Machine Learning-Based Anomaly Prediction for Proactive Monitoring in Data Centers: A Case Study on INFN-CNAF

Anomaly prediction in time series is crucial for ensuring the stability and security of data centers, especially in scientific contexts such as INFN-CNAF, the National Center for Research and Development in Information and Communication Technology of the National Institute for Nuclear Physics. At INFN-CNAF, large volumes of heterogeneous data critical to international experiments are managed using dedicated monitoring systems. To ensure continuous availability, artificial intelligence solutions are being explored to detect anomalies and predict potential failures proactively. This work presents a machine learning-based approach for automatic anomaly prediction in the operational metrics of INFN-CNAF’s WebDav service. We evaluate several methods, including Long Short-Term Memory, Random Forest, and various neural networks, assessing their Accuracy and sensitivity in distinguishing normal from anomalous behaviors. The results demonstrate the effectiveness of these methods, not only in predicting anomalies but also in pinpointing critical areas within monitored metrics. This contributes to more proactive IT resource monitoring and enhances data center management efficiency.