Adaptive Resource and Job Management for Limited Power Consumption

Yiannis Georgiou, David Glesser, Denis Trystram
2015 2015 IEEE International Parallel and Distributed Processing Symposium Workshop  
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-ofthe-art computing and data centers. 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
more » ... stems have to be adequately adapted in order to efficiently schedule jobs with optimized performance while limiting power usage whenever needed. This paper introduces a new scheduling strategy that provides the capability to autonomously adapt the executed workload to a limited power budget. The originality of this approach relies upon a combination of DVFS (Dynamic Voltage and Frequency 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 petaflopic supercomputer Curie. Pmax−P dvf s Pmax−P of f . In the third case, we choose
doi:10.1109/ipdpsw.2015.118 dblp:conf/ipps/GeorgiouGT15 fatcat:ea3awsrikfettiliebxkjc7eii