Power and energy-aware processor scheduling (abstracts only)

Power consumption is a critical consideration in high computing systems. We propose a novel job scheduler that optimizes power and energy consumed by clusters when running parallel benchmarks with minimal impact on performance. We construct accurate models for estimating power consumption. These models are based on measurements of power consumption on benchmarks with different characteristics and on systems with processors using different microarchitectures. We show the power estimation models achieve less than 2% error versus actual measurements. We show a job scheduler can be enhanced to make it "power-aware" and to optimize power consumption of jobs with similar performance characteristics. The enhanced scheduler can estimate the power consumed by a particular job using the power estimation model, configure the nodes in the cluster via suitably adjusting processor frequency on each of the nodes to maximize performance, minimize power, or minimize energy with a predictable impact on power, energy and performance.