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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 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 was 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 had applied different clustering techniques (partition and hierarchical clustering algorithms) using a real dataset from the Galician computation center (CESGA). We concluded that (i) those metrics (KPIs) related to the network (interface) traffic monitoring provided the best cohesion and separation to cluster HPC jobs, and (ii) hierarchical clustering algorithms were the most suitable for this task. Our approach was validated using a different real dataset from the same HPC center.

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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. INDEX TERMS Clustering, feature extraction, high-performance computing, time series analysis.