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

Published: 17 November 2013 Publication History

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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Published In

cover image ACM Conferences
SC '13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
November 2013
1123 pages
ISBN:9781450323789
DOI:10.1145/2503210
  • General Chair:
  • William Gropp,
  • Program Chair:
  • Satoshi Matsuoka
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 17 November 2013

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Author Tags

  1. electricity bill
  2. job scheduling
  3. power
  4. system utilization

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SC '13 Paper Acceptance Rate 91 of 449 submissions, 20%;
Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

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Cited By

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  • (2024)Reinforcement Learning-based Adaptive Mitigation of Uncorrected DRAM Errors in the FieldProceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing10.1145/3625549.3658686(240-252)Online publication date: 3-Jun-2024
  • (2024)Job Scheduling in High Performance Computing Systems with Disaggregated Memory Resources2024 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER59578.2024.00033(297-309)Online publication date: 24-Sep-2024
  • (2024)A review on the decarbonization of high-performance computing centersRenewable and Sustainable Energy Reviews10.1016/j.rser.2023.114019189(114019)Online publication date: Jan-2024
  • (2024)Clustering Based Job Runtime Prediction for Backfilling Using ClassificationJob Scheduling Strategies for Parallel Processing10.1007/978-3-031-74430-3_3(40-59)Online publication date: 21-Dec-2024
  • (2023)Energy-Aware Scheduling for High-Performance Computing Systems: A SurveyEnergies10.3390/en1602089016:2(890)Online publication date: 12-Jan-2023
  • (2023)Modeling Energy Consumption of Industrial Processes with Seq2Seq Machine Learning2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE)10.1109/ISIE51358.2023.10228118(1-4)Online publication date: 19-Jun-2023
  • (2023)IRLS: An Improved Reinforcement Learning Scheduler for High Performance Computing Systems2023 International Conference on System Science and Engineering (ICSSE)10.1109/ICSSE58758.2023.10227229(587-592)Online publication date: 27-Jul-2023
  • (2023)DeletePop: A DLT Execution Time Predictor Based on Comprehensive ModelingAlgorithms and Architectures for Parallel Processing10.1007/978-981-97-0862-8_9(126-145)Online publication date: 20-Oct-2023
  • (2022)DRAS: Deep Reinforcement Learning for Cluster Scheduling in High Performance ComputingIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2022.3205325(1-15)Online publication date: 2022
  • (2022)Energy-efficient Management of Data Centers using a Renewable-aware Scheduler2022 IEEE International Conference on Networking, Architecture and Storage (NAS)10.1109/NAS55553.2022.9925479(1-8)Online publication date: Oct-2022
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