# Anomaly Detection in Cloud Components

arXiv: https://arxiv.org/abs/2005.08739 · HTML: https://mlsanity.com/paper/2005.08739/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 3 of 3)

- [Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset](https://mlsanity.com/paper/2411.09047/) (2024)
- [Benchmarking Anomaly Detection Across Heterogeneous Cloud Telemetry Datasets](https://mlsanity.com/paper/2602.13288/) (2026)
- [ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoring](https://mlsanity.com/paper/2607.18127/) (2026)

## Abstract (arXiv)

Cloud platforms, under the hood, consist of a complex inter-connected stack of hardware and software components. Each of these components can fail which may lead to an outage. Our goal is to improve the quality of Cloud services through early detection of such failures by analyzing resource utilization metrics. We tested Gated-Recurrent-Unit-based autoencoder with a likelihood function to detect anomalies in various multi-dimensional time series and achieved high performance.