst-case delay, but might at the same time decrease the delay bound. The most straightforward and the most rigorous solution to find the optimal Flow Prolongation combinations is by doing exhaustive searches. However, this approach is not scalable with the network size. Thus, a machine learning model, Graph Neural Network (GNN), has been introduced for the prediction of the optimal Flow Prolongation combinations, mitigating the scalability issue. However, early research also found out that machine learning models consistently misclassify adversarial examples. In this thesis, Fast Gradient Sign Method (FGSM) is used to benchmark how adversarial attacks will influence the delay bound achieved by the Flow Prolongation method. It is performed by slightly modifying the input network features based on their gradients. To achieve this, we first learned the usage of NetCal DNC, an Free and Open Source Software, to calculate the Pay Multiplexing Only Once (PMOO), one of the Network Calculus methods for the delay bound calculation. Then we reproduced the GNN model based on PMOO, and achieved an accuracy of 65%. Finally, the FGSM is implemented on a newly created dataset with a large number of servers and flows inside. Our results demonstrate that with at most 14% changes on the network features input, the accuracy of GNN drastically decreases to an average 9.45%, and some prominent examples are found whose delay bounds are largely loosened by the GNN Flow Prolongation prediction after the FGSM attack.