Learning I/O Access Patterns to Improve Prefetching in SSDs
Learning I/O Access Patterns to Improve Prefetching in SSDs
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DOI：
10.1007/978-3-030-67667-4_26
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发表时间：
2020
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影响因子：
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通讯作者：
Chandranil Chakraborttii;Heiner Litz
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文献类型：
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作者：
Chandranil Chakraborttii;Heiner Litz
基于闪存的固态硬盘 (SSD) 已成为云和移动环境中硬盘驱动器的高性能替代品。然而，由于 I/O 访问延迟较高，SSD 仍然是计算机系统的性能瓶颈。改善访问延迟的常用方法是预取。预取可以预测未来的块访问并将其提前预加载到主内存中。在本文中，我们讨论了 SSD 中预取的挑战，解释了为什么先前的方法无法实现高精度，并提出了一种基于神经网络的预取方法，其性能显着优于最先进的技术。为了实现高性能，我们解决了在非常大的稀疏地址空间中预取的挑战，以及通过提前预测及时预取的挑战。我们从云服务器上运行的多个实际应用程序收集 I/O 跟踪文件，结果表明，我们提出的方法始终优于现有的步幅预取器多达 800 倍，优于之前基于马尔可夫链的预取方法多达 8 倍。此外，我们提出了一种地址映射学习技术，以证明我们的方法对以前未见过的 SSD 工作负载的适用性，并执行超参数敏感性研究。
Flash based solid state drives (SSDs) have established themselves as a higher-performance alternative to hard disk drives in cloud and mobile environments. Nevertheless, SSDs remain a performance bottleneck of computer systems due to their high I/O access latency. A common approach for improving the access latency is prefetching. Prefetching predicts future block accesses and preloads them into main memory ahead of time. In this paper, we discuss the challenges of prefetching in SSDs, explain why prior approaches fail to achieve high accuracy, and present a neural network based prefetching approach that significantly outperforms the state-of the-art. To achieve high performance, we address the challenges of prefetching in very large sparse address spaces, as well as prefetching in a timely manner by predicting ahead of time. We collect I/O trace files from several real-world applications running on cloud servers and show that our proposed approach consistently outperforms the existing stride prefetchers by up to 800and prior prefetching approaches based on Markov chains by up to 8. Furthermore, we propose an address mapping learning technique to demonstrate the applicability of our approach to previously unseen SSD workloads and perform a hyperparameter sensitivity study.