research-article

Techniques for energy-efficient power budgeting in data centers

Authors:

Sherief RedaAuthors Info & Claims

DAC '13: Proceedings of the 50th Annual Design Automation Conference

Article No.: 176, Pages 1 - 7

https://doi.org/10.1145/2463209.2488951

Published: 29 May 2013 Publication History

Abstract

We propose techniques for power budgeting in data centers, where a large power budget is allocated among the servers and the cooling units such that the aggregate performance of the entire center is maximized. Maximizing the performance for a given power budget automatically maximizes the energy efficiency. We first propose a method to partition the total power budget among the cooling and computing units in a self-consistent way, where the cooling power is sufficient to extract the heat of the computing power. Given the computing power budget, we devise an optimal computing budgeting technique based on knapsack-solving algorithms to determine the power caps for the individual servers. The optimal computing budgeting technique leverages a proposed on-line throughput predictor based on performance counter measurements to estimate the change in throughput of heterogeneous workloads as a function of allocated server power caps. We set up a simulation environment for a data center, where we simulate the air flow and heat transfer within the center using computational fluid dynamic simulations to derive accurate cooling estimates. The power estimates for the servers are derived from measurements on a real server executing heterogeneous workload sets. Our budgeting method delivers good improvements over previous power budgeting techniques.

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Cited By

Mao LChen RCheng HLin WLiu BWang J(2023)A resource scheduling method for cloud data centers based on thermal managementJournal of Cloud Computing10.1186/s13677-023-00462-212:1Online publication date: 10-Jun-2023
https://doi.org/10.1186/s13677-023-00462-2
Wan JDuan YGui XLiu CLi LMa Z(2023)SafeCool: Safe and Energy-Efficient Cooling Management in Data Centers With Model-Based Reinforcement LearningIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.32345457:6(1621-1635)Online publication date: Dec-2023
https://doi.org/10.1109/TETCI.2023.3234545
Wang STu RChen XYang XJia K(2022)Thermal Performance Analyses and Optimization of Data Center Centralized-Cooling SystemApplied Thermal Engineering10.1016/j.applthermaleng.2022.119817(119817)Online publication date: Dec-2022
https://doi.org/10.1016/j.applthermaleng.2022.119817
Show More Cited By

Index Terms

Techniques for energy-efficient power budgeting in data centers
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Client-server architectures
2. Information systems
  1. Data management systems
    1. Middleware for databases
      1. Application servers
      2. Database web servers

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cover image ACM Conferences

DAC '13: Proceedings of the 50th Annual Design Automation Conference

May 2013

1285 pages

ISBN:9781450320719

DOI:10.1145/2463209

Copyright © 2013 ACM.

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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EDAC: Electronic Design Automation Consortium
SIGDA: ACM Special Interest Group on Design Automation
IEEE-CEDA

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SIGBED: ACM Special Interest Group on Embedded Systems

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Association for Computing Machinery

New York, NY, United States

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Published: 29 May 2013

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DAC '13

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DAC '13: The 50th Annual Design Automation Conference 2013

May 29 - June 7, 2013

Texas, Austin

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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62nd ACM/IEEE Design Automation Conference

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Cited By

Mao LChen RCheng HLin WLiu BWang J(2023)A resource scheduling method for cloud data centers based on thermal managementJournal of Cloud Computing10.1186/s13677-023-00462-212:1Online publication date: 10-Jun-2023
https://doi.org/10.1186/s13677-023-00462-2
Wan JDuan YGui XLiu CLi LMa Z(2023)SafeCool: Safe and Energy-Efficient Cooling Management in Data Centers With Model-Based Reinforcement LearningIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.32345457:6(1621-1635)Online publication date: Dec-2023
https://doi.org/10.1109/TETCI.2023.3234545
Wang STu RChen XYang XJia K(2022)Thermal Performance Analyses and Optimization of Data Center Centralized-Cooling SystemApplied Thermal Engineering10.1016/j.applthermaleng.2022.119817(119817)Online publication date: Dec-2022
https://doi.org/10.1016/j.applthermaleng.2022.119817
He XLiu JXie ZChen HChen GZhang WLi DZhou HMoreira JMueller FEtsion Y(2021)Enabling energy-efficient DNN training on hybrid GPU-FPGA acceleratorsProceedings of the 35th ACM International Conference on Supercomputing10.1145/3447818.3460371(227-241)Online publication date: 3-Jun-2021
https://dl.acm.org/doi/10.1145/3447818.3460371
Chen HZhang YCaramanis MCoskun A(2019)EnergyQAREACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/32431724:1(1-31)Online publication date: 24-Jan-2019
https://dl.acm.org/doi/10.1145/3243172
Kim YKim JChoi SYoung Kim SChung S(2019)Temperature-aware Adaptive VM Allocation in Heterogeneous Data Centers2019 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)10.1109/ISLPED.2019.8824825(1-6)Online publication date: Jul-2019
https://doi.org/10.1109/ISLPED.2019.8824825
Benoit ALefèvre LOrgerie ARaïs I(2018)Reducing the energy consumption of large-scale computing systems through combined shutdown policies with multiple constraintsInternational Journal of High Performance Computing Applications10.1177/109434201771453032:1(176-188)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1177/1094342017714530
Gupta UAyoub RKishinevsky MKadjo DSoundararajan NTursun UOgras U(2018)Dynamic Power Budgeting for Mobile Systems Running Graphics WorkloadsIEEE Transactions on Multi-Scale Computing Systems10.1109/TMSCS.2017.26834874:1(30-40)Online publication date: 1-Jan-2018
https://doi.org/10.1109/TMSCS.2017.2683487
Wan JGui XZhang RFu L(2018)Joint Cooling and Server Control in Data Centers: A Cross-Layer Framework for Holistic Energy MinimizationIEEE Systems Journal10.1109/JSYST.2017.270086312:3(2461-2472)Online publication date: Sep-2018
https://doi.org/10.1109/JSYST.2017.2700863
Salinas-Hilburg JZapater MMoya JAyala J(2018)Fast Energy Estimation Through Partial Execution of HPC Applications2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP)10.1109/ASAP.2018.8445089(1-8)Online publication date: Jul-2018
https://doi.org/10.1109/ASAP.2018.8445089
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