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No "power" struggles: coordinated multi-level power management for the data center

Authors:

Ramya Raghavendra,

Parthasarathy Ranganathan,

Xiaoyun ZhuAuthors Info & Claims

ACM SIGOPS Operating Systems Review, Volume 42, Issue 2

Pages 48 - 59

https://doi.org/10.1145/1353535.1346289

Published: 01 March 2008 Publication History

Abstract

Power delivery, electricity consumption, and heat management are becoming key challenges in data center environments. Several past solutions have individually evaluated different techniques to address separate aspects of this problem, in hardware and software, and at local and global levels. Unfortunately, there has been no corresponding work on coordinating all these solutions. In the absence of such coordination, these solutions are likely to interfere with one another, in unpredictable (and potentially dangerous) ways. This paper seeks to address this problem. We make two key contributions. First, we propose and validate a power management solution that coordinates different individual approaches. Using simulations based on 180 server traces from nine different real-world enterprises, we demonstrate the correctness, stability, and efficiency advantages of our solution. Second, using our unified architecture as the base, we perform a detailed quantitative sensitivity analysis and draw conclusions about the impact of different architectures, implementations, workloads, and system design choices.

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References

[1]

L. Barroso. The price of performance. ACM Queue, 3(7), Sept. 2005.

Digital Library

[2]

P. Bohrer et al. The case for power management in web servers. In Power Aware Computing (PACS), 2002.

Digital Library

[3]

D. Brooks and M. Martonosi. Dynamic thermal management for high-performance microprocessors. In 7th International Symposium on High-Performance Computer Architecture, 2001.

Digital Library

[4]

E.V. Carrera, E. Pinheiro, and R. Bianchini. Conserving disk energy in network servers. In 17th International Conference on Supercomputing, 2003.

Digital Library

[5]

J. Chase et al. Managing energy and server resources in hosting centers. In 18th Symposium on Operating Systems Principles (SOSP), 2001.

Digital Library

[6]

J. Chase and R. Doyle. Balance of power: Energy management for server clusters. In 8th Workshop on Hot Topics in Operating Systems, May 2001.

Digital Library

[7]

Y. Chen et al. Managing server energy and operational costs in hosting centers. In ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, June 2005.

Digital Library

[8]

CIM Specification, DMTF industry group, www.dmtf,org

[9]

B. Diniz et al. Limiting the power consumption of main memory. In 34th International Symposium on Computer Architecture (ISCA), June 2007.

Digital Library

[10]

J. Donald and M. Martonosi. Techniques for multicore thermal management: Classification and new exploration. In Proc. of the 33rd International Symposium on Computer Architecture, 2006.

Digital Library

[11]

M. Elnozahy, M. Kistler, and R. Rajamony. Energy-efficient server clusters. In Power Aware Computing Systems (PACS), February 2002.

Digital Library

[12]

X. Fan et al. Power provisioning for a warehouse-sized computer, In 34th ACM International Symposium on Computer Architecture, CA, June 2007.

Digital Library

[13]

W. Felter et al., A performance-conserving approach for reducing peak power consumption in server systems. In 19th International Conference on Supercomputing, 2005.

Digital Library

[14]

M. Femal and V. Freeh. Safe over-provisioning: Using power limits to increase aggregate throughput. In Power-Aware Computing Systems (PACS), December 2004.

Digital Library

[15]

P. Gelsinger. Intel Developer Forum, Keynote, April 2006.

[16]

The Green Grid", http://www.thegreengrid.org/home

[17]

T. Heath et al. Self-configuring heterogeneous server clusters. In Workshop on Compilers and Operating Systems for Low Power (COLP), 2003.

[18]

Hewlett Packard. HP Power Regulator for Proliant. Online. http://h18004.www1.hp.com/products/servers/management/ilo/powerregulator.html.

[19]

Intel Corporation, Motorola Corporation, and Toshiba Corporation. Advanced configuration and power interface specification, December 1996. http://www.teleport.com/acpi.

