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A Novel Data Aggregation Preprocessing Algorithm in Flash Memory for Iot Based Power Grid Storage System
Author(s) -
Jie Shi,
Hualu Zhang,
Yang Bai,
Guangjie Han,
Gangyong Jia
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2873685
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
NAND flash memory has the advantages of strong shock resistance, low power consumption, non-volatility, and high performance, and it is gradually applied to embedded systems and enterprise servers, such as the IoT-based power grid storage system. Therefore, research on the flash translation layer strategy has become a popular trend for solid-state storage devices. The mapping granularity of different FTL policies is different in the methods of garbage collection. Furthermore, there is a problem of “write amplification”in the write operation of the flash memory device. This paper proposes a data aggregation preprocessing algorithm that can aggregate as many active pages as possible and reduce the proportion of write amplification. The algorithm is implemented and tested on the flash simulation platform FlashSim. The results indicate that our algorithm can effectively improve the performance of the IoT-based power grid storage system, reducing redundant write operations, the number of physical block erasures, the number of physical page read and write times, and the system response time, and extending the life of solid-state devices.

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