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.

翻译：高度时空内存(HTM)是一种不受监督的学习算法,它受新皮层特性的启发,可用于连续处理流数据并检测异常现象,而不需要大量的培训数据,也不需要贴标签数据。HTM还能够不断从样本中学习,提供一个总能更新观测结果的模型。这些特征使得HTM特别适合支持云系统的在线故障预测,云系统是具有动态变化行为的系统,必须加以监测,以预测问题。本文介绍了在故障预测方面评估HTM的首次系统研究。我们考虑到HTM的72个配置适用于清洁水云系统中引入的12种不同类型的故障,结果显示HTM能够帮助充分有效地预测故障(F-措施=0.76),这是对(半)超强算算法的一种有趣的实用替代方法。