Reducing Write Amplification of Flash Storage through Cooperative Data Management with NVM

Write amplification is a critical factor that limits the stable performance of flash-based storage systems. To reduce write amplification, this paper presents a new technique that cooperatively manages data in flash storage and nonvolatile memory (NVM). Our scheme basically considers NVM as the cache of flash storage, but allows the original data in flash storage to be invalidated if there is a cached copy in NVM, which can temporarily serve as the original data. This scheme eliminates the copy-out operation for a substantial number of cached data, thereby enhancing garbage collection efficiency. Experimental results show that the proposed scheme reduces the copy-out overhead of garbage collection by 51.4% and decreases the standard deviation of response time by 35.4% on average

Reducing Write Amplification of Flash Storage through Cooperative Data Management with NVM

Write amplification is a critical factor that limits the stable performance of flash-based storage systems. To reduce write amplification, this article presents a new technique that cooperatively manages data in flash storage and nonvolatile memory (NVM). Our scheme basically considers NVM as the cache of flash storage, but allows the original data in flash storage to be invalidated if there is a cached copy in NVM, which can temporarily serve as the original data. This scheme eliminates the copy-out operation for a substantial number of cached data, thereby enhancing garbage collection efficiency. Simulated results show that the proposed scheme reduces the copy-out overhead of garbage collection by 51.4% and decreases the standard deviation of response time by 35.4% on average. Measurement results obtained by implementing the proposed scheme in BlueDBM, 1 an open-source flash development platform developed by MIT, show that the proposed scheme reduces the execution time and increases IOPS by 2-21% and 3-18%, respectively, for the workloads that we considered. This article is an extended version of Lee et al. [2016], which was presented at the 32nd International Conference on Massive Data Storage Systems and Technology in 2016