research-article

WattApp: an application aware power meter for shared data centers

Authors:

Ricardo Koller,

Anindya NeogiAuthors Info & Claims

ICAC '10: Proceedings of the 7th international conference on Autonomic computing

Pages 31 - 40

https://doi.org/10.1145/1809049.1809055

Published: 07 June 2010 Publication History

Abstract

The increasing heterogeneity between applications in emerging virtualized data centers like clouds introduce significant challenges in estimating the power drawn by the data center. In this work, we presentWattApp: an application-aware power meter for shared data centers that addresses this challenge. In order to deal with heterogeneous applications, WattApp introduces application parameters (e.g, throughput) in the power modeling framework. WattApp is based on a carefully designed set of experiments on a mix of diverse applications: power benchmarks, web-transaction workloads, HPC workloads and I/O-intensive workloads. Given a set of N applications and M server types, WattApp runs in O(N) time, uses O(NxM) calibration runs, and predicts the power drawn by any arbitrary placement within 5%of the real power for the applications studied.

References

[1]

Afcom's data center institute issues five bold predictions for the future of the data center industry; new survey identifies labor shortage, power failures, relocation, risk management and virtualization as major issues. In Business Wire, March 23, 2006.

[2]

Control power and cooling for data center efficiency - hp thermal logic technology. an hp bladesystem innovation primer. In http://h71028.www7.hp.com/ERC/downloads/4AA0-5820ENW.pdf.

[3]

Hpl- a portable implementation of the high performance linpack benchmark for distributed memory computers. In http://www.netlib.org/benchmark/hpl/.

[4]

Ibm active energy manager. In http://www-03.ibm.com/systems/management/director/about/director52/extensions/actengmrg.html.

[5]

Ibm lotus domino. In http://www-306.ibm.com/software/lotus/products/domino/.

[6]

pseries and aix information center. In http://publib.boulder.ibm.com/infocenter/pseries/v5r3/index.jsp?topic=/com.ibm.aix.cmds/doc/aixcmds2/hpmstat.htm.

[7]

Simple linear regression. In http://en.wikipedia.org/wiki/Simple_linear_regression.

[8]

Specpower ssj2008. In http://www.spec.org/power_ssj2008/.

[9]

Tpw-w in java. In http://www.ece.wisc.edu/ pharm/tpcw.shtml.

[10]

Transaction processing performance council tpc-w. In http://www.tpc.org/tpcw/default.asp.

[11]

ASHRAE Technical Committee 9.9. Datacom equipment power trends and cooling applications, 2005.

[12]

L.A. Barroso and U. Hölzle. The case for energy proportional computing. In IEEE Computer, 2007.

Digital Library

[13]

C. Belady. In the data center, power and cooling costs more than the it equipment it supports. http://www.electronics-cooling.com/articles/2007/feb/a3/, 2007.

[14]

F. Bellosa. The benefits of event-driven enery accounting in power-sensitive systems. In Proc. SIGOPS European Workshop, 2000.

Digital Library

[15]

D. Brooks, V. Tiwari, and M. Martonosi. Wattch: A framework for architectural-level power analysis and optimizations. In Proc. ISCA, 2000.

Digital Library

[16]

M. C-Maury, A. Shah, F. Blagojevic, D.S. Nikolopoulos, B. de Supinski, and M. Schultz. Prediction models for multi-dimensional power-performance optimization on many cores. In PACT, 2008.

Digital Library

[17]

Y. Chen, A. Das, W. Qin, A. Sivasubramaniam, Q. Wang, and N. Gautam. Managing server energy and operational costs in hosting centers. In Sigmetrics, 2005.

Digital Library

[18]

L. Cherkasova and R. Gardner. Measuring cpu overhead for i/o processing in the xen virtual machine monitor. In Usenix ATC, 2005.

Digital Library

[19]

J. Choi, S. Govindan, B. Urgaonkar, and A. Sivasubramaniam. Profiling, prediction, and capping of power consumption in consolidated environments. In MASCOTS 2008.

