  Non-volatile memory (NVM) technologies suffer from limited write endurance.
To address this challenge, we propose Predict and Write (PNW), a K/V-store that
uses a clustering-based machine learning approach to extend the lifetime of
NVMs. PNW decreases the number of bit flips for PUT/UPDATE operations by
determining the best memory location an updated value should be written to. PNW
leverages the indirection level of K/V-stores to freely choose the target
memory location for any given write based on its value. PNW organizes NVM
addresses in a dynamic address pool clustered by the similarity of the data
values they refer to. We show that, by choosing the right target memory
location for a given PUT/UPDATE operation, the number of total bit flips and
cache lines can be reduced by up to 85% and 56% over the state of the art.
