SSDs use a huge number of internal parameters to achieve a tricky balance between performance, wear, and cost. The SSD Guy likes to compare this to a recording studio console like the one in this post’s graphic to emphasize just how tricky it is for SSD designers to find the right balance. Imagine trying to manage all of those knobs! (The picture is JacoTen’s Wikipedia photo of a Focusrite console.)
Vendors who produce differentiated SSDs pride themselves in their ability to fine-tune these parameters to achieve better performance or endurance than competing products.
About a year ago I suggested to the folks at NVMdurance that they might consider applying their machine learning algorithm to this problem. (The original NVMdurance product line was described in a Memory Guy post a while ago.) After all, the company makes a machine learning engine that tunes the numerous internal parameters of a NAND flash chip to extend the chip’s life while maintaining the specified performance. SSD management would be a natural use of machine learning since both SSDs and NAND flash chips currently use difficult and time-consuming manual processes to find the best mix of parameters to drive the design.
Little did I know that NVMdurance’s researchers Continue reading “Managing SSDs Using Machine Learning”