Abstract: As the scale of supercomputing systems continues to expand,the probability of computing node failures and anomalies also increases,seriously affecting the stability of systems.Traditional fault response methods mainly apply post event response and remedial policies,which can only partially recover the wastage.Predicting node failures and anomalies in advance can provide more response and processing time,thus has become a research hotspot.This paper proposes a node failure and anomaly prediction method for improving the stability of supercomputing systems and reducing the waste of computing resources.The method analyzes the historical running data of the system,and marks anomalies through unsupervised methods plus a small amount of manual assistance.These anomalies are used to find correlating pre-running features are discovered in the original dataset.Prediction models are then established using machine learning methods.This prediction method achieves the precision over 78% and the recall around 90% through cross validation over the original dataset,and it also ensures sufficient lead time.The dataset used in the evaluation comes from the raw running data of a real supercomputing system,proving the applicability of the proposed method.

Key words: Data analysis, Anomaly prediction, Running feature, Prediction model