Collating time-series resource data for system-wide job profiling

V. K.Cody Bumgardner; Victor W. Marek; Ray L. Hyatt

doi:10.1109/NOMS.2016.7502958

Collating time-series resource data for system-wide job profiling

V. K.Cody Bumgardner
, Victor W. Marek
, Ray L. Hyatt

Producción científica: Conference contribution › revisión exhaustiva

2 Citas (Scopus)

Resumen

Through the collection and association of discrete time-series resource metrics and workloads, we can both provide benchmark and intra-job resource collations, along with system-wide job profiling. Traditional RDBMSes are not designed to store and process long-term discrete time-series metrics and the commonly used resolution-reducing round robin databases (RRDB), make poor long-term sources of data for workload analytics. We implemented a system that employs Big-data (Hadoop/HBase) and other analytics (R) techniques and tools to store, process, and characterize HPC workloads. Using this system we have collected and processed over a 30 billion time-series metrics from existing short-term high-resolution (15-sec RRDB) sources, profiling over 200 thousand jobs across a wide spectrum of workloads. The system is currently in use at the University of Kentucky for better understanding of individual jobs and system-wide profiling as well as a strategic source of data for resource allocation and future acquisitions.

Idioma original	English
Título de la publicación alojada	Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium
Editores	Sema Oktug Badonnel, Mehmet Ulema, Cicek Cavdar, Lisandro Zambenedetti Granville, Carlos Raniery P. dos Santos
Páginas	1043-1048
Número de páginas	6
ISBN (versión digital)	9781509002238
DOI	https://doi.org/10.1109/NOMS.2016.7502958
Estado	Published - jun 30 2016
Evento	2016 IEEE/IFIP Network Operations and Management Symposium, NOMS 2016 - Istanbul, Turkey Duración: abr 25 2016 → abr 29 2016

Serie de la publicación

Nombre	Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium

Conference

Conference	2016 IEEE/IFIP Network Operations and Management Symposium, NOMS 2016
País/Territorio	Turkey
Ciudad	Istanbul
Período	4/25/16 → 4/29/16

Nota bibliográfica

Publisher Copyright:
© 2016 IEEE.

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems and Management

Acceder al documento

10.1109/NOMS.2016.7502958

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Bumgardner, V. K. C., Marek, V. W., & Hyatt, R. L. (2016). Collating time-series resource data for system-wide job profiling. En S. O. Badonnel, M. Ulema, C. Cavdar, L. Z. Granville, & C. R. P. dos Santos (Eds.), Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium (pp. 1043-1048). Artículo 7502958 (Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium). https://doi.org/10.1109/NOMS.2016.7502958

Bumgardner, V. K.Cody ; Marek, Victor W. ; Hyatt, Ray L. / Collating time-series resource data for system-wide job profiling. Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium. editor / Sema Oktug Badonnel ; Mehmet Ulema ; Cicek Cavdar ; Lisandro Zambenedetti Granville ; Carlos Raniery P. dos Santos. 2016. pp. 1043-1048 (Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium).

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title = "Collating time-series resource data for system-wide job profiling",

abstract = "Through the collection and association of discrete time-series resource metrics and workloads, we can both provide benchmark and intra-job resource collations, along with system-wide job profiling. Traditional RDBMSes are not designed to store and process long-term discrete time-series metrics and the commonly used resolution-reducing round robin databases (RRDB), make poor long-term sources of data for workload analytics. We implemented a system that employs Big-data (Hadoop/HBase) and other analytics (R) techniques and tools to store, process, and characterize HPC workloads. Using this system we have collected and processed over a 30 billion time-series metrics from existing short-term high-resolution (15-sec RRDB) sources, profiling over 200 thousand jobs across a wide spectrum of workloads. The system is currently in use at the University of Kentucky for better understanding of individual jobs and system-wide profiling as well as a strategic source of data for resource allocation and future acquisitions.",

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Bumgardner, VKC, Marek, VW & Hyatt, RL 2016, Collating time-series resource data for system-wide job profiling. En SO Badonnel, M Ulema, C Cavdar, LZ Granville & CRP dos Santos (eds.), Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium., 7502958, Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium, pp. 1043-1048, 2016 IEEE/IFIP Network Operations and Management Symposium, NOMS 2016, Istanbul, Turkey, 4/25/16. https://doi.org/10.1109/NOMS.2016.7502958

Collating time-series resource data for system-wide job profiling. / Bumgardner, V. K.Cody; Marek, Victor W.; Hyatt, Ray L.
Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium. ed. / Sema Oktug Badonnel; Mehmet Ulema; Cicek Cavdar; Lisandro Zambenedetti Granville; Carlos Raniery P. dos Santos. 2016. p. 1043-1048 7502958 (Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium).

Producción científica: Conference contribution › revisión exhaustiva

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ER -

Bumgardner VKC, Marek VW, Hyatt RL. Collating time-series resource data for system-wide job profiling. En Badonnel SO, Ulema M, Cavdar C, Granville LZ, dos Santos CRP, editores, Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium. 2016. p. 1043-1048. 7502958. (Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium). doi: 10.1109/NOMS.2016.7502958

Collating time-series resource data for system-wide job profiling

Resumen

Serie de la publicación

Conference

Nota bibliográfica

ASJC Scopus subject areas

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