research-article

DimmStore: memory power optimization for database systems

Authors:

Alexey Karyakin,

Kenneth SalemAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 12, Issue 11

Pages 1499 - 1512

https://doi.org/10.14778/3342263.33422629

Published: 01 July 2019 Publication History

Abstract

Memory can consume a substantial amount of power in database servers, yet memory power has received considerably less attention than CPU power. Memory power consumption is also highly non-proportional. Thus, memory power becomes even more significant in the common case in which a database server is either not completely busy or not completely full. In this paper, we study the application of two memory power optimization techniques - rank-aware allocation and rate-based layout - to database systems. By concentrating memory load, rather than spreading it out evenly, these techniques create and exploit memory idleness to achieve power savings. We have implemented these techniques in a prototype database system called DimmStore. DimmStore is part of a memory power testbed which includes customized hardware with direct power measurement capabilities, allowing us to measure the techniques' effectiveness. We use the testbed to empirically characterize the power saving opportunities provided by these techniques, as well as their performance impact, under YCSB and TPC-C workloads. Under simple YCSB workloads, power savings ranged up to 50%, depending on load and space utilization, with little performance impact. Savings were smaller, but still significant, for TPC-C, which has more complex data locality characteristics.

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Cited By

Ordonez CMacyna WBellatreche L(2024)Energy-Aware Analytics in the CloudProceedings of the International Workshop on Big Data in Emergent Distributed Environments10.1145/3663741.3664789(1-6)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3663741.3664789
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
Jin WJang WPark HLee JKim SLee JSolihin YHeinrich M(2023)DRAM Translation Layer: Software-Transparent DRAM Power Savings for Disaggregated MemoryProceedings of the 50th Annual International Symposium on Computer Architecture10.1145/3579371.3589051(1-13)Online publication date: 17-Jun-2023
https://dl.acm.org/doi/10.1145/3579371.3589051
Show More Cited By

DimmStore: memory power optimization for database systems
1. General and reference
  1. Cross-computing tools and techniques
2. Networks

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Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 12, Issue 11

July 2019

543 pages

ISSN:2150-8097

Editors:
Lei Chen,
Fatma Özcan

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VLDB Endowment

Publication History

Published: 01 July 2019

Published in PVLDB Volume 12, Issue 11

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Cited By

Ordonez CMacyna WBellatreche L(2024)Energy-Aware Analytics in the CloudProceedings of the International Workshop on Big Data in Emergent Distributed Environments10.1145/3663741.3664789(1-6)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3663741.3664789
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
Jin WJang WPark HLee JKim SLee JSolihin YHeinrich M(2023)DRAM Translation Layer: Software-Transparent DRAM Power Savings for Disaggregated MemoryProceedings of the 50th Annual International Symposium on Computer Architecture10.1145/3579371.3589051(1-13)Online publication date: 17-Jun-2023
https://dl.acm.org/doi/10.1145/3579371.3589051
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
Michalke AGrulich PLutz CZeuch SMarkl V(2021)An Energy-Efficient Stream Join for the Internet of ThingsProceedings of the 17th International Workshop on Data Management on New Hardware10.1145/3465998.3466005(1-6)Online publication date: 20-Jun-2021
https://dl.acm.org/doi/10.1145/3465998.3466005

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