Anomaly Detection in Data Center IT & Physical Infrastructure

Abstract

Anomaly detection in data center IT and physical infrastructure is challenging due to the amount of heterogeneous data to be analyzed. Defining a solution that early identifies unexpected anomalies is particularly important to prevent data losses, breakdown of the system, and any other event considered to be critical for the activity of the data center. In the context of the INFN CNAF data center, one of the WLCG Tier-1s, we have performed a study based on monitored cooling, electrical, and IT hardware and software metrics to identify anomalies. In the present work, we aim to explore statistical approaches and machine learning solutions in the anomaly detection field for time series numerical metrics related to IT and physical infrastructure sensors. With the usage of statistical Z-score and percentile approaches and clustering DBSCAN technique, we have been able to group and identify anomalous events. Using the presented approach, different relevance can be attributed to the likely anomalies.