Analytic Models of SSD Write Performance

Abstract

Solid-state drives (SSDs) update data by writing a new copy, rather than overwriting old data, causing prior copies of the same data to be invalidated . These writes are performed in units of pages , while space is reclaimed in units of multipage erase blocks , necessitating copying of any remaining valid pages in the block before reclamation. The efficiency of this cleaning process greatly affects performance under random workloads; in particular, in SSDs, the write bottleneck is typically internal media throughput, and write amplification due to additional internal copying directly reduces application throughput. We present the first nearly-exact closed-form solution for write amplification under greedy cleaning for uniformly-distributed random traffic, validate its accuracy via simulation, and show that its inaccuracies are negligible for reasonable block sizes and overprovisioning ratios. In addition, we also present the first models which predict performance degradation for both LRW (least-recently-written) cleaning and greedy cleaning under simple nonuniform traffic conditions; simulation results show the first model to be exact and the second to be accurate within 2%. We extend the LRW model to arbitrary combinations of random traffic and demonstrate its use in predicting cleaning performance for real-world workloads. Using these analytic models, we examine the strategy of separating “hot” and “cold” data, showing that for our traffic model, such separation eliminates any loss in performance due to nonuniform traffic. We then show how a system which segregates hot and cold data into different block pools may shift free space between these pools in order to achieve improved performance, and how numeric methods may be used with our model to find the optimum operating point, which approaches a write amplification of 1.0 for increasingly skewed traffic. We examine online methods for achieving this optimal operating point and show a control strategy based on our model which achieves high performance for a number of real-world block traces.