Investigating Memory Failure Prediction Across CPU Architectures

• URL: http://arxiv.org/abs/2406.05354v1
• Date: Sat, 8 Jun 2024 05:10:23 GMT
• Title: Investigating Memory Failure Prediction Across CPU Architectures
• Authors: Qiao Yu, Wengui Zhang, Min Zhou, Jialiang Yu, Zhenli Sheng, Jasmin Bogatinovski, Jorge Cardoso, Odej Kao,
• Abstract summary: We investigate the correlation between Correctable Errors (CEs) and Uncorrectable Errors (UEs) across different CPU architectures. Our analysis identifies unique patterns of memory failure associated with each processor platform. We conduct the memory failure prediction in different processors' platforms, achieving up to 15% improvements in F1-score compared to the existing algorithm.
• Score: 8.477622236186695
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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