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Effective data prediction method for in-memory database applications

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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.

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Acknowledgements

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

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  1. Yonsei University, Seoul, Republic of Korea

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

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  1. Ji-Tae Yun
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  2. Su-Kyung Yoon
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  4. Shin-Dug Kim
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Yun, JT., Yoon, SK., Kim, JG. et al. Effective data prediction method for in-memory database applications. J Supercomput 76, 580–601 (2020). https://doi.org/10.1007/s11227-019-03050-x

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