Browsing by Author "Lin, Wen-Huei"

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Dual Greedy: Adaptive Garbage Collection for Page-Mapping Solid-State Disks
(2012-01-01)
Lin, Wen-Huei
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Chang, Li-Pin
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資訊工程學系
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Department of Computer Science
In the recent years, commodity solid-state disks have started adopting powerful controllers and implemented page-level mapping for flash management. However, many of these models still use primitive garbage-collection algorithms, because prior approaches do not scale up with the dramatic increase of flash capacity. This study introduces Dual Greedy for garbage collection in page-level mapping. Dual Greedy identifies page-accurate data hotness using only block-level information, and adaptively switches its preference of victim selection between block space utilization and block stability. It can run in constant time and use very limited RAM space. Our experimental results show that Dual Greedy outperforms existing approaches in terms of garbage-collection overhead, especially with large flash blocks.
Stable Greedy: Adaptive Garbage Collection for Durable Page-Mapping Multichannel SSDs
(2016)
Chang, Li-Pin
;
Liu, Yu-Syun
;
Lin, Wen-Huei
;
資訊工程學系
;
Department of Computer Science
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.