# Operational Data Analytics in Practice: Experiences from Design to Deployment in Production HPC Environments

arXiv: https://arxiv.org/abs/2106.14423 · HTML: https://mlsanity.com/paper/2106.14423/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2021.

## Built on by (most cited first, 2 of 2)

- [EPIC: Generative AI Platform for Accelerating HPC Operational Data Analytics](https://mlsanity.com/paper/2509.16212/) (2025)
- [Symbolic Separation: Grounding Deep Agents in Knowledge Graphs for Trustworthy Operational Data Analytics](https://mlsanity.com/paper/2609.17107/) (2026)

## Abstract (arXiv)

As HPC systems grow in complexity, efficient and manageable operation is increasingly critical. Many centers are thus starting to explore the use of Operational Data Analytics (ODA) techniques, which extract knowledge from massive amounts of monitoring data and use it for control and visualization purposes. As ODA is a multi-faceted problem, much effort has gone into researching its separate aspects: however, accounts of production ODA experiences are still hard to come across. In this work we aim to bridge the gap between ODA research and production use by presenting our experiences with ODA in production, involving in particular the control of cooling infrastructures and visualization of job data on two HPC systems. We cover the entire development process, from design to deployment, highlighting our insights in an effort to drive the community forward. We rely on open-source tools, which make for a generic ODA framework suitable for most scenarios.