When F2FS meets address remapping

Lee, Yongmyung; Park, Jong-Hyeok; Park, Jonggyu; Gwak, Hyunho; Shin, Dongkun; Eom, Young Ik; Lee, Sang‐Won

doi:10.1145/3538643.3539755

Cited by 5 publications

(10 citation statements)

References 9 publications

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“…研究者们提出了许多 PM-aware 的文件系统, 如 PMFS [41] , NOVA [27] , SplitFS [38] 和 WineFS [42] , 并利用 PM 的可字节寻址能力消除面向块的存储成本. 这些系统还支持创建内存映射文件, 并通过 CPU 的 load/store 等指令 [15] 8) . 该平台配备有…”

Section: 存储软件栈unclassified

Structured storage for ubiquitous operating systems

FAN,

YAN,

WENG

2024

Sci. Sin.-Inf.

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范晓鹏等: 面向泛在操作系统的结构化存储在泛在操作系统环境中, 面临着 "端 (终端设备) -边 (边缘端) -云 (云端)" 3 个层次的存储需求. 在 3 个层级上均存在大量的结构化数据的存储需求. 端侧设备呈现资源极端化和异构化等特性, 需要存储系统具备模块化和强适配性, 在边/云侧可以部署新型存储设备来加速 I/O 处理, 满足泛在应用的低时延和实时性需求. 通行的解决方案是在字节流的文件系统基础之上实现结构化存储. 然而, 这种传统解决方案存在诸多挑战, 难以满足泛在场景需求. 面向端侧的结构化数据存储.(1) I/O 放大严重. 终端设备上的结构化数据管理依赖于堆叠式的软件栈, 导致了严重的输入/输出 (input/output, I/O) 放大, 这不仅降低了带宽利用率, 还缩短了设备的寿命. 堆叠式软件栈使得 I/O 路径复杂, 造成大量冗余开销, 无法满足泛在应用的 I/O 时效性需求.

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Section: 存储软件栈unclassified

Structured storage for ubiquitous operating systems

FAN,

YAN,

WENG

2024

Sci. Sin.-Inf.

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“…ICID FS uses a journal to protect the metadata in the presence of system crashes. The journaling mechanism has been widely used in a variety of file systems [33], [34]. Before updating the metadata, ICID FS stores the new metadata in a log region.…”

Section: File Organizationmentioning

confidence: 99%

I/O Causality Based In-Line Data Deduplication for Non-Volatile Memory Enabled Storage Systems

Liu,

Jin,

et al. 2024

IEEE Trans. Comput.

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Data deduplication technologies are widely exploited to reduce capacity demands for storage. Previous chunk-based offline deduplication technologies often cause serious performance overhead due to data chunking and indexing. Particularly, they are not efficient for non-volatile memory (NVM) based storage systems because they cannot fully exploit the byte-addressability feature of NVMs for fine-grained deduplication. In this paper, we propose I/O Causality based In-line Deduplication (ICID) to maximize the deduplication ratio for NVM-based storage systems. Unlike previous inline deduplication schemes that use hash indexes to identify duplicate data slices, ICID records memory-copy operations in a B-tree structure to achieve causality-based inline deduplication. We propose two novel techniques to manage memory-copy records in the B-tree efficiently. First, to speed up the B-tree lookup, we group memory-copy records targeted to the same page in a B-tree node to improve data locality. Second, we exploit the spatial locality of memory accesses to identify outdated memory-copy records, and delete them in time to reduce memory consumption of the B-tree. We evaluate ICID in a system equipped with Intel Optane DC Persistent Memory Modules. For a typical KV store-LevelDB, our experimental results show that ICID achieves up to 16× higher deduplication ratio and reduces the time cost of data deduplication by 47% on average compared with state-of-the-art deduplication schemes.

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“…To be specific, SSDs replace the platter and spinner structures of HDDs [11,28] with a massive number of CMOS-based NAND flash memory [11,28], which can deliver a high level of internal I/O parallelism. Moreover, with sophisticated buffer designs and log-structured techniques [18,29,34,59,60], the SSD write performance has been significantly improved.…”

Section: Introductionmentioning

confidence: 99%

LearnedFTL: A Learning-based Page-level FTL for Improving Random Reads in Flash-based SSDs

Wang¹,

Lin²,

Wu³

et al. 2023

Preprint

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We present LearnedFTL, which applies learned indexes to on-demand page-level flash translation layer (FTL) designs to improve the random read performance of flash-based solidstate drives (SSDs). The first of its kind, it minimizes the number of double reads induced by address translation in random read accesses. To apply the learned indexes to address translation, LearnedFTL proposes dynamic piece-wise regression to efficiently build learned indexes. LearnedFTL also exploits the unique feature of page relocation in SSD internal garbage collection (GC), and embeds the learned index training in GC, which can minimize additional delay on normal read and write operations. Additionally, LearnedFTL employs a bitmap prediction filter to guarantee the accuracy of learned indexes' predictions. With these designs, LearnedFTL considerably speeds up address translation while reducing the number of flash read accesses caused by the demand-based page-level FTL. Our benchmark-driven experiments on a FEMU-based prototype show that LearnedFTL reduces the 99th percentile tail latency by 4.8×, on average, compared to the state-of-the-art TPFTL scheme.

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When F2FS meets address remapping

Cited by 5 publications

References 9 publications

Structured storage for ubiquitous operating systems

Structured storage for ubiquitous operating systems

I/O Causality Based In-Line Data Deduplication for Non-Volatile Memory Enabled Storage Systems

LearnedFTL: A Learning-based Page-level FTL for Improving Random Reads in Flash-based SSDs

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