An In-Depth Correlative Study Between DRAM Errors and Server Failures in Production Data Centers

Dynamic Random Access Memory (DRAM) errors are prevalent and lead to server failures in production data centers. However, little is known about the correlation between DRAM errors and server failures in state-of-the-art field studies on DRAM error measurement. To fill this void, we present an in-depth datadriven correlative analysis between DRAM errors and server failures, with the primary goal of predicting server failures based on DRAM error characterization and hence enabling proactive reliability maintenance for production data centers. Our analysis is based on an eight-month dataset collected from over three million memory modules in the production data centers at Alibaba. We find that the correctable DRAM errors of most server failures only manifest within a short time before the failures happen, implying that server failure prediction should be conducted regularly at short time intervals for accurate prediction. We also study various impacting factors (including component failures in the memory subsystem, DRAM configurations, types of correctable DRAM errors) on server failures. Furthermore, we design a machinelearning-based server failure prediction workflow and demonstrate the feasibility of server failure prediction based on DRAM error characterization. To this end, we report 14 findings from our measurement and prediction studies.