[20]

P. Juang et al. Formal coordinated, distributed energy management of chip multiprocessors, International Symposium on Low Power Electronics and Design (ISLPED-05), August, 2005

Digital Library

[21]

C. Lefurgy et al. Energy management for commercial servers. In IEEE Computer, pp. 39--48, December 2003.

Digital Library

[22]

R. Nathuji and K. Schwan. VirtualPower: Coordinated power management in virtualized enterprise systems. In Proc. of the 21st Symposium on Operating Systems Principles (SOSP), October 2007.

Digital Library

[23]

C. Patel and P. Ranganathan. Enterprise power and cooling. ASPLOS Tutorial, October 2006.

[24]

C. Patel et al. Energy flow in the information technology stack: Introducing the coefficient of the ensemble at its impact on total cost of ownership. In HP Labs Technical Report HPL-2006-55, 2006.

[25]

E. Pinheiro et al. Load balancing and unbalancing for power and performance in cluster-based systems. In Proc. of the Workshop on Compilers and Operating Systems for Low Power (COLP), 2001.

[26]

R. Raghavendra et al. "No power struggles: Coordinated multi-level power management for the data center," Hewlett Packard Technical Report, HPL-TR-2007-194, December 2007.

[27]

P. Ranganathan and P. Leech. Simulating complex enterprise workloads using utilization traces. In 10th Workshop on Computer Architecture Evaluation using Commercial Workloads (CAECW), February 2007.

[28]

P. Ranganathan et al. Ensemble-level power management for dense blade servers. In Proc. of the 33rd International Symposium on Computer Architecture (ISCA), 2006.

Digital Library

[29]

J. Rolia et al. Statistical service assurances for applications in utility grid environments. In 10th International Workshop on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), October 2002

Digital Library

[30]

D.G. Sachs et al. Grace: A cross-layer adaptation framework for saving energy. In IEEE Computer, special issue on Power-Aware Computing, December 2003.

[31]

V. Sharma et al. Power-aware QoS management in web servers. In Proc. of the Real-Time Systems Symposium, December 2003.

Digital Library

[32]

United States Environmental Protection Agency (EPA). Enterprise server and data center efficiency initiatives. http://www.energystar.gov/index.cfm?c=prod_development.server_efficiency.

[33]

A. Vahdat et al. Every joule is precious: The case for revisiting operating system design for power efficiency. In 9th ACM SIGOPS European Workshop, 2000.

Digital Library

[34]

VMware. Vmotion: Virtual machine migration. http://www.vmware.com/products/vi/vc/vmotion.html.

[35]

Z. Wang, X. Zhu, and S. Singhal. Utilization and SLO-based control for dynamic sizing of resource partitions. In 16th IFIP/IEEE Distributed Systems: Operations and Management, Oct. 2005.

Digital Library

[36]

Q. Wu et al. Formal control techniques for power-performance management, IEEE Micro, Vol. 25, No. 5, September, 2005, pp. 52--63.

Digital Library

[37]

H. Zeng et al. Ecosystem: managing energy as a first class operating system resource. In Proc. of the ASPLOS-X, pp. 123--132. ACM Press, 2002.

Digital Library

[38]

H. Zeng et al. Currentcy: A unifying abstraction for expressing energy. In Proc. of the Usenix Annual Technical Conference, June 2003.

Digital Library

Cited By

Gupta SPapadopoulou NPericàs M(2023)Accelerating CNN inference on long vector architectures via co-design2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS54959.2023.00024(145-155)Online publication date: May-2023
https://doi.org/10.1109/IPDPS54959.2023.00024
Costero LIgual FOlcoz K(2023)Dynamic power budget redistribution under a power cap on multi-application environmentsSustainable Computing: Informatics and Systems10.1016/j.suscom.2023.10086538(100865)Online publication date: Apr-2023
https://doi.org/10.1016/j.suscom.2023.100865
Nasrollahi MFathi M(2022)Modeling Big Data Enablers for Service Operations ManagementBig Data and Blockchain for Service Operations Management10.1007/978-3-030-87304-2_3(49-94)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-87304-2_3
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Index Terms

No "power" struggles: coordinated multi-level power management for the data center
1. Computer systems organization
  1. Architectures
2. Hardware
  1. Communication hardware, interfaces and storage

Recommendations

No "power" struggles: coordinated multi-level power management for the data center
ASPLOS '08

