  DRAM failure prediction is a vital task in AIOps, which is crucial to
maintain the reliability and sustainable service of large-scale data centers.
However, limited work has been done on DRAM failure prediction mainly due to
the lack of public available datasets. This paper presents a comprehensive
empirical evaluation of diverse machine learning techniques for DRAM failure
prediction using a large-scale multi-source dataset, including more than three
millions of records of kernel, address, and mcelog data, provided by Alibaba
Cloud through PAKDD 2021 competition. Particularly, we first formulate the
problem as a multi-class classification task and exhaustively evaluate seven
popular/state-of-the-art classifiers on both the individual and multiple data
sources. We then formulate the problem as an unsupervised anomaly detection
task and evaluate three state-of-the-art anomaly detectors. Further, based on
the empirical results and our experience of attending this competition, we
discuss major challenges and present future research opportunities in this
task.
