research-article

Storage modeling for power estimation

Authors:

Miriam Allalouf,

Yuriy Arbitman,

Michael Factor,

Dalit NaorAuthors Info & Claims

SYSTOR '09: Proceedings of SYSTOR 2009: The Israeli Experimental Systems Conference

Article No.: 3, Pages 1 - 10

https://doi.org/10.1145/1534530.1534535

Published: 04 May 2009 Publication History

Abstract

Power consumption is a major issue in today's datacenters. Storage typically comprises a significant percentage of datacenter power. Thus, understanding, managing, and reducing storage power consumption is an essential aspect of any efforts that address the total power consumption of datacenters. We developed a scalable power modeling method that estimates the power consumption of storage workloads. The modeling concept is based on identifying the major workload contributors to the power consumed by the disk arrays.

To estimate the power consumed by a given host workload, our method translates the workload to the primitive activities induced on the disks. In addition, we identified that I/O queues have a fundamental influence on the power consumption. Our power estimation results are highly accurate, with only 2% deviation for typical random workloads with small transfer sizes (up to 8K), and a deviation of up to 8% for workloads with large transfer sizes. We successfully integrated our modeling into a power-aware capacity planning tool to predict system power requirements and integrated it into an online storage system to provide online estimation for the power consumed.

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Cited By

Mersy GKrishnan SChien AEilam TPorter GAnderson TJosephson CPark J(2023)Toward a Life Cycle Assessment for the Carbon Footprint of DataProceedings of the 2nd Workshop on Sustainable Computer Systems10.1145/3604930.3605724(1-9)Online publication date: 9-Jul-2023
https://dl.acm.org/doi/10.1145/3604930.3605724
Machida FHasebe KAbe HKato K(2022)Analysis of Optimal File Placement for Energy-Efficient File-Sharing Cloud Storage SystemIEEE Transactions on Sustainable Computing10.1109/TSUSC.2020.30372607:1(75-86)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TSUSC.2020.3037260
Bajaj S(2021)Current Drift in Energy Efficiency Cloud ComputingResearch Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing10.4018/978-1-7998-5339-8.ch057(1198-1214)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-5339-8.ch057
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Index Terms

Storage modeling for power estimation

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Published In

cover image ACM Other conferences

SYSTOR '09: Proceedings of SYSTOR 2009: The Israeli Experimental Systems Conference

May 2009

191 pages

ISBN:9781605586236

DOI:10.1145/1534530

General Chair:
Miriam Allalouf
IBM Research
,
Program Chairs:
Michael Factor
IBM Research
,
Dror Feitelson
HUJI

Copyright © 2009 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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Hebrew University of Jerusalem
Melanox Technologies
IBM: IBM

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 May 2009

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SYSTOR '09

Sponsor:

IBM

SYSTOR '09: Proceedings of the 2009 Israeli Experimental Systems Conference

May 4 - April 6, 2009

Haifa, Israel

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Overall Acceptance Rate 108 of 323 submissions, 33%

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Cited By

Mersy GKrishnan SChien AEilam TPorter GAnderson TJosephson CPark J(2023)Toward a Life Cycle Assessment for the Carbon Footprint of DataProceedings of the 2nd Workshop on Sustainable Computer Systems10.1145/3604930.3605724(1-9)Online publication date: 9-Jul-2023
https://dl.acm.org/doi/10.1145/3604930.3605724
Machida FHasebe KAbe HKato K(2022)Analysis of Optimal File Placement for Energy-Efficient File-Sharing Cloud Storage SystemIEEE Transactions on Sustainable Computing10.1109/TSUSC.2020.30372607:1(75-86)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TSUSC.2020.3037260
Bajaj S(2021)Current Drift in Energy Efficiency Cloud ComputingResearch Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing10.4018/978-1-7998-5339-8.ch057(1198-1214)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-5339-8.ch057
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
Takci MGozel THocaoglu M(2019)Forecasting Power Consumption of IT Devices in a Data Center2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)10.1109/ISAP48318.2019.9065937(1-8)Online publication date: Dec-2019
https://doi.org/10.1109/ISAP48318.2019.9065937
Bajaj S(2018)Current Drift in Energy Efficiency Cloud ComputingCritical Research on Scalability and Security Issues in Virtual Cloud Environments10.4018/978-1-5225-3029-9.ch014(283-303)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-3029-9.ch014
Lin WWu WWang HWang JHsu C(2018)Experimental and quantitative analysis of server power model for cloud data centersFuture Generation Computer Systems10.1016/j.future.2016.11.03486(940-950)Online publication date: Sep-2018
https://doi.org/10.1016/j.future.2016.11.034
Maaloul RChaari LCousin B(2018)Energy saving in carrier-grade networksComputer Standards & Interfaces10.1016/j.csi.2017.04.00155:C(8-26)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1016/j.csi.2017.04.001
Lin WXu SHe LLi J(2017)Multi-resource scheduling and power simulation for cloud computingInformation Sciences: an International Journal10.1016/j.ins.2017.02.054397:C(168-186)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1016/j.ins.2017.02.054
Llopis PIsaila FBlas JCarretero J(2017)Model-based energy-aware data movement optimization in the storage I/O stackThe Journal of Supercomputing10.1007/s11227-017-2095-673:12(5465-5495)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s11227-017-2095-6
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