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Impact of I/O and execution scheduling strategies on large scale parallel data mining

机译:I / O和执行调度策略对大规模并行数据挖掘的影响

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In the era of “Big Data”, there is an emerging need to process a massive data set using large cluster system. Anyway, without the right strategies to handle the data, it is challenging to gain a good performance from the system. In this paper, many I/O and execution scheduling strategies for parallel data mining application has been investigated. The goal is to discover strategies that balance the data processing load and better utilize a multi-core cluster system for data mining application. Issues that impact the performance have been explored. The simulation results show that a substantial performance improvement can be obtained especially with a multi-core cluster system when a proper I/O and task execution sequence scheduling has been employed.
机译:在“大数据”时代,迫切需要使用大型集群系统处理海量数据集。无论如何,如果没有正确的策略来处理数据,要从系统中获得良好的性能是一个挑战。本文研究了并行数据挖掘应用程序的许多I / O和执行调度策略。目的是发现平衡数据处理负载并更好地利用多核集群系统进行数据挖掘应用的策略。已经探讨了影响性能的问题。仿真结果表明,当采用适当的I / O和任务执行序列调度时,特别是对于多核集群系统,可以获得显着的性能提升。

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