Adaptive Resource and Job Management for Limited Power Consumption
Résumé
The last decades have been characterized by an
ever growing requirement in terms of computing and storage resources.
This tendency has recently put the pressure on the ability
to efficiently manage the power required to operate the huge
amount of electrical components associated with state-of-the-art
high performance computing systems. The power consumption of
a supercomputer needs to be adjusted based on varying power
budget or electricity availabilities. As a consequence, Resource
and Job Management Systems have to be adequately adapted
in order to efficiently schedule jobs with optimized performance
while limiting power usage whenever needed.
We introduce in this paper a new scheduling strategy that
can adapt the executed workload to a limited power budget. The
originality of this approach relies upon a combination of speed
scaling and node shutdown techniques for power reductions. It is
implemented into the widely used resource and job management
system SLURM. Finally, it is validated through large scale emulations
using real production workload traces of the supercomputer
Curie.
Origine : Fichiers produits par l'(les) auteur(s)
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