An empirical study of hot/cold data separation policies in solid state drives (SSDs)

Abstract

Separating hot data from cold data is known to allow for efficient management of NAND flash memory in Solid State Drives (SSDs). However, most of previous work has been evaluated with the trace-driven simulations under different workloads and testing conditions. The goal of this paper is to empirically study the performance, computation overhead, and memory consumption of the existing hot/cold data separation policies on a real SSD platform. After devising a general framework where a different policy can be easily plugged in, we have evaluated three hot/cold data separation policies: 2-level LRU (LRU), Multiple Bloom Filter (MBF), and Dynamic dAta Clustering (DAC). Our evaluation results show that DAC performs best, improving the performance by up to 58% in real workloads with a reasonable computation and memory overhead. Copyright 2013 ACM.