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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 generatea huge number of Key Performance Indicators (KPIs) to supervise the status ofthe jobs running in these systems. KPIs give data about CPU usage, memoryusage, network (interface) traffic, or other sensors that monitor the hardware.Analyzing this data, it is possible to obtain insightful information aboutrunning jobs, such as their characteristics, performance, and failures. Themain contribution in this paper is to identify which metric/s (KPIs) is/are themost appropriate to identify/classify different types of jobs according totheir behavior in the HPC system. With this aim, we have applied differentclustering techniques (partition and hierarchical clustering algorithms) usinga real dataset from the Galician Computation Center (CESGA). We have concludedthat (i) those metrics (KPIs) related to the Network (interface) trafficmonitoring 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 HPCcenter.

Unsupervised KPIs-Based Clustering of Jobs in HPC Data Centers