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|Title:||Optimizing hadoop parameter settings with gene expression programming guided PSO|
|Keywords:||Hadoop;Mapreduce;Big data analytics;Gene expression programming;Particle swarm optimization|
|Publisher:||John Wiley & Sons|
|Citation:||Concurrency Computation: Practice and Experience, 6: pp.42561-42571,(2016)|
|Abstract:||Hadoop MapReduce has become a major computing technology in support of big data analytics. The Hadoop framework has over 190 configuration parameters, and some of them can have a significant effect on the performance of a Hadoop job. Manually tuning the optimum or near optimum values of these parameters is a challenging task and also a time consuming process. This paper optimizes the performance of Hadoop by automatically tuning its configuration parameter settings. The proposed work first employs gene expression programming technique to build an objective function based on historical job running records, which represents a correlation among the Hadoop configuration parameters. It then employs particle swarm optimization technique, which makes use of the objective function to search for optimal or near optimal parameter settings. Experimental results show that the proposed work enhances the performance of Hadoop significantly compared with the default settings. Moreover, it outperforms both rule-of-thumb settings and the Starfish model in Hadoop performance optimization.|
|Appears in Collections:||Dept of Electronic and Computer Engineering Research Papers|
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