基于模式的预取与自适应缓存管理内部的固态驱动器

Jun Li, Xiaofei Xu, Zhigang Cai, Jianwei Liao, Kenli Li, Balazs Gerofi, Y. Ishikawa
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引用次数: 5

摘要

本文提出了一种在固态硬盘(ssd)的闪存转换层支持自适应缓存管理的基于模式的预取方案。它在ssd内部工作,具有操作系统依赖和使用透明性的特性。具体来说,它首先挖掘反映发生I/O请求之间相关性的频繁块访问模式。然后,它将当前时间窗口中的请求与确定的模式进行比较,以将预取数据直接放入ssd的缓存中。更重要的是,为了最大限度地提高缓存的使用效率,我们建立了一个数学模型,根据I/O工作负载特征自适应确定缓存分区,分别缓冲预取数据和写入数据。实验结果表明,与传统的ssd内部预取方案相比,我们的方案可以在不显著增加写延迟的情况下将平均读延迟提高1.8%-36.5%。
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Pattern-Based Prefetching with Adaptive Cache Management Inside of Solid-State Drives
This article proposes a pattern-based prefetching scheme with the support of adaptive cache management, at the flash translation layer of solid-state drives (SSDs). It works inside of SSDs and has features of OS dependence and uses transparency. Specifically, it first mines frequent block access patterns that reflect the correlation among the occurred I/O requests. Then, it compares the requests in the current time window with the identified patterns to direct prefetching data into the cache of SSDs. More importantly, to maximize the cache use efficiency, we build a mathematical model to adaptively determine the cache partition on the basis of I/O workload characteristics, for separately buffering the prefetched data and the written data. Experimental results show that our proposal can yield improvements on average read latency by 1.8%–36.5% without noticeably increasing the write latency, in contrast to conventional SSD-inside prefetching schemes.
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