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The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 9/2021

23.02.2021

Performance and energy task migration model for heterogeneous clusters

verfasst von: Esteban Stafford, José Luis Bosque

Erschienen in: The Journal of Supercomputing | Ausgabe 9/2021

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Abstract

This article presents a set of linear regression models to predict the impact of task migration on different objectives, like performance and energy consumption. It allows to establish whether at a given moment the migration of a task is profitable in terms of performance or energy consumption. Also, it can be used to determine the best node to migrate a task depending on the objective. The model uses a small set of parameters that are easily measurable. It has been validated against a small heterogeneous cluster using the Slurm resource manager. The model captures the tendencies observed in the results of the experiments, with average relative errors below 3.5% in execution time and 2.5% in energy consumption.
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Fußnoten
1
The code is available at https://​github.​com/​dmtcp.
 
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Metadaten
Titel
Performance and energy task migration model for heterogeneous clusters
verfasst von
Esteban Stafford
José Luis Bosque
Publikationsdatum
23.02.2021
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 9/2021
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-021-03663-1

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