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Hot Data Identification with Multiple Bloom Filters: Block-Level Decision vs I/O Request-Level Decision

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Abstract

Hot data identification is crucial for many applications though few investigations have examined the subject. All existing studies focus almost exclusively on frequency. However, effectively identifying hot data requires equally considering recency and frequency. Moreover, previous studies make hot data decisions at the data block level. Such a fine-grained decision fits particularly well for flash-based storage because its random access achieves performance comparable with its sequential access. However, hard disk drives (HDDs) have a significant performance disparity between sequential and random access. Therefore, unlike flash-based storage, exploiting asymmetric HDD access performance requires making a coarse-grained decision. This paper proposes a novel hot data identification scheme adopting multiple bloom filters to efficiently characterize recency as well as frequency. Consequently, it not only consumes 50% less memory and up to 58% less computational overhead, but also lowers false identification rates up to 65% compared with a state-of-the-art scheme. Moreover, we apply the scheme to a next generation HDD technology, i.e., Shingled Magnetic Recording (SMR), to verify its effectiveness. For this, we design a new hot data identification based SMR drive with a coarse-grained decision. The experiments demonstrate the importance and benefits of accurate hot data identification, thereby improving the proposed SMR drive performance by up to 42%.

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Acknowledgment

We would like to thank David Schwaderer (Samsung Semiconductor Inc., USA) for his valuable comments and proofreading.

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Authors and Affiliations

  1. Division of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies, Gyeonggi-do, 17035, Korea

    Dongchul Park

  2. Intel Corporation, Hillsboro, OR, 97124, U.S.A.

    Dongchul Park

  3. Dell Storage, Eden Prairie, MN, 55344, U.S.A.

    Weiping He

  4. Department of Computer Science and Engineering, University of Minnesota–Twin Cities, Minneapolis, MN, 55455, U.S.A.

    David H. C. Du

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  1. Dongchul Park
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Correspondence to Dongchul Park.

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Park, D., He, W. & Du, D.H.C. Hot Data Identification with Multiple Bloom Filters: Block-Level Decision vs I/O Request-Level Decision. J. Comput. Sci. Technol. 33, 79–97 (2018). https://doi.org/10.1007/s11390-018-1809-4

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