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计算机系统应用英文版:2014,23(4):1-6,11

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MapReduce并行计算技术发展综述

应毅¹, 刘亚军^1,2

(1.三江学院计算机科学与工程学院, 南京 210012;2.东南大学计算机科学与工程学院, 南京 210096)

Survey of Developments of MapReduce Parallel Computing Technology

YING Yi¹, LIU Ya-Jun^1,2

(1.College of Computer Science and Technology, Sanjiang University, Nanjing 210012, China;2.School of Computer Science and Engineering, Southeast University, Nanjing 210096, China)

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Received:September 06, 2013 Revised:October 12, 2013

中文摘要: 经过几年的发展，并行编程模型MapReduce产生了若干个改进框架，它们都是针对传统MapReduce的不足进行的修正或重写. 本文阐述和分析了这些研究成果，包括：以HaLoop为代表的迭代计算框架、以Twitter Storm为代表的实时计算框架、以Apache Hama为代表的图计算框架以及以Apache YARN为代表的框架管理平台. 这些专用系统在大数据领域发挥着越来越重要的作用.

中文关键词: MapReduce Hadoop 并行计算大数据处理

Abstract:With the rapid development of recent years, some improved framework of MapReduce parallel programming model appeared. They are correction and recoding against lack of MRv1. This paper describes and analyzes this research achievements, including iterative computing framework as represented by HaLoop, real-time computing framework as represented by Twitter Storm, graph computing framework as represented by Apache Hama, computing resources negotiation platform as represented by Apache YARN. These special systems play a vital role in BigData fields.

keywords: MapReduce Hadoop parallel computing big data processing

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基金项目:江苏省卓越工程师（软件类）计划试点专业（苏教高函[2012]17号）

Author Name	Affiliation
YING Yi	College of Computer Science and Technology, Sanjiang University, Nanjing 210012, China
LIU Ya-Jun	College of Computer Science and Technology, Sanjiang University, Nanjing 210012, China School of Computer Science and Engineering, Southeast University, Nanjing 210096, China

Author Name	Affiliation
YING Yi	College of Computer Science and Technology, Sanjiang University, Nanjing 210012, China
LIU Ya-Jun	College of Computer Science and Technology, Sanjiang University, Nanjing 210012, China School of Computer Science and Engineering, Southeast University, Nanjing 210096, China

引用文本：
应毅,刘亚军.MapReduce并行计算技术发展综述.计算机系统应用,2014,23(4):1-6,11
YING Yi,LIU Ya-Jun.Survey of Developments of MapReduce Parallel Computing Technology.COMPUTER SYSTEMS APPLICATIONS,2014,23(4):1-6,11