NDLI: A data driven scheduling approach for power management on HPC systems

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A data driven scheduling approach for power management on HPC systems

Content Provider	ACM Digital Library
Author	Allcock, William E. Coghlan, Susan Wallace, Sean Lan, Zhiling Vishwanath, Venkatram Yang, Xu Papka, Michael E.
Abstract	Modern schedulers running on HPC systems traditionally consider the number of resources and the time requested for each job that is to be executed when making scheduling decisions. Until recently this has been sufficient, however as systems get larger, other metrics like power consumption become necessary to ensure system stability. In this paper, we propose a data driven scheduling approach for controlling the power consumption of the entire system under any user defined budget. Here, "data driven" means that our approach actively observes, analyzes, and assesses power behaviors of the system and user jobs to guide scheduling decisions for power management. This design is based on the key observation that HPC jobs have distinct power profiles. Our work contains an empirical analysis of workload power characteristics on a production system, dynamic learner to estimate the job power profile for scheduling, and an online power-aware scheduler for managing the overall system power. Using real workload traces, we demonstrate that our design effectively controls system power consumption while minimizing the impact on system utilization.
Starting Page	1
Ending Page	11
Page Count	11
File Format	PDF
ISBN	9781467388153
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-11-13
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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