survey

Energy-Efficient Database Systems: A Systematic Survey

Authors:

Bin LiaoAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 6

Article No.: 111, Pages 1 - 53

https://doi.org/10.1145/3538225

Published: 07 December 2022 Publication History

Abstract

Constructing energy-efficient database systems to reduce economic costs and environmental impact has been studied for 10 years. With the emergence of the big data age, along with the data-centric and data-intensive computing trend, the great amount of energy consumed by database systems has become a major concern in a society that pursues Green IT. However, to the best of our knowledge, despite the importance of this matter in Green IT, there have been few comprehensive or systematic studies conducted in this field. Therefore, the objective of this article is to present a literature survey with breadth and depth on existing energy management techniques for database systems. The existing literature is organized hierarchically with two major branches focusing separately on energy consumption models and energy-saving techniques. Under each branch, we first introduce some basic knowledge, then we classify, discuss, and compare existing research according to their core ideas, basic approaches, and main characteristics. Finally, based on these observations through our study, we identify multiple open issues and challenges, and provide insights for future research. It is our hope that our outcome of this work will help researchers develop more energy-efficient database systems.

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https://dl.acm.org/doi/10.1016/j.future.2024.04.059
Alvarez-Valera HMaurice ARavat FSong JRoose PValles-Parlangeau N(2024)Energy Measurement System for Data Lake: An Initial ApproachIntelligent Information and Database Systems10.1007/978-981-97-4982-9_2(15-27)Online publication date: 15-Apr-2024
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Mugeni JLynden SAmagasa TMatono A(2024)MultiMatch: Low-Resource Generalized Entity Matching Using Task-Conditioned Hyperadapters in Multitask LearningBig Data Analytics and Knowledge Discovery10.1007/978-3-031-68323-7_4(51-65)Online publication date: 26-Aug-2024
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Energy-Efficient Database Systems: A Systematic Survey

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 6

June 2023

781 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3567471

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Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 07 December 2022

Online AM: 14 June 2022

Accepted: 06 April 2022

Revised: 30 March 2022

Received: 20 April 2019

Published in CSUR Volume 55, Issue 6

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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
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Alvarez-Valera HMaurice ARavat FSong JRoose PValles-Parlangeau N(2024)Energy Measurement System for Data Lake: An Initial ApproachIntelligent Information and Database Systems10.1007/978-981-97-4982-9_2(15-27)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1007/978-981-97-4982-9_2
Mugeni JLynden SAmagasa TMatono A(2024)MultiMatch: Low-Resource Generalized Entity Matching Using Task-Conditioned Hyperadapters in Multitask LearningBig Data Analytics and Knowledge Discovery10.1007/978-3-031-68323-7_4(51-65)Online publication date: 26-Aug-2024
https://dl.acm.org/doi/10.1007/978-3-031-68323-7_4
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
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Bouza LBugeau ALannelongue L(2023)How to estimate carbon footprint when training deep learning models? A guide and reviewEnvironmental Research Communications10.1088/2515-7620/acf81b5:11(115014)Online publication date: 21-Nov-2023
https://doi.org/10.1088/2515-7620/acf81b
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