Comparison of Evolving Granular Classifiers applied to Anomaly Detection for Predictive Maintenance in Computing Centers

Leticia Decker, Daniel Leite, Fabio Viola, Daniele Bonacorsi

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

5 Citas (Scopus)

Resumen

Log-based predictive maintenance of computing centers is a main concern regarding the worldwide computing grid that supports the CERN (European Organization for Nu-clear Research) physics experiments. A log, as event-oriented ad-hoc information, is quite often given as unstructured big data. Log data processing is a time-consuming computational task. The goal is to grab essential information from a continuously changeable grid environment to construct a classification model. Evolving granular classifiers are suited to learn from time-varying log streams and, therefore, perform online classification of the severity of anomalies. We formulated a 4-class online anomaly classification problem, and employed time windows between landmarks and two granular computing methods, namely, Fuzzy-set-Based evolving Modeling (FBeM) and evolving Granular Neural Network (eGNN), to model and monitor logging activity rate. The results of classification are of utmost importance for predictive maintenance because priority can be given to specific time intervals in which the classifier indicates the existence of high or medium severity anomalies.

Idioma originalInglés
Título de la publicación alojada2020 IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2020 - Proceedings
EditoresGiovanna Castellano, Ciro Castiello, Corrado Mencar
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728143842
DOI
EstadoPublicada - may. 2020
Publicado de forma externa
Evento12th IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2020 - Bari, Italia
Duración: 27 may. 202029 may. 2020

Serie de la publicación

NombreIEEE Conference on Evolving and Adaptive Intelligent Systems
Volumen2020-May
ISSN (versión impresa)2330-4863
ISSN (versión digital)2473-4691

Conferencia

Conferencia12th IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2020
País/TerritorioItalia
CiudadBari
Período27/05/2029/05/20

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