Real-time anomaly detection in data centers for log-based predictive maintenance using an evolving fuzzy-rule-based approach

Leticia Decker, Daniel Leite, Luca Giommi, Daniele Bonacorsi

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

14 Scopus citations

Abstract

Detection of anomalous behaviors in data centers is crucial to predictive maintenance and data safety. With data centers, we mean any computer network that allows users to transmit and exchange data and information. In particular, we focus on the Tier-1 data center of the Italian Institute for Nuclear Physics (INFN), which supports the high-energy physics experiments at the Large Hadron Collider (LHC) in Geneva. The center provides resources and services needed for data processing, storage, analysis, and distribution. Log records in the data center is a stochastic and non-stationary phenomenon in nature. We propose a real-time approach to monitor and classify log records based on sliding time windows, and a time-varying evolving fuzzy-rule-based classification model. The most frequent log pattern according to a control chart is taken as the normal system status. We extract attributes from time windows to gradually develop and update an evolving Gaussian Fuzzy Classifier (eGFC) on the fly. The real-time anomaly monitoring system has to provide encouraging results in terms of accuracy, compactness, and real-time operation.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Fuzzy Systems, FUZZ 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728169323
DOIs
StatePublished - Jul 2020
Externally publishedYes
Event2020 IEEE International Conference on Fuzzy Systems, FUZZ 2020 - Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

NameIEEE International Conference on Fuzzy Systems
Volume2020-July
ISSN (Print)1098-7584

Conference

Conference2020 IEEE International Conference on Fuzzy Systems, FUZZ 2020
Country/TerritoryUnited Kingdom
CityGlasgow
Period19/07/2024/07/20

Keywords

  • Anomaly detection
  • Evolving intelligent system
  • Fuzzy logic
  • Machine learning
  • Predictive maintenance

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