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Adaptive policies for balancing performance and lifetime of mixed SSD arrays through workload sampling

机译：通过工作负载采样来平衡混合SSD阵列的性能和寿命的自适应策略

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Solid-state drives (SSDs) have become promising storage components to serve large I/O demands in modern storage systems. Enterprise class (high-end) SSDs are faster and more resilient than client class (low-end) SSDs but they are expensive to be deployed in large scale storage systems. It is an attractive and practical alternative to exploit the high-end SSDs as a cache and low-end SSDs as main storage. This paper explores how to optimize a mixed SSD array in terms of performance and lifetime. This paper shows that simple integration of different classes of SSDs in traditional caching policies results in poor reliability. This paper also reveals that caching policies with static workload distribution are not always efficient. In this paper, we propose a sampling based adaptive approach that achieves fair workload distribution across the cache and the storage. The proposed algorithm enables fine-grained control of the workload distribution which minimizes latency over lifetime of mixed SSD arrays. We show that our adaptive algorithm is very effective in improving the latency over lifetime metric, on an average, by up to 2.36 times over LRU, across a number of workloads.
机译：固态驱动器（SSD）已成为有前途的存储组件，可以满足现代存储系统中的大量I / O需求。企业级（高端）SSD比客户端级（低端）SSD更快，更具弹性，但要在大型存储系统中部署它们价格昂贵。利用高端固态硬盘作为缓存，利用低端固态硬盘作为主存储，是一种有吸引力且实用的选择。本文探讨了如何在性能和寿命方面优化混合SSD阵列。本文表明，在传统的缓存策略中简单集成不同类别的SSD会导致可靠性下降。本文还揭示了具有静态工作负载分配的缓存策略并不总是有效的。在本文中，我们提出了一种基于采样的自适应方法，该方法可在整个缓存和存储中实现合理的工作负载分配。所提出的算法可实现对工作负载分布的细粒度控制，从而最大程度地减少了混合SSD阵列在整个生命周期内的延迟。我们表明，在多种工作负载下，我们的自适应算法在改善整个生命周期指标的延迟方面非常有效，平均而言，其延迟是LRU的2.36倍。

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