Quantifying Temporal and Spatial Localities in Storage Workloads and Transformations by Data Path Components
Quantifying Temporal and Spatial Localities in Storage Workloads and Transformations by Data Path Components
复制标题
通过数据路径组件量化存储工作负载和转换中的时间和空间位置
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Andy Wang
中科院分区：
文献类型：
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作者：
Cory Fox;Dragan Lojpur;Andy Wang
时间和空间位置是操作系统中的基本概念，存储系统依赖于位置来运行良好。令人惊讶的是，很难量化工作负载中存在的位置以及位置如何通过可在不同设置下进行比较的度量中的存储数据路径组件进行转换。在本文中，我们引入了堆栈和块亲和度量来量化时间和空间位置。我们证明了我们的指标(1)在极端和正常负载下表现良好，(2)可用于验证存储优化每个阶段的合成负载，(3)可以以对几代硬件具有弹性的方式捕获位置，以及(4)与性能有意义的关联。我们的经验还揭示了位置的隐藏语义，并确定了未来的研究方向。
Temporal and spatial localities are basic concepts in operating systems, and storage systems rely on localities to perform well. Surprisingly, it is difficult to quantify the localities present in workloads and how localities are transformed by storage data path components in metrics that can be compared under diverse settings. In this paper, we introduce stack- and block-affinity metrics to quantify temporal and spatial localities. We demonstrate that our metrics (1) behave well under extreme and normal loads, (2) can be used to validate synthetic loads at each stage of storage optimization, (3) can capture localities in ways that are resilient to generations of hardware, and (4) correlate meaningfully with performance. Our experience also unveiled hidden semantics of localities and identified future research directions.