Large-scale datacenters often experience memory failures, where Uncorrectable
Errors (UEs) highlight critical malfunction in Dual Inline Memory Modules
(DIMMs). Existing approaches primarily utilize Correctable Errors (CEs) to
predict UEs, yet they typically neglect how these errors vary between different
CPU architectures, especially in terms of Error Correction Code (ECC)
applicability. In this paper, we investigate the correlation between CEs and
UEs across different CPU architectures, including X86 and ARM. Our analysis
identifies unique patterns of memory failure associated with each processor
platform. Leveraging Machine Learning (ML) techniques on production datasets,
we conduct the memory failure prediction in different processors' platforms,
achieving up to 15% improvements in F1-score compared to the existing
algorithm. Finally, an MLOps (Machine Learning Operations) framework is
provided to consistently improve the failure prediction in the production
environment.

大规模数据中心中的内存故障和无法纠正错误发生率之间的相关性，在不同的 CPU 架构中进行了调查和分析。利用机器学习技术在不同的处理器平台上进行了内存故障预测，并相比现有算法提高了 15% 的 F1 分数。最后，提供了一个 MLOps 框架，以在生产环境中持续改进故障预测。