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In the data center, unexpected downtime caused by memory failures can lead toa decline in the stability of the server and even the entire informationtechnology infrastructure, which harms the business. Therefore, whether thememory failure can be accurately predicted in advance has become one of themost important issues to be studied in the data center. However, for the memoryfailure prediction in the production system, it is necessary to solve technicalproblems such as huge data noise and extreme imbalance between positive andnegative samples, and at the same time ensure the long-term stability of thealgorithm. This paper compares and summarizes some commonly used skills and theimprovement they can bring. The single model we proposed won the top 14th inthe 2nd Alibaba Cloud AIOps Competition belonging to the 25th PAKDD conference.It takes only 30 minutes to pass the online test, while most of the othercontestants' solution need more than 3 hours. Codes has been open source tohttps://www.github.com/ycd2016/acaioc2.

Highly Efficient Memory Failure Prediction using Mcelog-based Data Mining and Machine Learning