@article{Hilbrich_Frank_2017, title={Analysis of Series of Measurements from Job-Centric Monitoring by Statistical Functions}, volume={18}, url={https://journals.agh.edu.pl/csci/article/view/1791}, DOI={10.7494/csci.2017.18.1.2}, abstractNote={The rising number of executed programs (jobs) enabled by the<br />growing amount of available resources from Clouds, Grids,<br />and HPC (for example) has resulted in an enormous number of<br />jobs. Nowadays, most of the executed jobs are mainly<br />unobserved, so unusual behavior, non-optimal resource usage,<br />and silent faults are not systematically searched and<br />analyzed. Job-centric monitoring enables permanent job<br />observation and, thus, enables the analysis of monitoring<br />data.  In this paper, we show how statistic functions can be<br />used to analyze job-centric monitoring data and how the<br />methods compare to more-complex analysis methods.<br />Additionally, we present the usefulness of job-centric<br />monitoring based on practical experiences.<br /><br />}, number={1}, journal={Computer Science}, author={Hilbrich, Marcus and Frank, Markus}, year={2017}, month={Mar.}, pages={2} }