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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Veröffentlicht in:2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC) S. 1 - 11
Hauptverfasser: Yang, Xu, Zhou, Zhou, Wallace, Sean, Lan, Zhiling, Tang, Wei, Coghlan, Susan, Papka, Michael E.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 17.11.2013
Schriftenreihe:ACM Conferences
Schlagworte:
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
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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%.
ISBN:9781450323789
1450323782
ISSN:2167-4329
DOI:10.1145/2503210.2503264