DRAM Failure Prediction in Large-Scale Data Centers

Cloud computing is developing rapidly. Data centers are important infrastructures of cloud service and JointCloud structure. DRAM failure is one of the main causes which can lead to node outage in data centers. This paper proposes a decision-tree-based DRAM failure prediction method for large-scale data centers of cloud service. We utilize the first public-available DRAM failure prediction dataset released in PAKDD 2021 AIOps competition. We construct a suite of handcrafted features based on the system kernel log data and MCA log data. Feature engineering is detailedly introduced in this paper, which can inspire and foster future research in this field. Harnessing the power of a state-of-the-art classifier (i.e., XGBoost), our method can effectively and timely predict DRAM failures. Our solution has good performance on the PAKDD 2021 dataset, it can generally achieve more than 60% precision in the validation phase. Extensive experiments investigate the performance of variants of our method to validate the significance of different strategies in the proposed solution.