1. An Efficient Data Migration Scheme to Optimize Garbage Collection in SSDs
- Author
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Changsheng Xie, You Zhou, Shunzhuo Wang, Fei Wu, and Jiaona Zhou
- Subjects
Computer science ,business.industry ,Reliability (computer networking) ,02 engineering and technology ,Computer Graphics and Computer-Aided Design ,020202 computer hardware & architecture ,Embedded system ,0202 electrical engineering, electronic engineering, information engineering ,Overhead (computing) ,Electrical and Electronic Engineering ,Resource management (computing) ,business ,Software ,Data migration ,Wear leveling ,Garbage collection - Abstract
Garbage collection (GC) is time consuming and frequently executed all over the lifetime of solid-state drives (SSDs), which has a significant impact on system performance. Manufactures provide the copyback that directly transfers data within the same plane to accelerate data migration in GC. However, the introduction of copyback leads to two issues: 1) high detection overhead of copyback feasibility (whether data are carried out via copyback with guaranteed reliability) and 2) interplane unbalanced wear distribution. In this article, we first explore copyback error characteristics on the real NAND flash chip, then propose a fast GC scheme called FastGC . It utilizes copyback error characteristics to efficiently detect the copyback feasibility of data instead of transferring out all valid data for detecting. FastGC further utilizes a data migration leveler which aims at relieving migration overhead per GC to realize the wear leveling. Regarding data migrated via external data move (EDM), FastGC takes data coldness and erase counts of planes into consideration to even out the number of migrating data per plane and prolong the lifetime of SSDs. SSDsim, a validate simulation is used to implement FastGC and comprehensive experiments are carried out with various enterprise workloads to evaluate the system performance and the wear difference of SSDs. The experimental results in the SSDsim show the FastGC greatly promotes system performance and the wear leveling up to 46.68% and 12X, respectively, compared to the traditional copyback-based GC.
- Published
- 2021
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