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  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

낸드 플래시 메모리 시스템 기반의 지속성을 고려한 핫 데이터 식별 경량 기법

A lightweight technique for hot data identification considering the continuity of a Nand flash memory system

한국사물인터넷학회논문지 / Journal of The Korea Internet of Things Society, (P)2466-0078;
2022, v.8 no.5, pp.77-83
https://doi.org/https://doi.org/10.20465/kiots.2022.8.5.077
이승우 (영남이공대학교)
  • 다운로드 수
  • 조회수

초록

낸드 플래시 메모리는 구조적으로 쓰기 전 지우기(Erase-Before-Write) 동작이 요구된다. 이것을 해결하기위해서는 데이터 업데이트 동작이 빈번히 발생하는 페이지(Hot data page)를 구분하여 별도에 블록에 저장함으로 해결할 수 있으며 이러한 Hot data를 분류하는 기법을 핫 데이터 판단기법이라 한다. MHF(Multi Hash Function Framework)기법은 데이터 갱신요청의 빈도를 시스템 메모리에 기록하고 그 기록된 값이 일정 기준 이상일 때 해당데이터 갱신요청을 Hot data로 판단한다. 하지만 데이터 갱신요청에 빈도만을 단순히 카운트하는 방법으로는 정확한Hot data로 판단에 한계가 있다. 또한 데이터 갱신요청의 지속성을 판단 기준으로 하는 기법의 경우 갱신요청 사실을시간 간격을 기준으로 순차적으로 기록한 뒤 Hot data로 판단하는 방법이다. 이러한 지속성을 기준으로 하는 방법의경우 그 구현과 운용이 복잡한 단점이 있으며 갱신요청에 빈도를 고려하지 않는 경우 부정확하게 판단되는 문제가 있다. 본 논문은 데이터 갱신요청에 빈도와 지속성을 함께 고려한 경량화된 핫 데이터 판단기법을 제안한다.

keywords
Nand Flash Memory, FTL, Hot Data Identification, Garbage Collection, Mapping Algorithm

Abstract

Nand flash memory requires an Erase-Before-Write operation structurally. In order to solve this problem, it can be solved by classifying a page (hot data page) where data update operation occurs frequently and storing it in a separate block. The MHF (Multi Hash Function Framework) technique records the frequency of data update requests in the system memory, and when the recorded value exceeds a certain standard, the data update request is judged as hot data. However, the method of simply counting only the frequency of the data update request has a limit in judging it as accurate hot data. In addition, in the case of a technique that determines the persistence of a data update request, the fact of the update request is recorded sequentially based on a time interval and then judged as hot data. In the case of such a persistence-based method, its implementation and operation are complicated, and there is a problem of inaccurate judgment if frequency is not considered in the update request. This paper proposes a lightweight hot data determination technique that considers both frequency and persistence in data update requests.

keywords
Nand Flash Memory, FTL, Hot Data Identification, Garbage Collection, Mapping Algorithm

참고문헌

1.

Y.Takai, M.Fukuchi, R.Kinoshita, C.Matsui, and K.Takeuchi, “Analysis on Heterogeneous SSD Configuration with Quadruple-Level (QLC) NAND Flash Memory,” 2019IEEE 11th International Memory Workshop (IMW), pp.1-4, 2019.

2.

D.Ma, J.Feng, and G.Li, “A Survey of Address Translation Technologies for Flash Memories,” ACM Computing Surveys (CSUR), Vol.46, No.36, pp.1-39, 2014.

3.

S.Jiang, L.Zhang, X.Yuan, H.Hu, and Y.Chen, “S-ftl:An efficient address translation for flash memory by exploiting spatial locality,” IEEE/NASA Goddard Conference on Mass Storage Systems and Technologies (MSST), pp.1-12, 2011.

4.

J.Kim, J.M.Kim, S.H.Noh, S.L.Min, and Y.K.Cho, “A space-efficient flash translation layer for compact flash systems,” IEEE Transactions on Consumer Electronics, Vol.48, Issue.2, pp.366–375, 2002.

5.

S.W.Lee, D.J.Park, T.S.Chung, D.H.Lee, S.W.Park, and H.J.Song, “A log buffer-based flash translation layer using fully-associative sector translation,” ACM Transactions on Embedded Computing Systems, Vol.6, No.3, pp.18, 2007.

6.

D.W.Jung, J.U.Kang, H.S.Jo, J.S.Kim, and J.W.Lee, “Superblock FTL: A superblock-based flash translation layer with a hybrid address translation scheme,” ACM Transactions on Embedded Computing Systems, Vol.9, Issue.4, 2010.

7.

J.Liu, S.Chen, T.Wu, and H.Zhang, “A Novel Hot Data Identification Mechanism for NAND Flash Memory,”IIEEE Transactions on Consumer Electronics, Vol.61, Issue.4, pp.463-469, 2015.

8.

J.W.Hsieh, T.W.Kuo, and L.P.Chang, “Efficient identification of hot data for flash memory storage systems,” ACM Transactions on Storage (TOS), Vol.2, Issue.1, pp.22-40, 2006.

9.

H.S.Lee, H.S.Yun, and D.H.Lee, “HFTL:Hybrid Flash Translation Layer based on Hot Data Identification for Flach Memory,” IEEE Transactions on Consumer Electronics, Vol.55, Issue.4, pp.2005-2011, 2009.

10.

S.O.Park, and S.J.Kim, “An efficient file system for large-capacity storage with multiple NAND flash memories,” 2011 IEEE International Conference on Consumer Electronics (ICCE), pp.399-400, 2011.

11.

Y.J.Lee, H.W.Kim, H.J.Kim, T.Y.Huh, S.H.Jung, and Y.H.Song, “Adaptive Mapping Information Management Scheme for High Performance Large Sale Flash Memory Storages,” Journal of the Institute of Electronics and Information Engineers, Vol.50, Issue.3, pp.78-87, 2013.

12.

D.C.Park, and David H.C.D, “Hot data identification for flash-based storage systems using multiple bloom filters,” 2011 IEEE 27th Symposium on Mass Storage Systems and Technologies(MSST), pp.1-12, 2011.

13.

L.P.Chang, “On efficient wear leveling for large-scale flash-memory storage systems,” SAC '07: Proceedings of the 2007 ACM symposium on Applied computing, pp.1126-1130, 2007.

14.

S.H.Jung, Y.S.Lee, and Y.H.Song, “A process-aware hot/cold identification scheme for flash memory storage systems,” IEEE Transactions on Consumer Electronics, Vol.56, Issue.2, pp.339-347, 2010.

15.

K.W.Kim, S.H.Jung, and Y.H.Song, “Compression ratio based hot/cold data identification for flash memory,”2011 IEEE International Conference on Consumer Electronics (ICCE), pp.33-34, 2011.

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