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Efficient ELM-Based Two Stages Query Processing Optimization for Big Data

机译:基于高效ELM的大数据两阶段查询处理优化

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摘要

MapReduce and its variants have emerged as viable competitors for big data analysis with a commodity cluster of machines. As an extension of MapReduce, ComMapReduce realizes the lightweight communication mechanisms to enhance the performance of query processing applications for big data. However, different communication strategies of ComMapReduce can substantially affect the executions of query processing applications. Although there is already the research work that can identify the communication strategies of ComMapReduce according to the characteristics of the query processing applications, some drawbacks still exist, such as relative simple model, too much user participation, and relative simple query processing execution. Therefore, an efficient ELM-based two stages query processing optimization model is proposed in this paper, named ELM to ELM (E2E) model. Then, we develop an efficient sample training strategy to train our E2E model. Furthermore, two query processing executions based on the E2E model, respectively, Just-in-Time execution and Queue execution, are presented. Finally, extensive experiments are conducted to verify the effectiveness and efficiency of the E2E model.
机译:MapReduce及其变体已经成为使用商品集群的机器进行大数据分析的有力竞争者。作为MapReduce的扩展,ComMapReduce实现了轻量级的通信机制,以增强大数据查询处理应用程序的性能。但是,ComMapReduce的不同通信策略可能会严重影响查询处理应用程序的执行。尽管已经有研究工作可以根据查询处理应用程序的特征来识别ComMapReduce的通信策略,但是仍然存在一些缺点,例如相对简单的模型,过多的用户参与以及相对简单的查询处理执行。因此,本文提出了一种高效的基于ELM的两阶段查询处理优化模型,称为ELM到ELM(E2E)模型。然后,我们开发一种有效的样本培训策略来训练我们的E2E模型。此外,还提出了两种基于端到端模型的查询处理执行,分别是即时执行和队列执行。最后,进行了广泛的实验以验证E2E模型的有效性和效率。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第9期|236084.1-236084.12|共12页
  • 作者单位

    Liaoning Univ, Sch Informat, Shenyang 110036, Liaoning, Peoples R China.;

    Liaoning Univ, Sch Informat, Shenyang 110036, Liaoning, Peoples R China.;

    Liaoning Univ, Sch Informat, Shenyang 110036, Liaoning, Peoples R China.;

    Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China.;

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