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Analyzing the energy efficiency of a database server

Authors:

Dimitris Tsirogiannis,

Stavros Harizopoulos,

Mehul A. ShahAuthors Info & Claims

SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

Pages 231 - 242

https://doi.org/10.1145/1807167.1807194

Published: 06 June 2010 Publication History

Abstract

Rising energy costs in large data centers are driving an agenda for energy-efficient computing. In this paper, we focus on the role of database software in affecting, and, ultimately, improving the energy efficiency of a server. We first characterize the power-use profiles of database operators under different configuration parameters. We find that common database operations can exercise the full dynamic power range of a server, and that the CPU power consumption of different operators, for the same CPU utilization, can differ by as much as 60%. We also find that for these operations CPU power does not vary linearly with CPU utilization.

We then experiment with several classes of database systems and storage managers, varying parameters that span from different query plans to compression algorithms and from physical layout to CPU frequency and operating system scheduling. Contrary to what recent work has suggested, we find that within a single node intended for use in scale-out (shared-nothing) architectures, the most energy-efficient configuration is typically the highest performing one. We explain under which circumstances this is not the case, and argue that these circumstances do not warrant a retargeting of database system optimization goals. Further, our results reveal opportunities for cross-node energy optimizations and point out directions for new scale-out architectures.

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Cited By

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
Huang QWang XGao QCarraro ASozzi MMarinello F(2024)Indicators to Digitization Footprint and How to Get Digitization Footprint (Part 2)Computers and Electronics in Agriculture10.1016/j.compag.2024.109206224(109206)Online publication date: Sep-2024
https://doi.org/10.1016/j.compag.2024.109206
Dembele SDe Simone MLorusso ASantaniello D(2024)A Comprehensive Energy Modeling Approach for Query Processing: Steps and Machine Learning InfluenceProceedings of Ninth International Congress on Information and Communication Technology10.1007/978-981-97-5035-1_10(131-143)Online publication date: 23-Oct-2024
https://doi.org/10.1007/978-981-97-5035-1_10
Show More Cited By

Index Terms

Analyzing the energy efficiency of a database server
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Database query processing
  2. Information retrieval
    1. Evaluation of retrieval results
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query processing and optimization (theory)

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Published In

cover image ACM Conferences

SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

June 2010

1286 pages

ISBN:9781450300322

DOI:10.1145/1807167

General Chair:
Ahmed Elmagarmid
Purdue University, USA
,
Program Chair:
Divyakant Agrawal
University of California at Santa Barbara, 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]

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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

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Published: 06 June 2010

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SIGMOD/PODS '10

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SIGMOD

SIGMOD/PODS '10: International Conference on Management of Data

June 6 - 10, 2010

Indiana, Indianapolis, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

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
Huang QWang XGao QCarraro ASozzi MMarinello F(2024)Indicators to Digitization Footprint and How to Get Digitization Footprint (Part 2)Computers and Electronics in Agriculture10.1016/j.compag.2024.109206224(109206)Online publication date: Sep-2024
https://doi.org/10.1016/j.compag.2024.109206
Dembele SDe Simone MLorusso ASantaniello D(2024)A Comprehensive Energy Modeling Approach for Query Processing: Steps and Machine Learning InfluenceProceedings of Ninth International Congress on Information and Communication Technology10.1007/978-981-97-5035-1_10(131-143)Online publication date: 23-Oct-2024
https://doi.org/10.1007/978-981-97-5035-1_10
Liu JLi GHuang QBilal MXu XSong H(2023)Cooperative Resource Allocation for Computation-Intensive IIoT Applications in Aerial ComputingIEEE Internet of Things Journal10.1109/JIOT.2022.322234010:11(9295-9307)Online publication date: 1-Jun-2023
https://doi.org/10.1109/JIOT.2022.3222340
Bellatreche LDjellali FMacyna WOrdonez C(2023)Energy-Aware Query Processing: A Case Study on Join Reordering2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386332(3743-3752)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386332
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
Cecere LColace FLorusso AMarongiu FPellegrino MSantaniello D(2023)IoT and Deep Learning for Smart Energy ManagementProceedings of Eighth International Congress on Information and Communication Technology10.1007/978-981-99-3043-2_86(1037-1046)Online publication date: 1-Sep-2023
https://doi.org/10.1007/978-981-99-3043-2_86
Guo BYu JYang DLeng HLiao B(2022)Energy-Efficient Database Systems: A Systematic SurveyACM Computing Surveys10.1145/353822555:6(1-53)Online publication date: 7-Dec-2022
https://dl.acm.org/doi/10.1145/3538225
He WCafarella MIves ZBonifati AEl Abbadi A(2022)Controlled Intentional Degradation in Analytical Video SystemsProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3517899(2105-2119)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3517899
Bouhatous ABellatreche LAbdelwahed EOrdonez C(2022)The Impact of Multicore CPUs on Eco-Friendly Query Processors in Big Data Warehouses2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020703(4463-4472)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020703
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