论文标题
预测和写入：使用K-均值聚类来延长NVM存储的寿命
Predict and Write: Using K-Means Clustering to Extend the Lifetime of NVM Storage
论文作者
论文摘要
非易失性记忆（NVM）技术的写入耐力有限。为了应对这一挑战，我们提出了预测和写作（PNW），这是一种K/V商店，使用基于聚类的机器学习方法来延长NVM的寿命。 PNW通过确定应将更新值写入的最佳内存位置来减少PUT/更新操作的位数。 PNW利用K/V商店的间接级别根据其值自由选择目标内存位置。 PNW在动态地址池中组织了NVM地址，该地址由它们所指的数据值的相似性集群。我们表明，通过为给定的PUT/更新操作选择合适的目标存储位置，可以将总数翻转和缓存线的数量减少高达85％和56％。
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.
