SSD: Cache or Tier an Evaluation of SSD Cost and Efficiency using MapReduce
Fatimah Alsayoud, Ali Miri · 2019
Solid-State Drives (SSDs) play a crucial role in today's storage systems. They are appended into the Hard-Disk Drives (HDDs) storage systems to improve performance. They provide high IO rate and low latency, which makes them a perfect candidate for analytic-based workloads such as MapReduce. Defining an efficient SSD deployment strategies for MapReduce workloads is a challenging task: SSDs are costly and have limited capacity, the workloads have a big process data size, and the platform has a unique workflow nature. The goal of the work is to establish performance and cost relationship between SSD approaches and MapReduce workloads. In our setup, MapReduce workloads were executed with two SSD approaches of tiering and caching each with two setups: compress and uncompress. Our results showed that by using SSD as a tier, MapReduce workload performs better by up to 66% and increased SSD lifespan by around 20% when comparing with cache approach. We also observed that applying compression on the tier approach enhanced the lifespan by 60% but reduced lifespan of cache tier by 50%.