• Journal of KIISE
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• v.45 no.1
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• pp.9-14
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• 2018

Tar is a Linux command that combines several files into a single file. Combining multiple small files into large files increases the compression efficiency and data transfer speed. However, tar has a problem in that smaller target files, result in a lower performance. In this paper, we show that this performance degradation occurs when tar reads the data from the target files and propose qtar (quick tar) to solve this problem via flash-level remapping. When the size of an I/O request is less than 1 MB, the I/O performance decreases proportionally to the decrease in size of the I/O request. Since tar reads the data of files one by one, a smaller file size results in a lower performance. Therefore, the remapping technique is implemented in qtar to read data from the target files at the maximum I/O size regardless of the size of each file. Our evaluations show that the execution time with qtar is reduced by up to 3.4 times compared to that with tar.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1167-1175
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• 2016

This paper proposes an efficient page-level mapping algorithm that reduces the erase count in the FTL for flash memory systems. By maintaining the weight for each write request in the request buffer, the proposed algorithm estimates the degree of temporal locality for each incoming write request. To exploit temporal locality deliberately for determination of hot request, the degree of temporal locality should be much higher than the reference point determined experimentally. While previous LRU algorithm treats a new write request to have high temporal locality, the proposed algorithm allows write requests that are estimated to have high temporal locality to access hot blocks to store hot data intensively. The pages are more frequently updated in hot blocks than warm blocks. A hot block that has most of invalid pages is always selected as victim block at Garbage Collection, which results in delayed erase operation and in reduced erase count. Experimental results show that erase count is reduced by 9.3% for real I/O workloads, when compared to the previous LRU algorithm.