The rising number of executed programs (jobs) enabled by thegrowing amount of available resources from Clouds, Grids,and HPC (for example) has resulted in an enormous number ofjobs. Nowadays, most of the executed jobs are mainlyunobserved, so unusual behavior, non-optimal resource usage,and silent faults are not systematically searched andanalyzed. Job-centric monitoring enables permanent jobobservation and, thus, enables the analysis of monitoringdata. In this paper, we show how statistic functions can beused to analyze job-centric monitoring data and how themethods compare to more-complex analysis methods.Additionally, we present the usefulness of job-centricmonitoring based on practical experiences.
%0 Journal Article
%1 csci1791
%A Hilbrich, Marcus
%A Frank, Markus
%D 2017
%J Computer Science
%K Analysis Cloud, Grid, Job-centric Monitoring, Series Similarity, Statistical functions, measurements, monitoring, of
%N 1
%P 2
%T Analysis of Series of Measurements from Job-Centric Monitoring by Statistical Functions
%U https://journals.agh.edu.pl/csci/article/view/1791
%V 18
%X The rising number of executed programs (jobs) enabled by thegrowing amount of available resources from Clouds, Grids,and HPC (for example) has resulted in an enormous number ofjobs. Nowadays, most of the executed jobs are mainlyunobserved, so unusual behavior, non-optimal resource usage,and silent faults are not systematically searched andanalyzed. Job-centric monitoring enables permanent jobobservation and, thus, enables the analysis of monitoringdata. In this paper, we show how statistic functions can beused to analyze job-centric monitoring data and how themethods compare to more-complex analysis methods.Additionally, we present the usefulness of job-centricmonitoring based on practical experiences.
@article{csci1791,
abstract = {The rising number of executed programs (jobs) enabled by thegrowing amount of available resources from Clouds, Grids,and HPC (for example) has resulted in an enormous number ofjobs. Nowadays, most of the executed jobs are mainlyunobserved, so unusual behavior, non-optimal resource usage,and silent faults are not systematically searched andanalyzed. Job-centric monitoring enables permanent jobobservation and, thus, enables the analysis of monitoringdata. In this paper, we show how statistic functions can beused to analyze job-centric monitoring data and how themethods compare to more-complex analysis methods.Additionally, we present the usefulness of job-centricmonitoring based on practical experiences.},
added-at = {2018-02-14T14:42:23.000+0100},
author = {Hilbrich, Marcus and Frank, Markus},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/20f3c20e95e63f9e4ee4e33dc623179bf/markusfrank},
interhash = {dc197953b29cd72b4bca74593f85d86e},
intrahash = {0f3c20e95e63f9e4ee4e33dc623179bf},
issn = {2300-7036},
journal = {Computer Science},
keywords = {Analysis Cloud, Grid, Job-centric Monitoring, Series Similarity, Statistical functions, measurements, monitoring, of},
number = 1,
pages = 2,
timestamp = {2018-02-14T13:42:23.000+0100},
title = {Analysis of Series of Measurements from Job-Centric Monitoring by Statistical Functions},
url = {https://journals.agh.edu.pl/csci/article/view/1791},
volume = 18,
year = 2017
}
