# RUAD: unsupervised anomaly detection in HPC systems

arXiv: https://arxiv.org/abs/2208.13169 · HTML: https://mlsanity.com/paper/2208.13169/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 51 references that are in the map.
- First posted on arXiv 28 Aug 2022.

## Builds on (most cited first, 9 of 9)

- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data](https://mlsanity.com/paper/1811.08055/) (2018)
- [Deep Learning for Anomaly Detection: A Review](https://mlsanity.com/paper/2007.02500/) (2020)
- [Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environments](https://mlsanity.com/paper/1812.05443/) (2018)
- [A Machine Learning Approach to Online Fault Classification in HPC Systems](https://mlsanity.com/paper/2007.14241/) (2020)
- [Online Anomaly Detection in HPC Systems](https://mlsanity.com/paper/1902.08447/) (2019)
- [Anomaly and Fraud Detection in Credit Card Transactions Using the ARIMA Model](https://mlsanity.com/paper/2009.07578/) (2020)
- [FINJ: A Fault Injection Tool for HPC Systems](https://mlsanity.com/paper/1807.10056/) (2018)
- [Online Fault Classification in HPC Systems through Machine Learning](https://mlsanity.com/paper/1810.11208/) (2018)

## Built on by (most cited first, 4 of 4)

- [DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured Systems](https://mlsanity.com/paper/2405.07749/) (2024)
- [What Artificial Intelligence can do for High-Performance Computing systems?](https://mlsanity.com/paper/2602.00014/) (2026)
- [SeT-Diff: Towards Semantic Foundation Models for HPC Telemetry and Time-Series](https://mlsanity.com/paper/2607.22548/) (2026)
- [Symbolic Separation: Grounding Deep Agents in Knowledge Graphs for Trustworthy Operational Data Analytics](https://mlsanity.com/paper/2609.17107/) (2026)

## Abstract (arXiv)

The increasing complexity of modern high-performance computing (HPC) systems necessitates the introduction of automated and data-driven methodologies to support system administrators' effort toward increasing the system's availability. Anomaly detection is an integral part of improving the availability as it eases the system administrator's burden and reduces the time between an anomaly and its resolution. However, current state-of-the-art (SoA) approaches to anomaly detection are supervised and semi-supervised, so they require a human-labelled dataset with anomalies - this is often impractical to collect in production HPC systems. Unsupervised anomaly detection approaches based on clustering, aimed at alleviating the need for accurate anomaly data, have so far shown poor performance. In this work, we overcome these limitations by proposing RUAD, a novel Recurrent Unsupervised Anomaly Detection model. RUAD achieves better results than the current semi-supervised and unsupervised SoA approaches. This is achieved by considering temporal dependencies in the data and including long-short term memory cells in the model architecture. The proposed approach is assessed on a complete ten-month history of a Tier-0 system (Marconi100 from CINECA with 980 nodes). RUAD achieves an area under the curve (AUC) of 0.763 in semi-supervised training and an AUC of 0.767 in unsupervised training, which improves upon the SoA approach that achieves an AUC of 0.747 in semi-supervised training and an AUC of 0.734 in unsupervised training. It also vastly outperforms the current SoA unsupervised anomaly detection approach based on clustering, achieving the AUC of 0.548.