# Anomaly Detection in a Large-scale Cloud Platform

arXiv: https://arxiv.org/abs/2010.10966 · HTML: https://mlsanity.com/paper/2010.10966/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [Anomaly Detection in Large-Scale Cloud Systems: An Industry Case and Dataset](https://mlsanity.com/paper/2411.09047/) (2024)
- [Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features](https://mlsanity.com/paper/2307.10869/) (2023)
- [Benchmarking Anomaly Detection Across Heterogeneous Cloud Telemetry Datasets](https://mlsanity.com/paper/2602.13288/) (2026)
- [TensorBank: Tensor Lakehouse for Foundation Model Training](https://mlsanity.com/paper/2309.02094/) (2023)
- [Towards Generalizable Context-aware Anomaly Detection: A Large-scale Benchmark in Cloud Environments](https://mlsanity.com/paper/2508.01844/) (2025)
- [On Sequence-to-Sequence Models for Automated Log Parsing](https://mlsanity.com/paper/2602.07698/) (2026)
- [ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoring](https://mlsanity.com/paper/2607.18127/) (2026)

## Abstract (arXiv)

Cloud computing is ubiquitous: more and more companies are moving the workloads into the Cloud. However, this rise in popularity challenges Cloud service providers, as they need to monitor the quality of their ever-growing offerings effectively. To address the challenge, we designed and implemented an automated monitoring system for the IBM Cloud Platform. This monitoring system utilizes deep learning neural networks to detect anomalies in near-real-time in multiple Platform components simultaneously. After running the system for a year, we observed that the proposed solution frees the DevOps team's time and human resources from manually monitoring thousands of Cloud components. Moreover, it increases customer satisfaction by reducing the risk of Cloud outages. In this paper, we share our solutions' architecture, implementation notes, and best practices that emerged while evolving the monitoring system. They can be leveraged by other researchers and practitioners to build anomaly detectors for complex systems.