Flash memory is hugely been using for small handheld to large enterprise applications due to its attractive features. However, flash hardware characteristics like erasebefore-write and limited-erase-cycles are becoming big hurdle for researchers to provide reliable and performance oriented system softwares.
The effective way to mitigate the impacts of flash drawbacks is to manage the data by its access patterns. But, as a side affect, such approach imposes the demand of high main memory space and lengthy time
for initialization.
This thesis proposes the work towards reliable data management for NAND flash memory based storage systems. Proposed framework classifies data intellectually according to their access frequencies and proves the efficiency and effectiveness for data and memory management on all levels of system operations. Meticulous analytical discussions and comprehensive
experimental results demonstrate the highly improved system performance achieved by considering the diverse natures of data.
