Abstract
Flash memory is widely used in embedded devices and enterprise storage systems. Currently, flash-based storage devices usually use a flash translation layer (FTL) to cope with the special features of flash memory. Many methods for the design and implementation of the FTL have been proposed, such as BAST (block-associative sector translation), FAST (fully associative sector translation), and IPL (inpage logging), of which IPL has been demonstrated to have the best performance. However, IPL offers little consideration to reducing merge operations that consequently result in the degradation of the overall performance of flash-memory storage systems. We propose an improvement to IPL, called adaptive IPL (AIPL). The idea of adaptive IPL is to make the log region in a block resizable, therefore a hot block (i.e., a write-intensive block) will use a large log region so as to absorb more page updates and in turn reduce the merge operations, while a cold block, i.e., a block rarely written to, will use a small log region. This is realized by first detecting the update pattern of a block and then presenting an updatepattern-based algorithm to dynamically adjust the log region size of a newly allocated block. We conduct experiments on TPC-C traces and synthetic traces and compare the performance of AIPL with other competitors in terms of merge count, write count and elapsed time. The results demonstrate that compared with IPL, AIPL can reduce merge operations by 65% and write operations by 54% on average.
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Ke Lu is currently a PhD candidate in the School of Computer Science and Technology at University of Science and Technology of China (USTC), China. His research interests are spatiotemporal databases and flash-based databases.
Peiquan Jin received his PhD in Computer Science from the University of Science and Technology of China (USTC), China in 2003. He is now an associate professor in the School of Computer Science and Technology, USTC. He was a visiting scientist in University of Kaiserslautern, Germany in 2009. Currently, he is a senior member of CCF, and a member of IEEE and ACM. In recent years, his research interests have focused on spatiotemporal databases, flash-based databases, and Web information retrieval.
Puyuan Yang is currently a PhD candidate in the School of Computer Science and Technology at the University of Science and Technology of China (USTC), China. His research interests are in storage systems and flash-based databases.
Shouhong Wan is a lecturer in the School of Computer Science and Technology at University of Science and Technology of China (USTC), China. She received her BS and MS in Computer Science both from Anhui University. Her research interests include spatiotemporal information management and image processing. She has published over 40 papers in journals and conferences.
Lihua Yue has been a full professor in the School of Computer Science and Technology at University of Science and Technology of China (USTC), China since 2001. She received her BS and MS in Computer Science both from USTC. Her research interests include flash-based databases, spatiotemporal databases, information retrieval, and image processing. She is a committee member of the Database Society of the China Computer Federation (CCF DBS) and has served as a PC member of many conferences.
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Lu, K., Jin, P., Yang, P. et al. Adaptive in-page logging for flash-memory storage systems. Front. Comput. Sci. 8, 131–144 (2014). https://doi.org/10.1007/s11704-013-3013-6
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DOI: https://doi.org/10.1007/s11704-013-3013-6