Power delivery, electricity consumption, and heat management are becoming key challenges in data center environments. Several past solutions have individually evaluated different techniques to address separate aspects of this problem, in hardware and ...
No "power" struggles: coordinated multi-level power management for the data center
ASPLOS XIII: Proceedings of the 13th international conference on Architectural support for programming languages and operating systems

Power delivery, electricity consumption, and heat management are becoming key challenges in data center environments. Several past solutions have individually evaluated different techniques to address separate aspects of this problem, in hardware and ...
No "power" struggles: coordinated multi-level power management for the data center
ASPLOS '08

Power delivery, electricity consumption, and heat management are becoming key challenges in data center environments. Several past solutions have individually evaluated different techniques to address separate aspects of this problem, in hardware and ...

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Information & Contributors

Information

Published In

cover image ACM SIGOPS Operating Systems Review

ACM SIGOPS Operating Systems Review Volume 42, Issue 2

ASPLOS '08

March 2008

339 pages

ISSN:0163-5980

DOI:10.1145/1353535

Issue’s Table of Contents

ASPLOS XIII: Proceedings of the 13th international conference on Architectural support for programming languages and operating systems
March 2008
352 pages
ISBN:9781595939586
DOI:10.1145/1346281
General Chair:
Susan Eggers
University of Washington, USA
,
Program Chair:
James Larus
Microsoft Research, USA

Copyright © 2008 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 March 2008

Published in SIGOPS Volume 42, Issue 2

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Cited By

Gupta SPapadopoulou NPericàs M(2023)Accelerating CNN inference on long vector architectures via co-design2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS54959.2023.00024(145-155)Online publication date: May-2023
https://doi.org/10.1109/IPDPS54959.2023.00024
Costero LIgual FOlcoz K(2023)Dynamic power budget redistribution under a power cap on multi-application environmentsSustainable Computing: Informatics and Systems10.1016/j.suscom.2023.10086538(100865)Online publication date: Apr-2023
https://doi.org/10.1016/j.suscom.2023.100865
Nasrollahi MFathi M(2022)Modeling Big Data Enablers for Service Operations ManagementBig Data and Blockchain for Service Operations Management10.1007/978-3-030-87304-2_3(49-94)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-87304-2_3
Mandal RMondal MBanerjee SChakraborty CBiswas U(2021)A survey and critical analysis on energy generation from datacenterData Deduplication Approaches10.1016/B978-0-12-823395-5.00005-7(203-230)Online publication date: 2021
https://doi.org/10.1016/B978-0-12-823395-5.00005-7
Patel TTiwari DWeissman JButt ASmirni E(2019)PERQProceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing10.1145/3307681.3326607(171-182)Online publication date: 17-Jun-2019
https://dl.acm.org/doi/10.1145/3307681.3326607
AL-Hazemi FLorincz JMohammed A(2019)Minimizing Data Center Uninterruptable Power Supply Overload by Server Power CappingIEEE Communications Letters10.1109/LCOMM.2019.291971723:8(1342-1346)Online publication date: Aug-2019
https://doi.org/10.1109/LCOMM.2019.2919717
Venticinque SNacchia SMaisto S(2019)Reinforcement Learning for Resource Allocation in Cloud DatacenterAdvances on P2P, Parallel, Grid, Cloud and Internet Computing10.1007/978-3-030-33509-0_61(648-657)Online publication date: 20-Oct-2019
https://doi.org/10.1007/978-3-030-33509-0_61
Shahhosseini SMoazzemi KRahmani ADutt N(2018)On the feasibility of SISO control-theoretic DVFS for power capping in CMPsMicroprocessors and Microsystems10.1016/j.micpro.2018.09.01263(249-258)Online publication date: Nov-2018
https://doi.org/10.1016/j.micpro.2018.09.012
Hankendi CCoskun A(2017)Scale & CapACM Transactions on Design Automation of Electronic Systems10.1145/299414522:2(1-22)Online publication date: 9-Jan-2017
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Yang TPen HLi WYuan DZomaya A(2017)An Energy-Efficient Storage Strategy for Cloud Datacenters Based on Variable K-Coverage of a HypergraphIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2017.272300428:12(3344-3355)Online publication date: 1-Dec-2017
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