Task scheduling and voltage selection for energy minimization
Proceedings of the 39th annual Design Automation Conference
A Power-Aware Run-Time System for High-Performance Computing
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
IEEE Transactions on Parallel and Distributed Systems
ICPADS '08 Proceedings of the 2008 14th IEEE International Conference on Parallel and Distributed Systems
Towards energy-aware scheduling in data centers using machine learning
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
Some observations on optimal frequency selection in DVFS-based energy consumption minimization
Journal of Parallel and Distributed Computing
Power-aware linear programming based scheduling for heterogeneous computer clusters
Future Generation Computer Systems
Job allocation strategies for energy-aware and efficient Grid infrastructures
Journal of Systems and Software
Energy-efficient deadline scheduling for heterogeneous systems
Journal of Parallel and Distributed Computing
Parallel genetic algorithms for DVS scheduling of distributed embedded systems
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
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By becoming more popular and complex, HPC systems like computational grids, clusters, clouds and the supporting data centers are now changed to remarkable energy consumers. A wide variety of researches, ranging from power-aware hardware design to developing optimized programs and to power-aware job scheduling, have been done hitherto in order to reduce their energy consumption. However, the success of these approaches highly depends to having a precise knowledge about power consumption behavior of the target system. In this paper, some neglected facts are shown about combinational effects of jobs' and resources' characteristics on energy consumption rate and define corresponding parameters which make these facts practically utilizable by formulating them as functions of job-machine characteristics. These facts are supported by the experimental analyses on real machines and analytical studies. The outcome of this paper can be exploited to have more energy efficient task mapping process in large-scale heterogeneous computational systems.