GDL

MapReduce Integrated Multi-algorithm for HPC Running State Analysis

Journal from gdlhub / 2016-11-09 03:35:50
Oleh : ShuRen Liu, ChaoMin Feng, HongWu Luo, Ling Wen, Telkomnika
Dibuat : 2016-09-01, dengan 1 file

Keyword : High-Performance clusters (HPC); Hadoop; MapReduce; K-means; Naive Bayes
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/3771

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.

Deskripsi Alternatif :

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.

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