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    基于ZNS固态硬盘的存储优化技术综述

    A Survey of Storage Optimization Techniques for ZNS SSDs

    • 摘要: ZNS固态硬盘(zoned namespaces solid-state drive,ZNS SSD)减少了传统固态硬盘的“块接口税”所带来的性能开销,为构建高性能存储系统带来了新的机遇。然而,由于ZNS固态硬盘将垃圾回收、地址映射等操作上移至主机端,以及区域划分、顺序写入等存储范式的变化,导致基于ZNS固态硬盘的存储系统也面临诸多挑战。首先,系统地归纳了基于ZNS固态硬盘的存储系统所面临的3个核心挑战:写入放大、并行性和数据一致与可靠性。然后,系统地阐述了针对各类挑战的存储优化技术、研究进展和典型研究,并介绍了面向ZNS固态硬盘的模拟仿真器和性能优化辅助工具。最后,展望了基于ZNS固态硬盘的存储系统优化技术的未来研究方向,包括跨层协同设计、多层次异构集成、软件堆栈优化、系统全局优化。

       

      Abstract: Zoned namespaces solid-state drive (ZNS SSD) mitigate the performance overhead of the conventional “block interface tax,” opening new opportunities for high-performance storage system design. At the same time, ZNS SSDs fundamentally reshape the storage model by exposing zones to the host, enforcing sequential writes within each zone, and offloading flash-management functions—such as garbage collection and address mapping—from the device to host software. These shifts introduce significant challenges for ZNS-based systems. This survey distills three key challenges: 1) write amplification induced by host-managed placement and reclamation, where co-locating mixed-lifetime data within zones increases GC copying, compaction-GC mismatch leads to redundant data migration, and inefficient zone-reset policies cause unnecessary rewrites; 2) limited parallelism under fixed zone/resource mapping and sequential-write constraints, further exacerbated by intra- and inter-zone contention; and 3) elevated consistency and reliability overheads arising from wear imbalance, crash-recovery metadata costs, and parity maintenance in multi-device settings. For each challenge, we organize state-of-the-art optimization techniques, summarize recent advances, and highlight representative studies. We also review widely used simulators/emulators and auxiliary tools that support ZNS prototyping, debugging, and performance tuning. Finally, we discuss promising research directions, including cross-layer hardware-software co-design, multi-level heterogeneous integration, systematic software-stack refinement, and holistic, system-wide optimization.

       

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