MapReduce Integrated Multi-algorithm for HPC Running State Analysis

ShuRen Liu (Northwest Branch of PetroChina Research Institute of Petroleum Exploration and Development)
ChaoMin Feng (Northwest Branch of PetroChina Research Institute of Petroleum Exploration and Development)
HongWu Luo (Northwest Branch of PetroChina Research Institute of Petroleum Exploration and Development)
Ling Wen (Northwest Branch of PetroChina Research Institute of Petroleum Exploration and Development)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 14, No 3: September 2016

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.