A workload-aware flash translation layer enhancing performance and lifespan of TLC/SLC dual-mode flash memory in embedded systems

Duo Liu, Lei Yao, Linbo Long, Zili Shao, Yong Guan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

13 Citations (Scopus)

Abstract

Similar to traditional NAND flash memory, triple-level cell (TLC) flash memory is used as secondary storage to meet the fast growing demands on storage capacity. TLC flash memory exhibits attractive features such as shock resistance, high density, low cost, non-volatility and low access latency natures. However, TLC flash memory also has some extra limitations, such as write disturbance, low performances and very limited cycles compared to single-level cell (SLC) flash memory. In this paper, we propose a workload-aware flash translation layer, named Balloon-FTL, for the TLC/SLC dual-mode flash memory, to improve performance and lifespan of the system. We first build a workload identifier module with genetic algorithm to dynamically allocate TLC/SLC capacity based on different workloads, and produce the suitable data allocation to achieve a balanced write distribution in flash memory with low memory access cost. The basic idea is to classify metadata/userdata according to their access pattern, and allocate low-latency SLC and high-density TLC mode blocks for write-intensive metadata and a large quantities userdata, respectively. We then propose a special hybrid mapping strategy for the TLC/SLC dual-mode flash memory to improve the performance. Experimental results show that Balloon-FTL can effectively improve the performance and lifespan of the TLC/SLC dual-mode flash memory in embedded systems.
Original languageEnglish
Pages (from-to)343-354
Number of pages12
JournalMicroprocessors and Microsystems
Volume52
DOIs
Publication statusPublished - 1 Jul 2017

Keywords

  • Flash translation layer
  • Genetic algorithm
  • TLC flash memory

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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