Cargando…
Erratic server behavior detection using machine learning on streams of monitoring data
With the explosion of the number of distributed applications, a new dynamic server environment emerged grouping servers into clusters, utilization of which depends on the current demand for the application. To provide reliable and smooth services it is crucial to detect and fix possible erratic beha...
Autores principales: | Adam, Martin, Magnoni, Luca, Pilát, Martin, Adamová, Dagmar |
---|---|
Lenguaje: | eng |
Publicado: |
2020
|
Materias: | |
Acceso en línea: | https://dx.doi.org/10.1051/epjconf/202024507002 http://cds.cern.ch/record/2752527 |
Ejemplares similares
-
Detection of erratic behavior in load balanced clusters of servers using a machine learning based method
por: Adam, Martin, et al.
Publicado: (2019) -
Machine learning for data streams: with practical examples in MOA
por: Bifet, Albert, et al.
Publicado: (2017) -
SQL Server 2019 revealed: including big data clusters and machine learning
por: Ward, Bob, et al.
Publicado: (2019) -
Learning Spark SQL: architect streaming analytics and machine learning solutions
por: Sarkar, Aurobindo
Publicado: (2017) -
SQL Server 2017 Machine Learning Services with R: data exploration, modeling, and advanced analytics
por: Kaštrun, Tomaž, et al.
Publicado: (2018)