Data Center IT Anomaly Prediction and Classification: an INFN CNAF experience
Elisabetta Ronchieri, Luca Torzi, L. Giommi, Alessandro COSTANTINI, Luigi Benedettto Scarponi · 2024
The INFN CNAF data center provides a huge amount of heterogeneous data through the adoption of dedicated monitoring systems. Having to provide a 24/7 availability, it has started to assess artificial intelligence solutions to detect anomalies aimed to predict possible failures. In this study, the main goal is to define an artificial intelligence framework able to classify and predict anomalies in time series data obtained from different sensors and systems within the data center (i.e., electrical plant, cooling system, and UPS system). Having to deal with unlabeled data, the proposed framework performs as a first step a regression task to learn the behavior of the sensors and, given the previous 5 timestamps, provides the values of the sensors in the next timestamp. As a second step, it performs a classification task. Comparing the predicted and the actual behaviors of the sensors, in fact, evaluates the status of the system and possible anomalies. During the first step, a mean squared error of 0.025 has been obtained, while in the second one an F1-score of 0.997 has been reached.