An Adaptive Data Separation Aware FTL for Improving the Garbage Collection Efficiency of Solid State Drives Wei Xie and Yong Chen Texas Tech University CCGrid 2014 Poster Program Lightning Talk 27 May 2014

Solid State Drive and its Internals Flash based SSDs Now replacing HDDs in many applications for mainly for its better performance. Price falling down quickly. SSD Internals Cache Management. Flash Translation Layer: Mapping policy. Garbage collection. Wear leveling. Qingsong, W., Bozhao, G., Pathak, S., Veeravalli, B., Lingfang, Z., & Okada, K. (2011). WAFTL: A workload adaptive flash translation layer with data partition. 2011 IEEE 27th Symposium on Mass Storage Systems and Technologies (MSST), 1–12. doi:10.1109/MSST.2011.5937217

The motivation of Demand based page-mapping FTL Mapping scheme needed. But at what granularity? Page-mapping is good but expensive. 128GB SSD need 256MB on device RAM space for page mapping, assuming a page entry need 4 Bytes to store mapping table info. Cost too much. And large granularity mapping at block could degrade performance. Block mapping(in unit of 128KB): only 4MB RAM space needed. But need to sacrifice random write performance. Solution: Caching the page mapping table: DFTL(Demand based FTL) Map Table Entry Page Mapping Block Mapping Page Mapping RAM Data Block Data Block Data Block Map Table Map Table Entry Entry Translation Block Entry RAM RAM Flash

Garbage Collection Need to reclaim used pages when the memory is full(or reaching a threshold). Introduce extra overhead: Valid data need to be copied out. Overhead is large when hot/cold data are mixed. Mix of hot and cold 8 pages copied out 0 pages copied out Cold Hot 2 block(16 pages) erased 1 block(8 pages) erased invalid invalid valid invalid Get 16 free pages(8 net free pages) Get 8 free pages(8 net free pages) valid valid valid invalid Garbage Collection Efficiency: (16-8)/16=50% Garbage Collection Efficiency: (8-0)/8=100% 2 blocks, 16 pages 2 blocks, 16 pages

Hot/cold separation The number of pages need to be copied during GC is reduced by hot/cold data separation. How to detect hot/cold? Based on the recency. How to store hot/cold information? Store in Flash and selective cached in RAM like the map table in DFTL since hotness info could be big too. How to update the hot/cold information? Update immediately when in cache, asynchronously when in Flash. How to determine the separation criteria? Hot/cold information randomly sampled and criteria determined based on the sampled hotness info.

Evaluation Implemented in FlashSim. Simulated on Financial1, Microsoft Cambridge, TPCC1 and synthetic workloads. Up to 33% GC overhead reduced. Up to 15% average response time reduced.

Conclusions We proposed a method for separation hot/cold data: using sampling and selective caching the hotness information. The method is light-weight: it use small on-device RAM space and minimal computation. The method is effective: it reduces the GC overhead and thus improves the performance of the SSDs.

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