Graph Neural Networks for Anomaly Anticipation in HPC Systems
Martin Molan, Junaid Ahmed Khan, Andrea Borghesi, Andrea Giorgio Bartolini · 2023
In this paper, we explore the use of Graph Neural Networks (GNNs) for anomaly anticipation in high performance computing (HPC) systems. We propose a GNN-based approach that leverages the structure of the HPC system (particularly, the physical proximity of the compute nodes) to facilitate anomaly anticipation. We frame the task of forecasting the availability of the compute nodes as a supervised prediction problem; the GNN predicts the probability that a compute node will fail within a fixed-length future window.