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P2 AnyKey: A Key-Value SSD for All Workload Types

AnyKey:面向所有工作负载类型的键值 SSD
ASPLOS Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 12025匹配度 0/10 · 该论文研究的是键值SSD存储系统设计,核心在于元数据管理和存储性能优化,与用户关注的编程语言、静态分析、LLM/Agent、浮点程序分析、贝叶斯程序分析等方向完全无关。引用 6 inbox
Chanyoung Park, Jungho Lee, Chun-Yi Liu, Kyungtae Kang, Mahmut Taylan Kandemir, Wonil Choi
crossref: 2026-09-12dblp: 2026-09-12

AI 速览

一句话:提出 AnyKey 键值 SSD,通过抑制元数据膨胀,在键大小差异大的多种负载下优于现有设计。

现有键值固态硬盘(KV-SSD)主要针对“值远大于键”的负载进行优化,而实践中还存在键相对较大的另一类负载。本文重新评估当前 KV-SSD 设计在这类未被充分探索的负载下的表现,发现其性能显著下降,并指出根因是元数据规模随键尺寸增大而膨胀。为此,作者提出 AnyKey,一种新型 KV-SSD 设计,使元数据大小在不同键尺寸下都不会增长。基于多种真实负载的详细评估表明,在键和值尺寸变化的不同类型工作负载下,AnyKey 均优于当前最先进的 KV-SSD 设计。

问题
现有 KV-SSD 设计只针对值远大于键的负载调优,在键尺寸较大的真实负载下元数据膨胀,导致性能显著退化。
方法
分析性能退化根因是元数据规模随键尺寸增大而增加,进而设计 AnyKey,使元数据大小在不同键尺寸下保持不增长。
评估
使用多种真实工作负载,在不同键和值尺寸下与当前最先进的 KV-SSD 设计进行对比评估,结果显示 AnyKey 全面更优。
原文摘要(Abstract)

Key-value solid-state drives (KV-SSDs) are considered as a potential storage solution for large-scale key-value (KV) store applications. Unfortunately, the existing KV-SSD designs are tuned for a specific type of workload, namely, those in which the size of the values are much larger than the size of the keys. Interestingly, there also exists another type of workload, in practice, in which the sizes of keys are relatively large. We re-evaluate the current KV-SSD designs using such unexplored workloads and document their significantly-degraded performance. Observing that the performance problem stems from the increased size of the metadata, we subsequently propose a novel KV-SSD design, called AnyKey, which prevents the size of the metadata from increasing under varying sizes of keys. Our detailed evaluation using a wide range of real-life workloads indicates that AnyKey outperforms the state-of-the-art KV-SSD design under different types of workloads with varying sizes of keys and values.

链接与引用

DOI 原文 · PDF(开放获取) · DBLP

BibTeX
@inproceedings{0004L0KKC25,
  title = {AnyKey: A Key-Value SSD for All Workload Types},
  author = {Chanyoung Park and Jungho Lee and Chun-Yi Liu and Kyungtae Kang and Mahmut Taylan Kandemir and Wonil Choi},
  booktitle = {ASPLOS},
  year = {2025},
  doi = {10.1145/3669940.3707279}
}