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.

References

[1]

F. Ahmad and T. Vijaykumar, "Joint Optimization of Idle and Cooling Power in Data Centers while Maintaining Response Time," in Proceedings of Architectural Support for Programming Languages and Operating Systems, 2010, pp. 243--256.

Digital Library

[2]

H. Amur, K. Schwan, and M. Prvulovic, "Towards Optimal Power Management: Estimation of Performance Degradation due to DVFS on Modern Processors," Georgia Tech, Tech. Rep. GIT-CERCS-10-02, 2010.

[3]

L. A. Barroso and U. Holzle, The Datacenter as a Computer. Morgan and Claypool Publishers, 2009.

[4]

C. Bienia and K. Li, "PARSEC 2.0: A New Benchmark Suite for Chipmultiprocessors," in In Proceedings of the Annual Workshop on Modeling, Benchmarking and Simulation, 2009.

[5]

R. Cochran, C. Hankendi, A. Coskun, and S. Reda, "Identifying the Optimal Energy-Efficient Operating Points of Parallel Workloads," in ACM/IEEE International Conference on Computer-Aided Design, 2011, pp. 608--615.

Digital Library

[6]

S. Eyerman and L. Eeckhout, "A Counter Architecture for Online DVFS Profitability Estimation," IEEE Transactions on Computers, vol. 59, no. 11, pp. 1576--1583, 2010.

Digital Library

[7]

X. Fan, W. Weber, and L. Barroso, "Power Provisioning for a Warehouse-sized Computer," International Symposium on Computer Architecture, pp. 13--23, 2007.

Digital Library

[8]

A. Gandhi, M. Harchol-Balter, and R. Das, "Optimal Power Allocation in Server Farms," in International Conference on Measurement and Modeling of Computer Systems, 2009, pp. 157--168.

Digital Library

[9]

C. Isci, A. Buyuktosunoglu, C.-Y. Cher, P. Bose, and M. Martonosi, "An Analysis of Efficient Multi-Core Global Power Management Policies: Maximizing Performance for a Given Power Budget," in International Symposium on Microarchitecture, 2006, pp. 347--358.

Digital Library

[10]

C. Lefurgy, X. Wang, and M. Ware, "Power Capping: A Prelude to Power Shifting," Cluster Computing, vol. 11, pp. 183--105, 2008.

Digital Library

[11]

J. Leverich and C. Kozyrakis, "On the Energy (In)efficiency of Hadoop Clusters," ACM SIGOPS Operating Systems Review, vol. 44, no. 1, pp. 61--65, 2010.

Digital Library

[12]

J. Moore, J. Chase, P. Ranganathan, and R. Sharma, "Making Scheduling "Cool": Temperature-Aware Workload Placement in Data Centers," in Proceedings of USENIX Annual Technical Conference, 2005, pp. 61--75.

Digital Library

[13]

R. Nathuji, C. Isci, E. Gorbatov, and K. Schwan, "Providing Platform Heterogeneity-Awareness for Data Center Power Management," Cluster Computing, vol. 11, pp. 159--271, 2008.

Digital Library

[14]

C. Patel and A. Shah, "Cost Model for Planning, Development and Operation of a Data Center," Hewlett-Packard Laboratories Technical Report, 2005.

[15]

D. Pisinger, "Algorithms for Knapsack Problems," Ph.D. dissertation, University of Copenhagen, 1995.

[16]

R. Raghavendra, P. Ranganathan, V. Talwar, Z. Wang, and X. Zhu, "No "Power" Struggles: Coordinated Multi-Level Power Management for the Data Center," in Architectural Support for Programming Languages and Operating Systems, 2008, pp. 48--59.

Digital Library

[17]

K. Rajamani, H. Hanson, J. Rubio, S. Ghiasi, and F. Rawson, "Application-Aware Power Management," in International Workshop on Workload Characterization, 2006, pp. 39--48.

[18]

J. Sartori and R. Kumar, "Three Scalable Approaches to Improving Many-core Throughput for a Given Peak Power Budget," in International Conference on High-Performance Computing, 2009, pp. 89--98.

[19]

C. D. Spradling, "SPEC 2006 Benchmark Tools," SIGARCH Computer Architecture News, vol. 35, no. 1, pp. 13--134, 2007.

Digital Library

[20]

TileFlow, "http://inres.com/products/tileflow."

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

Recommendations

Dynamo: facebook's data center-wide power management system
ISCA'16

Data center power is a scarce resource that often goes underutilized due to conservative planning. This is because the penalty for overloading the data center power delivery hierarchy and tripping a circuit breaker is very high, potentially causing long ...
Power Budgeting Techniques for Data Centers
The development of cloud computing and data science result in rapid increases of number and scale of data centers. Because of cost and sustainability concerns, energy efficiency has been a major goal for data center architects. Focusing on reducing the ...
Analyzing Energy-Time Tradeoff in Power Overprovisioned HPC Data Centers
IPDPSW '15: Proceedings of the 2015 IEEE International Parallel and Distributed Processing Symposium Workshop

Minimizing energy and power consumption of large scale data centers is one of the biggest challenges faced by the high performance computing community. In an over provisioned data center, nodes are power capped to run below their Thermal Design Power (...

Comments

Information & Contributors

Information

Published In

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]

Sponsors

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

In-Cooperation

SIGBED: ACM Special Interest Group on Embedded Systems

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 May 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

DAC '13

Sponsor:

EDAC
SIGDA

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

May 29 - June 7, 2013

Texas, Austin

Acceptance Rates

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

Upcoming Conference

DAC '25

Sponsor:
sigda

62nd ACM/IEEE Design Automation Conference

June 22 - 26, 2025

San Francisco , CA , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

24
Total Citations
View Citations
295
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)0

Reflects downloads up to 27 Jan 2025

Other Metrics

View Author Metrics

Citations

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
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten