ASA-FTL: An adaptive separation aware flash translation layer for solid state drives

Abstract

• An innovative FTL approach (ASA-FTL) for identifying and separating hot/cold data at the page level based on data clustering for SSDs; • A sampling and selective caching technique to control the overhead of clustering-based hot/cold data separation approach; • A Hotness Updating Algorithm that samples and clusters data periodically to update the separation criteria and an overhead analysis of the approach; • An evaluation of the proposed approach using the FlashSim simulator with several real world workloads, showing the performance benefits of our approach compared to a current state of the art FTL; • An evaluation of the effect of the sample size and the adaptivity of the proposed ASA-FTL. The flash-memory based Solid State Drive (SSD) presents a promising storage solution for increasingly critical data-intensive applications due to its low latency (high throughput), high bandwidth, and low power consumption . Within an SSD, its Flash Translation Layer (FTL) is responsible for exposing the SSD’s flash memory storage to the computer system as a simple block device. The FTL design is one of the dominant factors determining an SSD’s lifespan and performance. To reduce the garbage collection overhead and deliver better performance, we propose a new, low-cost, adaptive separation-aware flash translation layer (ASA-FTL) that combines sampling, data clustering and selective caching of recency information to accurately identify and separate hot/cold data while incurring minimal overhead. We use sampling for light-weight identification of separation criteria, and our dedicated selective caching mechanism is designed to save the limited RAM resource in contemporary SSDs. Using simulations of ASA-FTL with both real-world and synthetic workloads, we have shown that our proposed approach reduces the garbage collection overhead by up to 28% and the overall response time by 15% compared to one of the most advanced existing FTLs. We find that the data clustering using a small sample size provides significant performance benefit while only incurring a very small computation and memory cost. In addition, our evaluation shows that ASA-FTL is able to adapt to the changes in the access pattern of workloads, which is a major advantage comparing to existing fixed data separation methods.