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Youngjae Kim, Aayush Gupta, Bhuvan Urgaonkar. A Temporal Locality-Aware Page-Mapped Flash Translation Layer[J]. Journal of Computer Science and Technology, 2013, 28(6): 1025-1044. DOI: 10.1007/s11390-013-1395-4
Citation: Youngjae Kim, Aayush Gupta, Bhuvan Urgaonkar. A Temporal Locality-Aware Page-Mapped Flash Translation Layer[J]. Journal of Computer Science and Technology, 2013, 28(6): 1025-1044. DOI: 10.1007/s11390-013-1395-4

A Temporal Locality-Aware Page-Mapped Flash Translation Layer

  • The poor performance of random writes has been a cause of major concern which needs to be addressed to better utilize the potential of flash in enterprise-scale environments. We examine one of the important causes of this poor performance: the design of the flash translation layer (FTL) which performs the virtual-to-physical address translations and hides the erase-before-write characteristics of flash. We propose a complete paradigm shift in the design of the core FTL engine from the existing techniques with our Demand-Based Flash Translation Layer (DFTL) which selectively caches pagelevel address mappings. Our experimental evaluation using FlashSim with realistic enterprise-scale workloads endorses the utility of DFTL in enterprise-scale storage systems by demonstrating: 1) improved performance, 2) reduced garbage collection overhead and 3) better overload behavior compared with hybrid FTL schemes which are the most popular implementation methods. For example, a predominantly random-write dominant I/O trace from an OLTP application running at a large financial institution shows a 78% improvement in average response time (due to a 3-fold reduction in operations of the garbage collector), compared with the hybrid FTL scheme. Even for the well-known read-dominant TPC-H benchmark, for which DFTL introduces additional overheads, we improve system response time by 56%. Moreover, interestingly, when write-back cache on DFTL-based SSD is enabled, DFTL even outperforms the page-based FTL scheme, improving their response time by 72% in Financial trace.
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