Parallelism and Garbage Collection Aware I/O Scheduler with Improved SSD Performance

作者：Jiayang Guo, Yiming Hu, Bo Mao, Suzhen Wu · 年份：2017 · DOI：10.1109/ipdps.2017.55 · 被引用次数：35 · 研究领域：Advanced Data Storage Technologies、Caching and Content Delivery、Cellular Automata and Applications

In this paper, we propose PGIS, a parallelism and garbage collection aware I/O Scheduler, which identifies the hot data based on trace characteristics to exploit the channel level internal parallelism of flash-based storage systems. PGIS not only fully exploits abundant channel resource in the SSD, but also it introduces a hot data identification mechanism to reduce the garbage collection overhead. By dispatching hot read data to different channel, the channel level internal parallelism is fully exploited. By dispatching hot write data to the same physical block, the garbage collection overhead has been alleviated. The experiment results show that compared with existing I/O schedulers, PGIS improves the response time and garbage collection performance significantly. Consequently, PGIS reduces the garbage collection overhead up to 30.9%, while exploiting channel level internal parallelism.