MapReduce Integrated Multi-algorithm for HPC Running State Analysis

ShuRen Liu, ChaoMin Feng, HongWu Luo, Ling Wen


High-performance computer clusters are major seismic processing platforms in the oil industry and have a frequent occurrence of failures. In this study, K-means and the Naive Bayes algorithm were programmed into MapReduce and run on Hadoop. The accumulated high-performance computer cluster running status data were first clustered by K-means, and then the results were used for Naive Bayes training. Finally, the test data were discriminated for the knowledge base and equipment failure. Experiments indicate that K-means returned good results, the Naive Bayes algorithm had a high rate of discrimination, and the multi-algorithm used in MapReduce achieved an intelligent prediction mechanism.


High-Performance clusters (HPC); Hadoop; MapReduce; K-means; Naive Bayes

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