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Black-Box Performance Modeling for Solid-State Drives

机译:固态驱动器的黑匣子性能建模

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摘要

Flash-based Solid-State Drives (SSDs) have become a promising alternative to magnetic Hard Disk Drives (HDDs) thanks to the large improvements in performance, power consumption, and shock resistance. An accurate SSD performance model will provide the important research tools for exploring the design space of flash-based storage systems. While many HDD performance models have been developed, architectural differences prevent these models from being effective for SSDs, mostly because their designs cannot accurately account for many unique SSD characteristics (e.g., low latencies, slow updates, and expensive erases). In this paper, we utilize the black-box modeling technique to analyze and evaluate SSD performance, including latency, bandwidth, and throughput. Such an approach is appealing because it requires minimal a priori information about SSDs. We construct and evaluate our models on three commercial SSDs. Although this approach may lead to less accurate predictions for HDDs, we find that a black-box model with a comprehensive set of workload characteristics can achieve the mean relative errors of 20%, 13%, and 6% for latency, bandwidth, and throughput predictions, respectively.
机译:基于闪存的固态驱动器(SSD)在性能,功耗和抗震性方面有了很大的改进,已成为磁硬盘驱动器(HDD)的有前途的替代品。准确的SSD性能模型将为探索基于闪存的存储系统的设计空间提供重要的研究工具。虽然已经开发了许多HDD性能模型,但是架构差异使这些模型无法有效用于SSD,主要是因为它们的设计无法准确说明许多独特的SSD特性(例如,低延迟,更新缓慢和昂贵的擦除操作)。在本文中,我们利用黑盒建模技术来分析和评估SSD性能,包括延迟,带宽和吞吐量。这种方法之所以吸引人,是因为它需要有关SSD的最少先验信息。我们在三个商用SSD上构建和评估我们的模型。尽管这种方法可能会导致对HDD的预测不准确,但是我们发现具有全面工作负载特征集的黑盒模型可以在延迟,带宽和吞吐量方面实现20%,13%和6%的平均相对误差。预测。

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