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

Predict and Write: Using K-Means Clustering to Extend the Lifetime of NVM Storage