Energy-Efficient In-Memory Data Stores on Hybrid Memory Hierarchies

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Increasingly large amounts of data are stored in main memory of data center servers. However, DRAM-based memory is an important consumer of energy and is unlikely to scale in the future. Various byte-addressable non-volatile memory (NVM) technologies promise high density and near-zero static energy, however they suffer from increased latency and increased dynamic energy consumption.

This paper proposes to leverage a hybrid memory architecture, consisting of both DRAM and NVM, by novel, application-level data management policies that decide to place data on DRAM vs. NVM. We analyze modern column-oriented and key-value data stores and demonstrate the feasibility of application-level data management. Cycle-accurate simulation confirms that our methodology reduces the energy with least performance degradation as compared to the current state-of-the-art hardware or OS approaches. Moreover, we utilize our techniques to apportion DRAM and NVM memory sizes for these workloads.
Original languageEnglish
Title of host publicationProceedings of the 11th International Workshop on Data Management on New Hardware
Place of PublicationNew York
PublisherACM
Number of pages8
ISBN (Print)9781450336383
DOIs
Publication statusPublished - Jun 2015
EventEleventh International Workshop on Data Management on New Hardware (DaMoN 2015) - Melbourne, Australia
Duration: 01 Jun 201501 Jun 2015

Conference

ConferenceEleventh International Workshop on Data Management on New Hardware (DaMoN 2015)
CountryAustralia
CityMelbourne
Period01/06/201501/06/2015

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  • Cite this

    Hassan, A., Vandierendonck, H., & Nikolopoulos, D. S. (2015). Energy-Efficient In-Memory Data Stores on Hybrid Memory Hierarchies. In Proceedings of the 11th International Workshop on Data Management on New Hardware [1] ACM. https://doi.org/10.1145/2771937.2771940