PoS - Proceedings of Science
Volume 434 - International Symposium on Grids & Clouds (ISGC) 2023 in conjunction with HEPiX Spring 2023 Workshop (ISGC&HEPiX2023) - Artificial Intelligence (AI)
Anomaly Detection in Data Center IT Infrastructure using Natural language Processing and Time Series Solutions
E. Ronchieri*, F. Pacinelli, L. Giommi, D.C. Duma, A. Costantini and D. Salomoni
Full text: pdf
Published on: October 25, 2023
Abstract
Data centers house IT and physical infrastructures to support researchers in transmitting, processing and exchanging data and provide resources and services with a high level of reliability. Through the usage of infrastructure observability platforms, it is possible to access and analyse data that provide information on data center status enabling the prediction of events of interest.

During the last few years, in the context of the main data processing and computing technology research center of the Italian Institute for Nuclear Physics, we have performed a set of studies based on service log files and machine metrics to identify anomalies and define alarm signals. In the present work we aim at validating our previous studies by considering critical scenarios and extending the range and type of monitoring data. With the usage of principal component analysis, clustering techniques, and statistical anomaly detection solutions, we have been able to achieve a faster, almost real-time, detection of anomalies taking into consideration the collection of past events.

As an added value, the relationship between the identified anomalies and the threshold-risk values will be assessed and shown as a dynamic level of risks to be used for predictive maintenance management.
DOI: https://doi.org/10.22323/1.434.0024
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.