[20]

DAXPY. http://www.netlib.org/blas/daxpy.f.

[21]

D. Economou, S. Rivoire, C. Kozyrakis, and P. Ranganathan. Full system power analysis and modeling for server environments. In WMBS, 2006.

[22]

S. Gurumurthi et al. Using complete machine simulation for software power estimation: The softwatt approach. In HPCA, 2002.

Digital Library

[23]

T. Heath, B. Diniz, E. V. Carrera, W. Meira Jr., and R. Bianchini. Energy conservation in heterogeneous server clusters. In Proc. PPoPP, 2005.

Digital Library

[24]

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 MICRO 2006.

Digital Library

[25]

John Levon. OProfile Manual. Victoria University of Manchester, 2004.

[26]

Ripal Nathuji and Karsten Schwan. Virtualpower: coordinated power management in virtualized enterprise systems. In Proc. SOSP, 2007.

Digital Library

[27]

P. Ranganathan, P. Leech, D. Irwin, and J. Chase. Ensemble-level power management for dense blade servers. In Proc. ISCA, 2006.

Digital Library

[28]

S. Rivoire, P. Ranganathan, and C. Kozyrakis. A comparison of high-level full-system power models. In HotPower, 2008.

Digital Library

[29]

D. Snowdon, S. Petters, and G. Heiser. Accurate on-line prediction of processor and memory energy usage under voltage scaling. In EMSOFT, 2007.

Digital Library

[30]

J. Stoess, C. Lang, and F. Bellosa. Energy management for hypervisor-based virtual machines. In Usenix ATC, 2007.

Digital Library

[31]

A. Verma, P. Ahuja, and A. Neogi. pmapper: Power and migration cost aware application placement in virtualized systems. In Middleware, 2008.

Digital Library

[32]

A. Verma, P. Ahuja, and A. Neogi. Power-aware dynamic placement of hpc applications. In ICS, 2008.

Digital Library

[33]

A. Verma, G. Dasgupta, T. Nayak, P. De, and R. Kothari. Server workload analysis for power minimization using consolidation. In Usenix ATC, 2009.

Digital Library

[34]

H. Zeng, C.S. Ellis, A.R. Lebeck, and A. Vahdat. Ecosystem: Managing energy as a first class operating system resource. In ASPLOS, 2002.

Digital Library

Cited By

Kosar T(2023)Towards Zero-Carbon Data Movement at the HPC and Cloud Data Centers2023 IEEE John Vincent Atanasoff International Symposium on Modern Computing (JVA)10.1109/JVA60410.2023.00019(54-55)Online publication date: 5-Jul-2023
https://doi.org/10.1109/JVA60410.2023.00019
Wiesner PScheinert DWittkopp TThamsen LKao O(2022)Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge WorkloadsEuro-Par 2022: Parallel Processing10.1007/978-3-031-12597-3_14(218-232)Online publication date: 22-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-12597-3_14
Lin WShi FWu WLi KWu GMohammed A(2020)A Taxonomy and Survey of Power Models and Power Modeling for Cloud ServersACM Computing Surveys10.1145/340620853:5(1-41)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1145/3406208
Show More Cited By

Index Terms

WattApp: an application aware power meter for shared data centers
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

Recommendations

A performance-conserving approach for reducing peak power consumption in server systems
ICS '05: Proceedings of the 19th annual international conference on Supercomputing

The combination of increasing component power consumption, a desire for denser systems, and the required performance growth in the face of technology-scaling issues are posing enormous challenges for powering and cooling of server systems. The ...
Energy accounting for shared virtualized environments under DVFS using PMC-based power models

Virtualized infrastructure providers demand new methods to increase the accuracy of the accounting models used to charge their customers. Future data centers will be composed of many-core systems that will host a large number of virtual machines (VMs) ...
The challenges of implementing fine-grained power gating
GLSVLSI '10: Proceedings of the 20th symposium on Great lakes symposium on VLSI

