基于大数据平台多标杆应用的公共参数集提取框架优化

Int. J. Networked Distributed Comput. Pub Date : 2018-09-28 DOI:10.2991/IJNDC.2018.4.6.1

Jongyeop Kim, Abhilash Kancharla, Jongho Seol, Indy Park, N. Park

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引用次数: 1

摘要

Apache Hadoop分布式文件系统(Hadoop Distributed File System, HDFS)[1]作为大数据处理框架的突出引擎之一[2]，其在由多个节点组成的集群上具有分布式处理能力[3]。这个开放源代码的核心技术称为map and reduce，它通过将一个大任务适当地拆分到每个节点，并通过进程间通信将其合并来实现。

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Optimized Common Parameter Set Extraction Framework by Multiple Benchmarking Applications on a Big Data Platform

The Apache Hadoop Distributed File System (HDFS) [1] is one of the prominent engines as a big data processing framework [2] with its distributed processing capabilities over a cluster that composed of multiple nodes [3]. The core technology of this open source is called map and reduce, which is accomplished by appropriately splitting a big task into each node and merging it through inter process communication.

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Int. J. Networked Distributed Comput.

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