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

TL;DR

This paper introduces Predict and Write (PNW), a machine learning-based method using K-Means clustering to significantly reduce bit flips in NVM storage, thereby extending its lifetime.

Contribution

It presents a novel clustering-based approach that dynamically selects memory locations for writes in NVMs to minimize wear, outperforming existing methods.

Findings

Reduces total bit flips by up to 85%.

Decreases cache line writes by up to 56%.

Extends NVM lifetime significantly.

Abstract

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.