用于Hadoop2.x的MapReduce性能评估模型

基于MapReduce的程序被越来越多地应用于大型数据分析的应用中.Apache Hadoop是最常用的开源MapReduce模型之一.程序运行时间的缩短对于MapReduce程序以及所有数据处理应用而言至关重要,而能够准确估算MapReduce程序的执行时间是优化程序的重要环节.本文定义了一个在Hadoop2.x版本...

MapReduce Performance Evaluation Model for Hadoop2.x

MapReduce-based systems are increasingly being used for large-scale data analysis applications. Apache Hadoop is one of the most common open-source implementations of such paradigm. Minimizing the execution time is vital for MapReduce as well as for all data-processing applications, and the accurate estimation of execution time is essential for optimization. In this study, the author created a MapReduce performance model for Hadoop2.x that can precisely estimate the execution time of workload in MapReduce. This model combines a precedence tree model that can capture dependencies between different tasks in one MapReduce job, and a queueing network model that can capture the intra-job synchronization constraints. Such an analytical performance model is a particularly attractive tool as it might provide reasonably accurate job response time at significantly lower cost than the simulation experiment of real data-analysis systems. Furthermore, a clear understanding of systematic job response time under different circumstances is key to making decisions in MapReduce workload management and resource capacity planning.