Effective data prediction method for in-memory database applications

Ji Tae Yun, Su Kyung Yoon, Jeong Geun Kim, Shin Dug Kim

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

The amount of data is increasing explosively, and many in-memory-based database management systems have been developed to efficiently manage data in real time. However, these in-memory databases mainly use DRAM main memory, which raises problems due to price and energy consumption. To mitigate these problems, we propose a hybrid main memory structure based on DRAM and NAND flash that is cheaper and consumes less energy than DRAM. The proposed system incorporates a prefetching mechanism in last-level cache based on regression analysis to handle irregular memory access from the in-memory application and a migration technique based on clustering between DRAM and NAND flash to mitigate NAND flash slow access latency, which could otherwise significantly degrade system performance. We experimentally confirmed approximately 58% and 51% execution time and energy improvement compared with using DRAM alone. We also compared existing prefetching models without migration to evaluate the proposed prefetching and migration techniques and showed approximately 24% and 23% improvement for execution time and an energy consumption, respectively.

Original languageEnglish
Pages (from-to)580-601
Number of pages22
JournalJournal of Supercomputing
Volume76
Issue number1
DOIs
Publication statusPublished - 2020 Jan 1

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea funded by the Korea government (MSIT) (NRF-2019R1A2C1008716). Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Funding Information:
This work was supported by the National Research Foundation of Korea funded by the Korea government (MSIT) (NRF-2019R1A2C1008716).

Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture

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