skip to main content
10.1145/1454115.1454153acmconferencesArticle/Chapter ViewAbstractPublication PagespactConference Proceedingsconference-collections
research-article

Multi-mode energy management for multi-tier server clusters

Published: 25 October 2008 Publication History

Abstract

This paper presents an energy management policy for reconfigurable clusters running a multi-tier application, exploiting DVS together with multiple sleep states. We develop a theoretical analysis of the corresponding power optimization problem and design an algorithm around the solution. Moreover, we rigorously investigate selection of the optimal number of spare servers for each power state, a problem that has only been approached in an ad-hoc manner in current policies.
To validate our results and policies, we implement them on an actual multi-tier server cluster where nodes support all power management techniques considered. Experimental results using realistic dynamic workloads based on the TPCW benchmark show that exploiting multiple sleep states results in significant additional cluster-wide energy savings up to 23% with little or no performance degradation.

References

[1]
L. Benini, A. Bogliolo, and G. D. Micheli. A survey of design techniques for system-level dynamic power management. IEEE Trans. VLSI Syst., 8(3):299--316, 2000.
[2]
P. Bohrer, E. N. Elnozahy, T. Keller, M. Kistler, C. Lefurgy, and R. Rajamony. The case for power management in web servers. In R. Graybill and R. Melhem, editors, Power-Aware Computing, Kluwer/Plenum series in Computer Science. Kluwer Academic Publishers, Jan. 2002.
[3]
Y. Chen, A. Das, W. Qin, A. Sivasubramaniam, Q. Wang, and N. Gautam. Managing server energy and operational costs in hosting centers. SIGMETRICS Perform. Eval. Rev., 33(1):303--314, 2005.
[4]
E. Elnozahy, M. Kistler, and R. Rajamony. Energy-efficient server clusters. In Proc. Workshop on Power-Aware Computing Systems, Feb. 2002.
[5]
X. Fan, W.-D. Weber, and L. A. Barroso. Power provisioning for a warehouse-sized computer. In Proc. 34th Annual ACM/IEEE International Symposium on Computer Architecture, pages 13--23, 2007.
[6]
C. Hang, K. Astrom, and W. Ho. Refinements of the Ziegler-Nichols tuning formula. IEE Proceedings D, Control Theory and Applications, 138(2):111--118, Mar. 1991.
[7]
T. Heath, B. Diniz, E. V. Carrera, W. M. Jr., and R. Bianchini. Energy conservation in heterogeneous server clusters. In Proc. 10th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pages 186--195, 2005.
[8]
C. hsing Hsu and W. chun Feng. When discreteness meets continuity: Energy-optimal dvs scheduling revisited. Technical Report LA-UR 05-3104, Los Alamos National Laboratory, Feb. 2005.
[9]
C. Lefurgy, K. Rajamani, F. Rawson, W. Felter, M. Kistler, and T. W. Keller. Energy management for commercial servers. IEEE Computer, 36(12):39--48, 2003.
[10]
A. Miyoshi, C. Lefurgy, E. V. Hensbergen, R. Rajamony, and R. Rajkumar. Critical power slope: understanding the runtime effects of frequency scaling. In Proc. 16th International Conference on Supercomputing, pages 35--44, 2002.
[11]
T. Mudge. Power: A first-class architectural design constraint. IEEE Computer, 34(4):52--58, 2001.
[12]
E. Pinheiro, R. Bianchini, E. Carrera, and T. Heath. Dynamic cluster reconfiguration for power and performance. In L. Benini, M. Kandemir, and J. Ramanujam, editors, Compilers and Operating Systems for Low Power. Kluwer Academic Publishers, 2002.
[13]
R. Raghavendra, P. Ranganathan, V. Talwar, Z. Wang, and X. Zhu. No "power" struggles: coordinated multi-level power management for the data center. SIGARCH Comput. Archit. News, 36(1):48--59, 2008.
[14]
K. Rajamani and C. Lefurgy. On evaluating request-distribution schemes for saving energy in server clusters. In Proc. IEEE International Symposium on Performance Analysis of Systems and Software, pages 111--122, 2003.
[15]
P. Ranganathan, P. Leech, D. Irwin, and J. Chase. Ensemble-level power management for dense blade servers. Proc. 33rd Annual ACM/IEEE International Symposium on Computer Architecture, pages 66--77, 2006.
[16]
C. Rusu, A. Ferreira, C. Scordino, and A. Watson. Energy-efficient real-time heterogeneous server clusters. In Proc. 12th IEEE Real-Time and Embedded Technology and Applications Symposium, pages 418--428, 2006.

Cited By

View all
  • (2024)Multitier scalable clustering wireless network design approach using honey bee ratel optimizationJournal of High Speed Networks10.3233/JHS-23008630:2(203-219)Online publication date: 1-Jan-2024
  • (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
  • (2022)Cost Efficient GPU Cluster Management for Training and Inference of Deep LearningEnergies10.3390/en1502047415:2(474)Online publication date: 10-Jan-2022
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
PACT '08: Proceedings of the 17th international conference on Parallel architectures and compilation techniques
October 2008
328 pages
ISBN:9781605582825
DOI:10.1145/1454115
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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 October 2008

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. dynamic power management
  2. dynamic voltage scaling
  3. end-to-end latency
  4. energy management
  5. internet servers
  6. multi-tier applications
  7. reconfigurable clusters
  8. sleep states

Qualifiers

  • Research-article

Conference

PACT '08
Sponsor:

Acceptance Rates

Overall Acceptance Rate 121 of 471 submissions, 26%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)19
  • Downloads (Last 6 weeks)3
Reflects downloads up to 20 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Multitier scalable clustering wireless network design approach using honey bee ratel optimizationJournal of High Speed Networks10.3233/JHS-23008630:2(203-219)Online publication date: 1-Jan-2024
  • (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
  • (2022)Cost Efficient GPU Cluster Management for Training and Inference of Deep LearningEnergies10.3390/en1502047415:2(474)Online publication date: 10-Jan-2022
  • (2022)User-Centric Interference-Aware Load Balancing for Cloud-Deployed ApplicationsIEEE Transactions on Cloud Computing10.1109/TCC.2019.294356010:1(736-748)Online publication date: 1-Jan-2022
  • (2021)A Three-Level Modelling Approach for Asynchronous Speed Scaling in High-Performance Data CentresProceedings of the Twelfth ACM International Conference on Future Energy Systems10.1145/3447555.3466580(417-423)Online publication date: 22-Jun-2021
  • (2021)Difference Equations Approach for Multi-Server Queueing Models with Removable ServersMethodology and Computing in Applied Probability10.1007/s11009-021-09848-824:3(1297-1321)Online publication date: 1-May-2021
  • (2019)TS-BatPro: Improving Energy Efficiency in Data Centers by Leveraging Temporal–Spatial BatchingIEEE Transactions on Green Communications and Networking10.1109/TGCN.2018.28710253:1(236-249)Online publication date: Mar-2019
  • (2019)SprintCon: Controllable and Efficient Computational Sprinting for Data Center Servers2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS.2019.00090(815-824)Online publication date: May-2019
  • (2019)Linear Power Modeling for Cloud Data Centers: Taxonomy, Locally Corrected Linear Regression, Simulation Framework and EvaluationIEEE Access10.1109/ACCESS.2019.29568817(175003-175019)Online publication date: 2019
  • (2019)Deep Learning-Based Sustainable Data Center Energy Cost Minimization With Temporal MACRO/MICRO Scale ManagementIEEE Access10.1109/ACCESS.2018.28888397(5477-5491)Online publication date: 2019
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media