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

Leticia Decker, Daniel Leite, Fabio Viola, Daniele Bonacorsi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2020 - Proceedings
EditorsGiovanna Castellano, Ciro Castiello, Corrado Mencar
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728143842
DOIs
StatePublished - May 2020
Externally publishedYes
Event12th IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2020 - Bari, Italy
Duration: 27 May 202029 May 2020

Publication series

NameIEEE Conference on Evolving and Adaptive Intelligent Systems
Volume2020-May
ISSN (Print)2330-4863
ISSN (Electronic)2473-4691

Conference

Conference12th IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2020
Country/TerritoryItaly
CityBari
Period27/05/2029/05/20

Keywords

  • Anomaly Detection
  • Computing Center
  • Evolving Systems
  • Online Learning
  • Predictive Maintenance

Fingerprint

Dive into the research topics of 'Comparison of Evolving Granular Classifiers applied to Anomaly Detection for Predictive Maintenance in Computing Centers'. Together they form a unique fingerprint.

Cite this