Predictive analytics tools to adjust and monitor performance metrics for the ATLAS Production System

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Having information such as an estimation of the processing time or possibility of system outage (abnormal behaviour) helps to assist to monitor system performance and to predict its next state. The current cyber-infrastructure presents computing conditions in which contention for resources among hig...

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Autores principales: Barreiro Megino, Fernando Harald, Borodin, Mikhail, Golubkov, Dmitry, Grigoryeva, Maria, Gubin, Maksim, Klimentov, Alexei, Korchuganova, Tatiana, Maeno, Tadashi, Padolski, Siarhei, Titov, Mikhail
Lenguaje:eng
Publicado: 2017
Materias:
Particle Physics - Experiment
Acceso en línea:https://dx.doi.org/10.1088/1742-6596/1085/3/032051
http://cds.cern.ch/record/2290126
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author Barreiro Megino, Fernando Harald
Borodin, Mikhail
Golubkov, Dmitry
Grigoryeva, Maria
Gubin, Maksim
Klimentov, Alexei
Korchuganova, Tatiana
Maeno, Tadashi
Padolski, Siarhei
Titov, Mikhail
author_facet Barreiro Megino, Fernando Harald
Borodin, Mikhail
Golubkov, Dmitry
Grigoryeva, Maria
Gubin, Maksim
Klimentov, Alexei
Korchuganova, Tatiana
Maeno, Tadashi
Padolski, Siarhei
Titov, Mikhail
author_sort Barreiro Megino, Fernando Harald
collection CERN
description Having information such as an estimation of the processing time or possibility of system outage (abnormal behaviour) helps to assist to monitor system performance and to predict its next state. The current cyber-infrastructure presents computing conditions in which contention for resources among high-priority data analysis happens routinely, that might lead to significant workload and data handling interruptions. The lack of the possibility to monitor and to predict the behaviour of the analysis process (its duration) and system’s state itself caused to focus on design of the built-in situational awareness analytic tools.
id cern-2290126
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2017
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spelling cern-22901262021-02-09T10:05:29Zdoi:10.1088/1742-6596/1085/3/032051http://cds.cern.ch/record/2290126engBarreiro Megino, Fernando HaraldBorodin, MikhailGolubkov, DmitryGrigoryeva, MariaGubin, MaksimKlimentov, AlexeiKorchuganova, TatianaMaeno, TadashiPadolski, SiarheiTitov, MikhailPredictive analytics tools to adjust and monitor performance metrics for the ATLAS Production SystemParticle Physics - ExperimentHaving information such as an estimation of the processing time or possibility of system outage (abnormal behaviour) helps to assist to monitor system performance and to predict its next state. The current cyber-infrastructure presents computing conditions in which contention for resources among high-priority data analysis happens routinely, that might lead to significant workload and data handling interruptions. The lack of the possibility to monitor and to predict the behaviour of the analysis process (its duration) and system’s state itself caused to focus on design of the built-in situational awareness analytic tools.ATL-SOFT-PROC-2017-059oai:cds.cern.ch:22901262017-10-20
spellingShingle Particle Physics - Experiment
Barreiro Megino, Fernando Harald
Borodin, Mikhail
Golubkov, Dmitry
Grigoryeva, Maria
Gubin, Maksim
Klimentov, Alexei
Korchuganova, Tatiana
Maeno, Tadashi
Padolski, Siarhei
Titov, Mikhail
Predictive analytics tools to adjust and monitor performance metrics for the ATLAS Production System
title Predictive analytics tools to adjust and monitor performance metrics for the ATLAS Production System
title_full Predictive analytics tools to adjust and monitor performance metrics for the ATLAS Production System
title_fullStr Predictive analytics tools to adjust and monitor performance metrics for the ATLAS Production System
title_full_unstemmed Predictive analytics tools to adjust and monitor performance metrics for the ATLAS Production System
title_short Predictive analytics tools to adjust and monitor performance metrics for the ATLAS Production System
title_sort predictive analytics tools to adjust and monitor performance metrics for the atlas production system
topic Particle Physics - Experiment
url https://dx.doi.org/10.1088/1742-6596/1085/3/032051
http://cds.cern.ch/record/2290126
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