Power consumption in digital systems, especially in portable devices, is a crucial design factor. Due to downscaling of technology, dynamic switching power is not the only relevant source of power consumption anymore as power dissipation caused by ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ICAC '10: Proceedings of the 7th international conference on Autonomic computing

June 2010

246 pages

ISBN:9781450300742

DOI:10.1145/1809049

General Chair:
Manish Parashar
Rutgers University, USA
,
Program Chairs:
Renato Figueiredo
University of Florida, USA
,
Emre Kıcıman
Microsoft Research, USA

Copyright © 2010 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

SIGARCH: ACM Special Interest Group on Computer Architecture

In-Cooperation

IEEE
University of Arizona: University of Arizona

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 June 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tag

power modeling

Qualifiers

Research-article

Conference

ICAC '10

Sponsor:

SIGARCH

ICAC '10: 7th International Conference on Autonomic Computing

June 7 - 11, 2010

DC, Washington, USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

68
Total Citations
View Citations
461
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)0

Reflects downloads up to 02 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Kosar T(2023)Towards Zero-Carbon Data Movement at the HPC and Cloud Data Centers2023 IEEE John Vincent Atanasoff International Symposium on Modern Computing (JVA)10.1109/JVA60410.2023.00019(54-55)Online publication date: 5-Jul-2023
https://doi.org/10.1109/JVA60410.2023.00019
Wiesner PScheinert DWittkopp TThamsen LKao O(2022)Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge WorkloadsEuro-Par 2022: Parallel Processing10.1007/978-3-031-12597-3_14(218-232)Online publication date: 22-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-12597-3_14
Lin WShi FWu WLi KWu GMohammed A(2020)A Taxonomy and Survey of Power Models and Power Modeling for Cloud ServersACM Computing Surveys10.1145/340620853:5(1-41)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1145/3406208
Ismail LMaterwala H(2020)Computing Server Power Modeling in a Data CenterACM Computing Surveys10.1145/339060553:3(1-34)Online publication date: 12-Jun-2020
https://dl.acm.org/doi/10.1145/3390605
Chang HKodialam MLakshman TMukherjee S(2019)Microservice Fingerprinting and Classification using Machine Learning2019 IEEE 27th International Conference on Network Protocols (ICNP)10.1109/ICNP.2019.8888077(1-11)Online publication date: Oct-2019
https://doi.org/10.1109/ICNP.2019.8888077
Arora SBala A(2019)A survey: ICT enabled energy efficiency techniques for big data applicationsCluster Computing10.1007/s10586-019-02958-6Online publication date: 16-Jul-2019
https://doi.org/10.1007/s10586-019-02958-6
Tziritas NLoukopoulos TKhan SXu CZomaya A(2018)A Communication-Aware Energy-Efficient Graph-Coloring Algorithm for VM Placement in Clouds2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)10.1109/SmartWorld.2018.00286(1684-1691)Online publication date: Oct-2018
https://doi.org/10.1109/SmartWorld.2018.00286
Zulkar Nine MDi Tacchio LImran AKosar TBulut MHwang J(2018)GreenDataFlow: Minimizing the Energy Footprint of Global Data Movement2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8622570(335-342)Online publication date: Dec-2018
https://doi.org/10.1109/BigData.2018.8622570
Taniguchi YSuganuma KDeguchi THasegawa GNakamura YUkita NAizawa NShibata KMatsuda KMatsuoka M(2017)Tandem Equipment Arranged Architecture with Exhaust Heat Reuse System for Software-Defined Data Center InfrastructureIEEE Transactions on Cloud Computing10.1109/TCC.2015.24402455:2(182-192)Online publication date: 1-Apr-2017
https://doi.org/10.1109/TCC.2015.2440245
Jiang WLiu FTang GWu KJin H(2017)Virtual Machine Power Accounting with Shapley Value2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS.2017.235(1683-1693)Online publication date: Jun-2017
https://doi.org/10.1109/ICDCS.2017.235
Show More Cited By

View Options

Get Access

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.

Media

Figures

Other

Tables

View Table of Contents