Flash-based key-value caching is becoming popular in data centers for providing high-speed key-value services. These systems adopt slab-based space management on flash and provide a low-cost solution for key-value caching. However, optimizing cache efficiency for flash-based key-value cache systems is highly challenging, due to the huge number of key-value items and the unique technical constraints of flash devices. In this article, we present a dynamic on-line compression scheme, called SlimCache , to improve the cache hit ratio by virtually expanding the usable cache space through data compression. We have investigated the effect of compression granularity to achieve a balance between compression ratio and speed, and we leveraged the unique workload characteristics in key-value systems to efficiently identify and separate hot and cold data. To dynamically adapt to workload changes during runtime, we have designed an adaptive hot/cold area partitioning method based on a cost model. To avoid unnecessary compression, SlimCache also estimates data compressibility to determine whether the data are suitable for compression or not. We have implemented a prototype based on Twitter’s Fatcache. Our experimental results show that SlimCache can accommodate more key-value items in flash by up to 223.4%, effectively increasing throughput and reducing average latency by up to 380.1% and 80.7%, respectively.

中文翻译：

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超薄缓存

基于闪存的键值缓存在数据中心越来越流行，用于提供高速键值服务。这些系统在闪存上采用基于平板的空间管理，并为键值缓存提供低成本的解决方案。然而，基于闪存的键值缓存系统的缓存效率优化具有很大的挑战性，因为键值项的数量巨大，而且闪存设备具有独特的技术限制。在本文中，我们提出了一种动态在线压缩方案，称为超薄缓存，通过数据压缩虚拟扩展可用缓存空间来提高缓存命中率。我们研究了压缩粒度的影响，以实现压缩比和速度之间的平衡，并利用键值系统中独特的工作负载特性来有效地识别和分离冷热数据。为了在运行时动态适应工作负载的变化，我们设计了一种基于成本模型的自适应冷热区域划分方法。为了避免不必要的压缩，SlimCache 还会估计数据的可压缩性，以确定数据是否适合压缩。我们已经实现了一个基于 Twitter 的 Fatcache 的原型。我们的实验结果表明，SlimCache 可以容纳更多的闪存中的键值项高达 223.4%，