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쓰기요청의 연속성과 발생빈도를 고려한 hot data 검증기법

Hot Data Verification Method Considering Continuity and Frequency of Write Requests Using Counting Filter

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초록

Hard disks, which have long been used as secondary storage in computing systems, are increasingly being replaced by solid state drives (SSDs), due to their relatively fast data input / output speeds and small, light weight. SSDs that use NAND flash memory as a storage medium are significantly different from hard disks in terms of physical operation and internal operation. In particular, there is a feature that data overwrite can not be performed, which causes erase operation before writing. In order to solve this problem, a hot data for frequently updating a data for a specific page is distinguished from a cold data for a relatively non-hot data. Hot data identification helps to improve overall performance by identifying and managing hot data separately. Among the various hot data identification methods known so far, there is a technique of recording consecutive write requests by using a Bloom filter and judging the values by hot data. However, the Bloom filter technique has a problem that a new bit array must be generated every time a set of items is changed. In addition, since it is judged based on a continuous write request, it is possible to make a wrong judgment. In this paper, we propose a method using a counting filter for accurate hot data verification. The proposed method examines consecutive write requests. It also records the number of times consecutive write requests occur. The proposed method enables more accurate hot data verification.

Abstract

Hard disks, which have long been used as secondary storage in computing systems, are increasingly being replaced by solid state drives (SSDs), due to their relatively fast data input / output speeds and small, light weight. SSDs that use NAND flash memory as a storage medium are significantly different from hard disks in terms of physical operation and internal operation. In particular, there is a feature that data overwrite can not be performed, which causes erase operation before writing. In order to solve this problem, a hot data for frequently updating a data for a specific page is distinguished from a cold data for a relatively non-hot data. Hot data identification helps to improve overall performance by identifying and managing hot data separately. Among the various hot data identification methods known so far, there is a technique of recording consecutive write requests by using a Bloom filter and judging the values by hot data. However, the Bloom filter technique has a problem that a new bit array must be generated every time a set of items is changed. In addition, since it is judged based on a continuous write request, it is possible to make a wrong judgment. In this paper, we propose a method using a counting filter for accurate hot data verification. The proposed method examines consecutive write requests. It also records the number of times consecutive write requests occur. The proposed method enables more accurate hot data verification.

참고문헌 (11)

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  3. Jun Liu, "A Novel Hot Data Identification Mechanism for NAND Flash Memory", IEEE Journals & Magazines, 61(4), pp.463~469 (2015)
  4. Jen-Wei Hsieh, "Efficient identification of hot data for flash memory storage systems", ACM Transactions on Storage (TOS), 2(1) (2006)
  5. Hyun-Seob Lee, "HFTL : Hybrid Flash Translation Layer based on Hot Data Identification for Flach Memory", IEEE Journals &Magazines (2009)
  6. Dong-chul Park, "Hot Data Identification for Flash-based Storage Systems Using Multiple Bloom Filters", 2011 IEEE 27th Symposium on Mass Storage Systems and Technologies (MSST)
  7. L. -P. Chang, "On efficient wear-leveling for largescale flash-memory storage systems", Proc. of the 2007 ACM symposium on Applied computing, pp.1126~1111 (2007)
  8. Hyesook Lim, "Complement Bloom Filter for Identifying True Positiveness of a Bloom Filter", IEEE Communications Letters, 19(11) (2015)
  9. Ori Rottenstreich, "The Bloom Paradox:When Not to Use a Bloom Filter", IEEE, 2015, pp.23(3), ~
  10. Peizhen Lin, "Enhancing Dynamic Packet Filtering Technique with d-Left Counting Bloom Filter Algorithm", 2009 Second International Conference on Intelligent Networks and Intelligent Systems
  11. "Access Pattern"[URL]

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