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.

中文翻译：

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预测和写入：使用 K-Means 聚类延长 NVM 存储的生命周期

非易失性存储器 (NVM) 技术的写入耐久性有限。为了应对这一挑战，我们提出了预测和写入 (PNW)，这是一种 K/V 存储，它使用基于聚类的机器学习方法来延长 NVM 的生命周期。PNW 通过确定更新值应写入的最佳内存位置来减少 PUT/UPDATE 操作的位翻转次数。PNW 利用 K/V 存储的间接级别，根据其值自由选择任何给定写入的目标内存位置。PNW 在动态地址池中组织 NVM 地址，这些地址池由它们所引用的数据值的相似性聚类。我们表明，通过为给定的 PUT/UPDATE 操作选择正确的目标内存位置，与现有技术相比，总位翻转和缓存行的数量最多可以减少 85% 和 56%。