Progressive filling partitioning and mapping algorithm for Spark based on allocation fitness degree |
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Authors: | Chen BIAN Jiong YU Wei-rong XIU Bin LIAO Chang-tian YING Yu-rong QIAN |
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Affiliation: | 1. College of Software,Xinjiang University,Urumqi 830008,China;2. College of Statistics and Information,Xinjiang University of Finance and Economics,Urumqi 830012,China |
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Abstract: | The job execution mechanism of Spark was analyzed,task efficiency model and Shuffle model were established,then allocation fitness degree (AFD) was defined and the optimization goal was put forward.On the basis of the model definition,the progressive filling partitioning and mapping algorithm (PFPM) was proposed.PFPM established the data distribution scheme adapting Reducers’ computing ability to decrease synchronous latency during Shuffle process and increase cluster the computing efficiency.The experiments demonstrate that PFPM could improve the rationality of workload distribution in Shuffle and optimize the execution efficiency of Spark. |
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Keywords: | parallel computing Spark progressive filling partitioning and mapping allocation fitness degree |
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