  Log-based predictive maintenance of computing centers is a main concern
regarding the worldwide computing grid that supports the CERN (European
Organization for Nuclear Research) physics experiments. A log, as
event-oriented adhoc 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.
