# Fault Injection Analytics: A Novel Approach to Discover Failure Modes in Cloud-Computing Systems

arXiv: https://arxiv.org/abs/2010.00331 · HTML: https://mlsanity.com/paper/2010.00331/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

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

- [Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep Learning](https://mlsanity.com/paper/2106.15182/) (2021)
- [PyResBugs: A Dataset of Residual Python Bugs for Natural Language-Driven Fault Injection](https://mlsanity.com/paper/2505.05777/) (2025)
- [syslrn: Learning What to Monitor for Efficient Anomaly Detection](https://mlsanity.com/paper/2203.15324/) (2022)

## Abstract (arXiv)

Cloud computing systems fail in complex and unexpected ways due to unexpected combinations of events and interactions between hardware and software components. Fault injection is an effective means to bring out these failures in a controlled environment. However, fault injection experiments produce massive amounts of data, and manually analyzing these data is inefficient and error-prone, as the analyst can miss severe failure modes that are yet unknown. This paper introduces a new paradigm (fault injection analytics) that applies unsupervised machine learning on execution traces of the injected system, to ease the discovery and interpretation of failure modes. We evaluated the proposed approach in the context of fault injection experiments on the OpenStack cloud computing platform, where we show that the approach can accurately identify failure modes with a low computational cost.