›› 2018, Vol. 33 ›› Issue (1): 79-97.doi: 10.1007/s11390-018-1809-4

Special Issue: Computer Architecture and Systems

• Computer Architecture and Systems • Previous Articles     Next Articles

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

Dongchul Park1,2, Weiping He3, David H. C. Du4, Fellow, IEEE   

  1. 1 Division of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies Gyeonggi-do 17035, Korea;
    2 Intel Corporation, Hillsboro, OR 97124, U.S.A;
    3 Dell Storage, Eden Prairie, MN 55344, U.S.A;
    4 Department of Computer Science and Engineering, University of Minnesota-Twin Cities, Minneapolis, MN 55455, U.S.A
  • Received:2016-10-06 Revised:2017-11-12 Online:2018-01-05 Published:2018-01-05
  • About author:Dongchul Park is currently an assistant professor in Division of Computer & Electronic Systems Engineering at Hankuk University of Foreign Studies (HUFS), Gyeonggi-do, South Korea. Before joining HUFS, he was a senior staff research engineer in Storage Technology Group (STG) at Intel, Hillsboro, Oregon, USA in 2017 and a senior research engineer in Memory Solutions Laboratory (MSL) at Samsung Semiconductor Inc. in San Jose, California, USA from 2012 to 2016. He received his Ph.D. degree in computer science and engineering at the University of Minnesota-Twin Cities, Minneapolis, in 2012, and was a member of Center for Research in Intelligent Storage (CRIS) group under the advice of professor David H. C. Du. His research interests focus on storage system design and applications including non-volatile memories, in-storage computing, big data processing, Hadoop MapReduce, data deduplication, key-value store, cloud computing, and shingled magnetic recording (SMR) technology.
  • Supported by:

    This work was supported by Hankuk University of Foreign Studies Research Fund of Korea, and also partially supported by the National Science Foundation (NSF) Awards of USA under Grant Nos. 1053533, 1439622, 1217569, 1305237, and 1421913.

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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