Evaluating unsupervised fault detection in self-healing systems using stochastic primitives

Abstract

Autonomous fault detection represents one approach for reducing operational costs in large-scale computing environments. However, little empirical evidence exists regarding the implementation or comparison of such methodologies, or offers proof that such approaches reduce costs. This paper compares the effectiveness of several types of stochastic primitives using unsupervised learning to heuristically determine the root causes of faults. The results suggest that self-healing systems frameworks leveraging these techniques can reliably and autonomously determine the source of an anomaly within as little as five minutes. This finding lays the foundation for determining the potential these approaches have for reducing operational costs and ultimately concludes with new avenues for exploring anomaly prediction.