  High-Performance Computing (HPC) systems need to be constantly monitored to
ensure their stability. The monitoring systems collect a tremendous amount of
data about different parameters or Key Performance Indicators (KPIs), such as
resource usage, IO waiting time, etc. A proper analysis of this data, usually
stored as time series, can provide insight in choosing the right management
strategies as well as the early detection of issues. In this paper, we
introduce a methodology to cluster HPC jobs according to their KPI indicators.
Our approach reduces the inherent high dimensionality of the collected data by
applying two techniques to the time series: literature-based and variance-based
feature extraction. We also define a procedure to visualize the obtained
clusters by combining the two previous approaches and the Principal Component
Analysis (PCA). Finally, we have validated our contributions on a real data set
to conclude that those KPIs related to CPU usage provide the best cohesion and
separation for clustering analysis and the good results of our visualization
methodology.
