  Performance analysis is an essential task in High-Performance Computing (HPC)
systems and it is applied for different purposes such as anomaly detection,
optimal resource allocation, and budget planning. HPC monitoring tasks generate
a huge number of Key Performance Indicators (KPIs) to supervise the status of
the jobs running in these systems. KPIs give data about CPU usage, memory
usage, network (interface) traffic, or other sensors that monitor the hardware.
Analyzing this data, it is possible to obtain insightful information about
running jobs, such as their characteristics, performance, and failures. The
main contribution in this paper is to identify which metric/s (KPIs) is/are the
most appropriate to identify/classify different types of jobs according to
their behavior in the HPC system. With this aim, we have applied different
clustering techniques (partition and hierarchical clustering algorithms) using
a real dataset from the Galician Computation Center (CESGA). We have concluded
that (i) those metrics (KPIs) related to the Network (interface) traffic
monitoring provide the best cohesion and separation to cluster HPC jobs, and
(ii) hierarchical clustering algorithms are the most suitable for this task.
Our approach was validated using a different real dataset from the same HPC
center.
