Abstract
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 paper, 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 leveraged the unique workload characteristics in key-value systems to efficiently identify and separate hot and cold data. In order to dynamically adapt to workload changes during runtime, we have designed an adaptive hot/cold area partitioning method based on a cost model. Inorder 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 125.9%, effectively increasing throughput and reducing average latency by up to 255.6% and 78.9%, respectively.
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