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When a system fails to function properly, health-related data are collected for troubleshooting. However, it is challenging to effectively identify anomalies from the voluminous amount of noisy, high-dimensional data. The traditional manual approach is time-consuming, error-prone, and even worse, not scalable. In this paper, the authors present an automated mechanism for node-level anomaly identification in large-scale systems. A set of techniques are presented to automatically analyze collected data: data transformation to construct a uniform data format for data analysis, feature extraction to reduce data size, and unsupervised learning to detect the nodes acting differently from others.
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