# Cloud Failure Prediction with Hierarchical Temporal Memory: An Empirical Assessment

arXiv: https://arxiv.org/abs/2110.03431 · HTML: https://mlsanity.com/paper/2110.03431/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 6 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex](https://mlsanity.com/paper/1511.00083/) (2015)
- [Continuous online sequence learning with an unsupervised neural network model](https://mlsanity.com/paper/1512.05463/) (2015)
- [A Survey and Taxonomy of Self-Aware and Self-Adaptive Cloud Autoscaling Systems](https://mlsanity.com/paper/1609.03590/) (2016)
- [How Bad Can a Bug Get? An Empirical Analysis of Software Failures in the OpenStack Cloud Computing Platform](https://mlsanity.com/paper/1907.04055/) (2019)
- [Encoding Data for HTM Systems](https://mlsanity.com/paper/1602.05925/) (2016)
- [Predicting Failures in Multi-Tier Distributed Systems](https://mlsanity.com/paper/1911.09561/) (2019)
- [Anomaly Detection As-a-Service](https://mlsanity.com/paper/1909.08378/) (2019)

## Abstract (arXiv)

Hierarchical Temporal Memory (HTM) is an unsupervised learning algorithm inspired by the features of the neocortex that can be used to continuously process stream data and detect anomalies, without requiring a large amount of data for training nor requiring labeled data. HTM is also able to continuously learn from samples, providing a model that is always up-to-date with respect to observations. These characteristics make HTM particularly suitable for supporting online failure prediction in cloud systems, which are systems with a dynamically changing behavior that must be monitored to anticipate problems. This paper presents the first systematic study that assesses HTM in the context of failure prediction. The results that we obtained considering 72 configurations of HTM applied to 12 different types of faults introduced in the Clearwater cloud system show that HTM can help to predict failures with sufficient effectiveness (F-measure = 0.76), representing an interesting practical alternative to (semi-)supervised algorithms.