Decode the Workload: Training Deep Learning Models for Efficient Compute Cluster Representation

In this study, we address the mounting challenge of monitoring high throughput computing clusters running computationally intensive jobs, which increasingly strains system administrators. We develop autoencoders that analyze traces of Linux kernel CPU metrics to capture salient system features by producing robust compressed embeddings for various downstream tasks. In addition, we employ graph neural networks to incorporate contextual information from surrounding CPUs and assess their performance. We also demonstrate the enhanced job differentiation achieved by increasing the sampling rate of these traces. Our models are evaluated based on their ability to generate meaningful latent representations, detect anomalies, and distinguish between different job types, marking a preliminary step towards self-supervised, large-scale foundation models for computing centers.