An Adaptive SSD Cache Architecture Simultaneously Using Multiple Caches

Nikolaus Jeremic, Helge Parzyjegla, Gero Mühl
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Abstract

Due to a notably higher cost per bit of storage capacity, NAND flash memory solid state drives (SSDs) are not expected to completely replace hard disk drives (HDDs) in the near future. Using SSDs as a cache for HDDs, however, proved very effective in increasing the performance of storage systems. The performance increase depends strongly on both the SSD cache design and the workload applied to the storage system. However, distinct parts of the data may exhibit significantly different access patterns that potentially change rapidly and unpredictably over time. This particularly applies to complex dynamic systems, such as virtualized environments. Existing SSD cache architectures are not able to exploit such differences in access patterns, as they only use a single cache design, even when adapting certain cache parameters.In this paper, we propose a novel generic architecture for adaptive block-level SSD caches that simultaneously employs multiple SSD caches with complementary designs. The goal is to use for each kind of access pattern the SSD cache design that fits the pattern best. Results of our experimental evaluation show that the proposed SSD cache architecture adapts to different workloads well. For a broad range of workloads, it provides an overall throughput that is comparable to the respective best single cache design, whereas it is able to outperform these cache designs for superimposed mixed workloads and workloads with changing characteristics.
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同时使用多个缓存的自适应SSD缓存架构
由于每比特存储容量的成本明显较高,NAND闪存固态驱动器(ssd)预计在不久的将来不会完全取代硬盘驱动器(hdd)。然而,使用ssd作为hdd的缓存被证明在提高存储系统的性能方面非常有效。性能提升很大程度上取决于SSD缓存设计和应用于存储系统的工作负载。但是,数据的不同部分可能表现出明显不同的访问模式,这些模式可能随着时间的推移而迅速和不可预测地变化。这尤其适用于复杂的动态系统,比如虚拟化环境。现有的SSD缓存架构无法利用访问模式中的这种差异,因为它们只使用单一的缓存设计,即使在调整某些缓存参数时也是如此。在本文中,我们为自适应块级SSD缓存提出了一种新的通用架构,该架构同时使用具有互补设计的多个SSD缓存。我们的目标是为每种访问模式使用最适合该模式的SSD缓存设计。实验评估结果表明,所提出的SSD缓存架构能够很好地适应不同的工作负载。对于广泛的工作负载,它提供的总体吞吐量可与各自最佳的单一缓存设计相媲美,而对于叠加混合工作负载和具有变化特征的工作负载,它能够优于这些缓存设计。
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NAS 2019 Program Optimizing Tail Latency of LDPC based Flash Memory Storage Systems Via Smart Refresh HCMonitor: An Accurate Measurement System for High Concurrent Network Services Learning Workflow Scheduling on Multi-Resource Clusters An Adaptive SSD Cache Architecture Simultaneously Using Multiple Caches
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