RT Journal Article
T1 KPIs-based clustering and visualization of HPC jobs: a feature reduction approach
A1 Halawa, Mohamed Soliman
A1 Díaz Redondo, Rebeca Pilar
A1 Fernández Vilas, Ana
K1 1209.03 Análisis de Datos
K1 1203.17 Informática
AB 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.
PB IEEE Access
SN 21693536
YR 2021
FD 2021-02
LK http://hdl.handle.net/11093/2469
UL http://hdl.handle.net/11093/2469
LA eng
NO IEEE Access, 9: 25522-25543 (2021)
NO Agencia Estatal de Investigación | Ref. TEC2017-84197-C4-2-R
DS Investigo
RD 25-ene-2025