research-article

An analysis of memory power consumption in database systems

Authors:

Alexey Karyakin,

Kenneth SalemAuthors Info & Claims

DAMON '17: Proceedings of the 13th International Workshop on Data Management on New Hardware

Article No.: 2, Pages 1 - 9

https://doi.org/10.1145/3076113.3076117

Published: 14 May 2017 Publication History

Abstract

The growing appetite for in-memory computing is increasing memory's share of total server power consumption. However, memory power consumption in database management systems is not well understood. This paper presents an empirical characterization of memory power consumption in database systems, for both analytical and transactional workloads. Our results indicate that memory power optimization will be effective only if it can reduce back-ground power through more aggressive use of low power memory idle states.

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Cited By

Gsteiger VLong PSun YJavanrood PShahrad MWitchel EArpaci-Dusseau ARossbach CKeeton K(2024)Caribou: Fine-Grained Geospatial Shifting of Serverless Applications for SustainabilityProceedings of the ACM SIGOPS 30th Symposium on Operating Systems Principles10.1145/3694715.3695954(403-420)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3694715.3695954
Guo BWu JPu YZhang JYu J(2024)Energy consumption estimation and profiling for queries in distributed database systems based on a bottom-up comprehensive energy modelFuture Generation Computer Systems10.1016/j.future.2024.04.059159:C(379-394)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.future.2024.04.059
Souter NBhagwat NRacey CWilkinson RDuncan NSamuel GLannelongue LSelvan RRae C(2024) Measuring and reducing the carbon footprint of fMRI preprocessing in fMRIPrep Human Brain Mapping10.1002/hbm.7000345:12Online publication date: 26-Aug-2024
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cover image ACM Other conferences

DAMON '17: Proceedings of the 13th International Workshop on Data Management on New Hardware

May 2017

70 pages

ISBN:9781450350259

DOI:10.1145/3076113

Copyright © 2017 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

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Publication History

Published: 14 May 2017

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SIGMOD/PODS'17

SIGMOD/PODS'17: International Conference on Management of Data

May 14 - 19, 2017

Illinois, Chicago

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Overall Acceptance Rate 94 of 127 submissions, 74%

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Cited By

Gsteiger VLong PSun YJavanrood PShahrad MWitchel EArpaci-Dusseau ARossbach CKeeton K(2024)Caribou: Fine-Grained Geospatial Shifting of Serverless Applications for SustainabilityProceedings of the ACM SIGOPS 30th Symposium on Operating Systems Principles10.1145/3694715.3695954(403-420)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3694715.3695954
Guo BWu JPu YZhang JYu J(2024)Energy consumption estimation and profiling for queries in distributed database systems based on a bottom-up comprehensive energy modelFuture Generation Computer Systems10.1016/j.future.2024.04.059159:C(379-394)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.future.2024.04.059
Souter NBhagwat NRacey CWilkinson RDuncan NSamuel GLannelongue LSelvan RRae C(2024) Measuring and reducing the carbon footprint of fMRI preprocessing in fMRIPrep Human Brain Mapping10.1002/hbm.7000345:12Online publication date: 26-Aug-2024
https://doi.org/10.1002/hbm.70003
Baidoo CYaokumah WOwusu E(2023)Estimating Overhead Performance of Supervised Machine Learning Algorithms for Intrusion DetectionInternational Journal of Information Technologies and Systems Approach10.4018/IJITSA.31688916:1(1-19)Online publication date: 3-Feb-2023
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Dembele SBellatreche LLorusso AMarongiu FSantaniello D(2023)In-Memory Database Query Energy Estimation: Modeling & Green Strategy Support2023 IEEE World Conference on Applied Intelligence and Computing (AIC)10.1109/AIC57670.2023.10263900(278-285)Online publication date: 29-Jul-2023
https://doi.org/10.1109/AIC57670.2023.10263900
Rehman TSanyal DChattopadhyay SBhowmick PDas P(2023)Generation of Highlights From Research Papers Using Pointer-Generator Networks and SciBERT EmbeddingsIEEE Access10.1109/ACCESS.2023.329230011(91358-91374)Online publication date: 2023
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Lannelongue LAronson HBateman ABirney ECaplan TJuckes MMcEntyre JMorris AReilly GInouye M(2023)GREENER principles for environmentally sustainable computational scienceNature Computational Science10.1038/s43588-023-00461-y3:6(514-521)Online publication date: 26-Jun-2023
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Li HShen YXi HXiao Y(2022)Complementary in Time and Space: Optimization on Cost and Performance with Multiple Resources Usage by Server Consolidation in Cloud Data CenterApplied Sciences10.3390/app1219965412:19(9654)Online publication date: 26-Sep-2022
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Guo BYu JYang DLeng HLiao B(2022)Energy-Efficient Database Systems: A Systematic SurveyACM Computing Surveys10.1145/353822555:6(1-53)Online publication date: 14-Jun-2022
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Cao YCheng MZhang SMao HWang PLi CFeng YDing Z(2022)Data-driven flexibility assessment for internet data center towards periodic batch workloadsApplied Energy10.1016/j.apenergy.2022.119665324(119665)Online publication date: Oct-2022
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