  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.
