DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured Systems

arXiv:2405.07749v1 Announce Type: new
Abstract: Anomaly detection in distributed systems such as High-Performance Computing (HPC) clusters is vital for early fault detection, performance optimisation, security monitoring, reliability in general but also operational insights. Deep Neural Networks have seen successful use in detecting long-term anomalies in multidimensional data, originating for instance from industrial or medical systems, or weather prediction. A downside of such methods is that they require a static input size, or lose data through cropping, sampling, or other dimensionality …

abstract anomaly anomaly detection arxiv computing cs.ai cs.dc cs.lg data detection distributed distributed systems general hpc industrial insights instance long-term monitoring multidimensional networks neural networks optimisation performance reliability security series systems type vital