Improving Flash Write Performance by Using Update Frequency

Solid-state drives (SSDs) are quickly becoming the de-fault storage medium as the cost of NAND flash memory continues to drop. However, flash memory introduces new challenges, as data cannot be efficiently updated in-place. To overcome the technology’s limitations, SSDs incorpo-rate a software Flash Translation Layer (FTL) that imple-ments out-of-place updates, typically by storing data in a log-structured fashion. Despite a large number of existing FTL algorithms, SSD performance, predictability, and life-time remain an issue, especially for the write-intensive work-loads specific to database applications. In this paper, we show how to design FTLs that are more efficient by using the I/O write skew to guide data place-ment on flash memory. We model the relationship between data placement and write performance for basic I/O write patterns and detail the most important concepts of writing to flash memory: i) the trade-off between the extra capac-ity available and write overhead, ii) the benefit of adapt-ing data placement to write skew, iii) the impact of the cleaning policy, and iv) how to estimate the best achievable write performance for a given I/O workload. Based on the findings of the theoretical model, we propose a new prin-cipled data placement algorithm that can be incorporated into existing FTLs. We show the benefits of our data place-ment algorithm when running micro-benchmarks and real database I/O traces: our data placement algorithm reduces write overhead by 20 %- 75 % when compared to state-of-art techniques.