In NAND flash memory, wear leveling is employed to evenly distribute program/erase bit flips so as to prevent overall chip failure caused by excessive writes to certain hot spots of the chip. In this paper, we analyze latest wear leveling algorithms, and propose Observational Wear Leveling (OWL). OWL considers the temporal locality of write activities at runtime when blocks are allocated. It also transfers data between blocks of different ages. From our experiments, with minimal additional space and time overhead, OWL can improve wear evenness by as much as 29.9% and 43.2% compared to two state-of-the-art wear leveling algorithms, respectively.
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