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Best Algorithm to Improve Flash SSD Life Expectancy - Static Data Wear Leveling

Posted by Biraj Jamalayam on Tuesday, 11 Feb 2014

Unlike Dynamic Data Wear Leveling, Static Wear Leveling algorithm not only rotates the dynamic blocks, but also the blocks that store a static data partition. In many embedded applications, a significant portion of memory space is actually used by a large static partition such as OS or Application program files. Typically these files are written once and read multiple times as on the device power-up sequence. The large static data block leaves only a small memory space for file updates or data recording. Due to such unbalanced usage the dynamic memory area gets extensive wear. Once any of these high usage blocks approach the endurance limit, and begin to lose data, the whole drive would fail. To avoid this situation, a Static Wear leveling algorithm was implemented.

When the device is in a standby (non-active) mode, the algorithm drives the Flash controller to re-write the static partition from the less used blocks to the dynamic partition of the higher used blocks. By readjusting the block usage, the memory wear will be evenly spread between the static and dynamic memory partitions. The algorithm defines a usage threshold which when reached by any one memory block initiates a static partition rotation, or swapping, of the highest usage blocks for the lowest usage ones.  This process typically occurs in the background while the Flash controller is in a stand-by or non-active mode.

While the Static Data Wear Leveling algorithm can slow down Flash Drive operation during the swapping sequence and also increases the overall block usage due to the rotation overhead, it is nonetheless the best method to maximize the SSD life.

Fortasa Memory Systems, Inc. products utilize both the Static and Dynamic Data Wear Leveling algorithms and offer the highest product reliability and usable life of SSD and Flash Drive/Module.

Please contact Fortasa for more information about Fortasa Products reliability.