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

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...

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Vydané v:	2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC) s. 1 - 11
Hlavní autori:	Yang, Xu, Zhou, Zhou, Wallace, Sean, Lan, Zhiling, Tang, Wei, Coghlan, Susan, Papka, Michael E.
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	New York, NY, USA ACM 17.11.2013
Edícia:	ACM Conferences
Predmet:	Computer systems organization Computer systems organization > Dependable and fault-tolerant systems and networks Electricity Electricity Bill General and reference General and reference > Cross-computing tools and techniques General and reference > Cross-computing tools and techniques > Performance Hardware Job Scheduling Networks Networks > Network performance evaluation Power Power demand Pricing Production Resource management Supercomputers System Utilization Theory of computation Theory of computation > Design and analysis of algorithms Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis > Scheduling algorithms Theory of computation > Design and analysis of algorithms > Online algorithms Theory of computation > Design and analysis of algorithms > Online algorithms > Online learning algorithms Theory of computation > Design and analysis of algorithms > Online algorithms > Online learning algorithms > Scheduling algorithms Theory of computation > Theory and algorithms for application domains Theory of computation > Theory and algorithms for application domains > Machine learning theory Theory of computation > Theory and algorithms for application domains > Machine learning theory > Reinforcement learning Theory of computation > Theory and algorithms for application domains > Machine learning theory > Reinforcement learning > Sequential decision making power system utilization electricity bill job scheduling
ISBN:	9781450323789, 1450323782
ISSN:	2167-4329
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