SlimCache

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 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.