Adaptive Resource and Job Management for Limited Power Consumption

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.