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 |
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| Hauptverfasser: | , , , , , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
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New York, NY, USA
ACM
17.11.2013
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| Schriftenreihe: | ACM Conferences |
| Schlagworte: |
Theory of computation
> Design and analysis of algorithms
> Approximation algorithms analysis
> Scheduling 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
> Machine learning theory
> Reinforcement learning
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| ISBN: | 9781450323789, 1450323782 |
| ISSN: | 2167-4329 |
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| Abstract | 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%. |
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| AbstractList | 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%. |
| Author | Coghlan, Susan Zhou, Zhou Tang, Wei Papka, Michael E. Yang, Xu Wallace, Sean Lan, Zhiling |
| Author_xml | – sequence: 1 givenname: Xu surname: Yang fullname: Yang, Xu email: xyang56@hawk.iit.edu organization: Illinois Institute of Technology, Chicago, IL – sequence: 2 givenname: Zhou surname: Zhou fullname: Zhou, Zhou email: zzhou1@hawk.iit.edu organization: Illinois Institute of Technology, Chicago, IL – sequence: 3 givenname: Sean surname: Wallace fullname: Wallace, Sean email: swallac6@hawk.iit.edu organization: Illinois Institute of Technology, Chicago, IL – sequence: 4 givenname: Zhiling surname: Lan fullname: Lan, Zhiling email: lan@iit.edu organization: Illinois Institute of Technology, Chicago, IL – sequence: 5 givenname: Wei surname: Tang fullname: Tang, Wei email: wtang@anl.gov organization: Argonne National Laboratory, Argonne, IL – sequence: 6 givenname: Susan surname: Coghlan fullname: Coghlan, Susan email: smc@anl.gov organization: Argonne National Laboratory, Argonne, IL – sequence: 7 givenname: Michael E. surname: Papka fullname: Papka, Michael E. email: papka@anl.gov organization: Argonne National Laboratory, Argonne, IL |
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| Keywords | power system utilization electricity bill job scheduling |
| Language | English |
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| Snippet | The research literature to date mainly aimed at reducing energy consumption in HPC environments. In this paper we propose a job power aware scheduling... |
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| SubjectTerms | 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 |
| Title | Integrating dynamic pricing of electricity into energy aware scheduling for HPC systems |
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