Prodigy: Towards Unsupervised Anomaly Detection in Production HPC Systems
Burak Aksar, Efe Sencan, Benjamin Schwaller, Omar Aaziz, Vitus J. Leung, Jim M. Brandt, Brian Kulis, Manuel Egele, Ayse Kivilcim Coskun · 2023
Performance variations caused by anomalies in modern High Performance Computing (HPC) systems lead to decreased efficiency, impaired application performance, and increased operational costs. While machine learning (ML)-based frameworks for automated anomaly detection (often based on time series telemetry data) are gaining popularity in the literature, practical deployment challenges are often overlooked. Some ML-based frameworks require extensive customization, while others need a rich set of labeled samples, none of which are feasible for a production HPC system.