# AIOps for a Cloud Object Storage Service

arXiv: https://arxiv.org/abs/2005.03094 · HTML: https://mlsanity.com/paper/2005.03094/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

## Built on by (most cited first, 5 of 5)

- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)
- [Blockchain-Enabled Accountability in Data Supply Chain: A Data Bill of Materials Approach](https://mlsanity.com/paper/2408.08536/) (2024)
- [TensorBank: Tensor Lakehouse for Foundation Model Training](https://mlsanity.com/paper/2309.02094/) (2023)
- [AIOps-Driven Enhancement of Log Anomaly Detection in Unsupervised Scenarios](https://mlsanity.com/paper/2311.02621/) (2023)
- [AI Factories: It's time to rethink the Cloud-HPC divide](https://mlsanity.com/paper/2509.12849/) (2025)

## Abstract (arXiv)

With the growing reliance on the ubiquitous availability of IT systems and services, these systems become more global, scaled, and complex to operate. To maintain business viability, IT service providers must put in place reliable and cost efficient operations support. Artificial Intelligence for IT Operations (AIOps) is a promising technology for alleviating operational complexity of IT systems and services. AIOps platforms utilize big data, machine learning and other advanced analytics technologies to enhance IT operations with proactive actionable dynamic insight. In this paper we share our experience applying the AIOps approach to a production cloud object storage service to get actionable insights into system's behavior and health. We describe a real-life production cloud scale service and its operational data, present the AIOps platform we have created, and show how it has helped us resolving operational pain points.