R&D: Unified RAM Management Scheme for NAND Flash Storage Devices

Discrete Dynamics in Nature and Society has published, in Cognitive Modeling of Multimodal Data Intensive Systems for Applications in Nature and Society 2021, an article written by A. Xiaochang Li, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China, B. Jichen Chen, Xi’an Microelectronics Technology Institute, Xi’an, China, C. Zhengjun Zhai, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China, D. Mingchen Feng, College of Information Engineering, Northwest A&F University, Xi’an, China, and E. Xin Ye, Xi’an Technological University, Xi’an, China.

Abstract: “In NAND flash storage devices, the random access memory (RAM) is composed of a data buffer and mapping cache that play critical roles in storage performance. Furthermore, as the capacity growth rate of RAM chips lags far behind that of flash memory chips, determining how to take advantage of precious RAM is still a crucial issue. However, most existing buffer management studies on storage devices report performance degradation since these devices cannot refine reference regularities such as sequential, hot, or looping data patterns. In addition, most of these studies focus only on separately managing the data buffer or mapping cache. Compared with the existing buffer/cache management schemes (BMSs), we propose a unified RAM management (URM) scheme for not only the mapping cache but also the data buffer in NAND flash storage devices. URM compresses the mapping table to save memory space, and the remaining dynamic RAM space is used for the data buffer. For the data buffer part, we utilize the program counter-technique in the host layer that provides automatic pattern recognition for different applications, in contrast to existing BMSs. The program counter-technique in our design is able to distinguish four patterns. According to these patterns, the data buffer is divided into four size-adjustable zones. Therefore, our approach is linked to multimodal data and used in a data-intensive system. In particular, in URM, we use a multivariate classification to predict prefetching length in mapping buffer management. Our multivariate classification is transformed into multiple binary classifications (logistic regressions). Finally, we extensively evaluate URM using various realistic workloads, and the experimental results show that, compared with three data buffer management schemes, CFLRU, BPLRU, and VBBMS, URM can improve the hit ratio of data buffer and save response time by an average to 32% and 18%, respectively.“