MapReduce任务调度的资源优化研究  

Research on resource optimization of MapReduce task scheduling

作　　者：李琴[1] 

机构地区：[1]贵州交通职业技术学院,贵阳550008

出　　处：《互联网天地》2016年第11期33-38,共6页China Internet

摘　　要：现有的大数据处理技术以面向数据并行编程模型为核心,计算任务的并行化必然会面临计算任务调度策略和资源配置优化问题。简要分析了当前大数据处理技术,介绍了MapReduce编程模型及其支撑系统,重点分析了MapReduce任务调度的资源优化策略研究现状,并对未来任务调度的资源配置优化提出了挑战。The existing big data processing techniques mostly focus on the parallel computing models, and the parallel of computing tasks coexist with task scheduling and resource optimization. The present big data processing technologies were analyzed briefly, and the programming model and supporting system of MapReduce were introduced. Then the research status of resource optimization of MapReduce task scheduling was analyzed prominently. At last, the challenges that resource optimization of task scheduling in the future were proposed.

关 键 词：大数据处理 MapReduce编程模型 任务调度 资源优化 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]