Data Mining Association Rule Algorithm Based on Hadoop

Huang Suyu
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引用次数: 5

Abstract

This paper proposes a kind of speculated task scheduling based on data locality, aimed on the current not high optimization of task scheduling algorithm on Hadoop platform. This algorithm combined the local features of data at different nodes, through the length proportion of Map and Reduce task on computing nodes, adopts more accurate task scheduling detection mode than current algorithm to find out fast or slow node, and backup for backward task with longest remaining start time at fast node, use mobile computing instead of mobile data. It conducts experiment in Hadoop environment, the result demonstrates that this algorithm has shorten the task average operation time than current algorithm, while speed up the task execution efficiency.
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基于Hadoop的数据挖掘关联规则算法
针对目前Hadoop平台上任务调度算法优化程度不高的问题,提出了一种基于数据局部性的任务调度算法。该算法结合不同节点数据的局部特征,通过Map和Reduce任务在计算节点上的长度比例,采用比现有算法更精确的任务调度检测模式,找出快节点或慢节点,并在快节点备份剩余起始时间最长的落后任务,使用移动计算代替移动数据。在Hadoop环境下进行了实验,结果表明该算法比现有算法缩短了任务的平均运行时间,同时提高了任务的执行效率。
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