Integrating dynamic pricing of electricity into energy aware scheduling for HPC systems

Authors:
Xu Yang;Zhou Zhou;Sean Wallace;Zhiling Lan;Wei Tang;Susan Coghlan;Michael E. Papka
Affiliations:
Illinois Institute of Technology, Chicago, IL;Illinois Institute of Technology, Chicago, IL;Illinois Institute of Technology, Chicago, IL;Illinois Institute of Technology, Chicago, IL;Argonne National Laboratory, Argonne, IL;Argonne National Laboratory, Argonne, IL;Argonne National Laboratory, Argonne, IL
Venue:
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Year:
2013

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Abstract

The research literature to date mainly aimed at reducing energy consumption in HPC environments. In this paper we propose a job power aware scheduling mechanism to reduce HPC's electricity bill without degrading the system utilization. The novelty of our job scheduling mechanism is its ability to take the variation of electricity price into consideration as a means to make better decisions of the timing of scheduling jobs with diverse power profiles. We verified the effectiveness of our design by conducting trace-based experiments on an IBM Blue Gene/P and a cluster system as well as a case study on Argonne's 48-rack IBM Blue Gene/Q system. Our preliminary results show that our power aware algorithm can reduce electricity bill of HPC systems as much as 23%.