# Enhancing Failure Propagation Analysis in Cloud Computing Systems

arXiv: https://arxiv.org/abs/1908.11640 · HTML: https://mlsanity.com/paper/1908.11640/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep Learning](https://mlsanity.com/paper/2106.15182/) (2021)

## Abstract (arXiv)

In order to plan for failure recovery, the designers of cloud systems need to understand how their system can potentially fail. Unfortunately, analyzing the failure behavior of such systems can be very difficult and time-consuming, due to the large volume of events, non-determinism, and reuse of third-party components. To address these issues, we propose a novel approach that joins fault injection with anomaly detection to identify the symptoms of failures. We evaluated the proposed approach in the context of the OpenStack cloud computing platform. We show that our model can significantly improve the accuracy of failure analysis in terms of false positives and negatives, with a low computational cost.