Stable Greedy: Adaptive Garbage Collection for Durable Page-Mapping Multichannel SSDs

Abstract

Commodity solid state drives (SSDs) have recently begun involving the adoption of powerful controllers for multichannel flash management at the page level. However, many of these models still use primitive garbage collection algorithms, because previous approaches are subject to poor scalability with high-capacity flash memory. This study presents Stable Greedy for garbage collection in page-mapping multichannel SSDs. Stable Greedy identifies page-accurate data hotness using block-level information, and jointly considers block space utilization and block stability for victim selection. Its design considers flash wear leveling for SSD lifetime enhancement at the block level as well as at the channel level. Stable Greedy runs at a constant time, and requires limited RAM space. The simulation results revealed that Stable Greedy outperformed previous methods considerably under various workloads and multichannel architectures. Stable Greedy was successfully implemented on the OpenSSD platform, and the actual performance measurements were consistent with the simulation results.