Huge number of data series can be collected from HPC
system virtual and physical sensors creating big amount of
data and complexity of the analysis. The compute nodes of the
HPC system analysed are grouped into three different queues:
serial, parallel and test. The majority of servers were
associated to the parallel queue to run jobs in parallel. The test
queue with only one compute node works similar with the
servers of the serial queue. By clusterization of variables
representing variable can be selected for each cluster. By
definition the clusters should have strongly correlated
elements. The method proposed for calculating and
characterizing the intra-cluster correlation among the elements
can be used for characterizing the inter-cluster correlation, as
well. It was found that the clusterization method presented in
this paper gives strong correlation inside the clusters and weak
correlation among the representing variables. This method
reduces the number of variables necessary to be sampled from
20 to 8 (one variable per each cluster), meaning decreasing by
60% of the management data collected from the sensors of a
HPC system.