MapReduce并行编程模型研究综述

Survey of MapReduce Parallel Programming Model

MapReduce并行编程模型通过定义良好的接口和运行时支持库,能够自动并行执行大规模计算任务,隐藏底层实现细节,降低并行编程的难度.本文对MapReduce的国内外相关研究现状进行了综述,阐述和分析了当前国内外与MapReduce相关的典型研究成果的特点和不足,重点对MapReduce涉及的关键技术(包括:模型改进、模型针对不同平台的实现、任务调度、负载均衡和容错)的研究现状进行了深入的分析.本文最后还对MapReduce未来的发展趋势进行了展望.

Through well-defined interfaces and runlime support library, MapReduce parallel programming model can auto- mafically perform the large-scale computing tasks in paraUel,hide the underlying implementation details,and reduce the difficulty of parallel programming. This paper reviews the domestic and overseas research of the MapReduce, describes and analyzes the charac- teristics and lack of the typical research achievements about MapReduce at home and abroad. Then this paper focus on the in-depth analysis of the key technologies about MapReduce （including：model optimization,model implementation according to the different platforms,task scheduling, load balancing, and fault tolerance）.Finally, this paper prospects the MapReduce for the future trend.