DRAM failures are one of the major hardware threats to the reliability of large-scale data centers since the uncorrectable errors in DRAMs may cause servers to shut down. Existing works try to solve this problem by predicting DRAM failures in advance with Machine Learning models. In these works, correctable errors (CEs) are generally deemed as the most important feature. The major reason behind CEs' emergence is the accumulated stress caused by intensive workloads. Moreover, defective DRAMs will not manifest themselves as system errors until the defective cells are accessed by some specific workloads. Therefore, the running workloads on a server are also important for DRAM failure prediction. In this paper, we focus on the impact of workloads on DRAM failures. We design the workload features from both macroscopical and microscopical aspects, i.e. node-level performance metrics and cell-level DRAM access pattern, respectively. Furthermore, we propose Hierarchical DRAM Error Code (HiDEC) to represent the DRAM access pattern. We leverage several Decision Tree-based models for DRAM failure prediction to highlight the generality of our designed features. Experiments are carried out based on the dataset collected from a real-world commercial data center. The results show that both macroscopic and microscopic features can bring significant improvements to the prediction performance.

Many software services today are hosted on cloud computing platforms, such as Amazon EC2, due to many benefits like reduced operational costs. However, node failures in these platforms can impact the availability of their hosted services and potentially lead to large financial losses. Predicting node failures before they actually occur is crucial, as it enables DevOps engineers to minimize their impact by performing preventative actions. However, such predictions are hard due to many challenges like the enormous size of the monitoring data and the complexity of the failure symptoms. AIOps ( A rtificial I ntelligence for IT Op eration s ), a recently introduced approach in DevOps, leverages data analytics and machine learning to improve the quality of computing platforms in a cost-effective manner. However, the successful adoption of such AIOps solutions requires much more than a top-performing machine learning model. Instead, AIOps solutions must be trustable, interpretable, maintainable, scalable, and evaluated in context. To cope with these challenges, in this article we report our process of building an AIOps solution for predicting node failures for an ultra-large-scale cloud computing platform at Alibaba. We expect our experiences to be of value to researchers and practitioners, who are interested in building and maintaining AIOps solutions for large-scale cloud computing platforms.