Title: Exploiting Internal Parallelism for Address Translation in Solid-State Drives

Abstract

Solid-state Drives (SSDs) have changed the landscape of storage systems and present a promising storage solution for data-intensive applications due to their low latency, high bandwidth, and low power consumption compared to traditional hard disk drives. SSDs achieve these desirable characteristics using internal parallelism—parallel access to multiple internal flash memory chips—and a Flash Translation Layer (FTL) that determines where data are stored on those chips so that they do not wear out prematurely. However, current state-of-the-art cache-based FTLs like the Demand-based Flash Translation Layer (DFTL) do not allow IO schedulers to take full advantage of internal parallelism, because they impose a tight coupling between the logical-to-physical address translation and the data access. In this study to address this limitation, we introduce a new FTL design called Parallel-DFTL that works with the DFTL to decouple address translation operations from data accesses. Parallel-DFTL separates address translation and data access operations into different queues, allowing the SSD to use concurrent flash accesses for both types of operations. We also present a Parallel-LRU cache replacement algorithm to improve the concurrency of address translation operations. To compare Parallel-DFTL against existing FTL approaches, we present a Parallel-DFTL performance model and compare its predictions against those formore » DFTL and an ideal page-mapping approach. We also implemented the Parallel-DFTL approach in an SSD simulator using real device parameters, and used trace-driven simulation to evaluate Parallel-DFTL’s efficacy. Our evaluation results show that Parallel-DFTL improved the overall performance by up to 32% for the real IO workloads we tested, and by up to two orders of magnitude with synthetic test workloads. Finally, we also found that Parallel-DFTL is able to achieve reasonable performance with a very small cache size and that it provides the best benefit for those workloads with large request size or with high write ratio.« less

Authors:

Xie, Wei ^[1]; Chen, Yong ^[1]; Roth, Philip C.  ^[2]

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+ Show Author Affiliations

1. Texas Tech Univ., Lubbock, TX (United States)
2. ; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Publication Date:

Sat Dec 15 00:00:00 EST 2018

Research Org.:

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

OSTI Identifier:

1490593

Grant/Contract Number:  

AC05-00OR22725

Resource Type:

Accepted Manuscript

Journal Name:

ACM Transactions on Storage

Additional Journal Information:

Journal Volume: 14; Journal Issue: 4; Journal ID: ISSN 1553-3077

Publisher:

Association for Computing Machinery (ACM)

Country of Publication:

United States

Language:

English

Subject:

97 MATHEMATICS AND COMPUTING; Flash translation layer; SSD; parallelism; DFTL; address translation