基于YARN资源调度器的MapReduce作业数调节方法  

Number Adjustment Method of MapReduce Jobs Based on YARN Resource Scheduler

作　　者：廉华 刘瑜[1] LIAN Hua;LIU Yu(Faculty of Mechanical Engineering&Automation,Zhejiang Sci-Tech University,Hangzhou 310018,China)

机构地区：[1]浙江理工大学机械与自动控制学院,杭州310018

出　　处：《计算机系统应用》2020年第3期218-222,共5页Computer Systems & Applications

摘　　要：YARN是Hadoop的一个分布式的资源管理系统,用来提高分布式集群的内存、I/O、网络、磁盘等资源的利用率.然而,YARN的配置参数众多,要对其人工调优并获得最佳的性能费时费力.本文在现有的YARN资源调度器的基础上,结合了一种闭环反馈控制方法,可在集群运行状态下动态地对MapReduce(MR)作业数进行优化,省去了人工调整参数的过程.实验表明,在YARN的容量调度器和公平调度器的基础上使用该方法,相比于默认配置,MR作业完成时间分别减少53%和14%左右.YARN is a distributed resource management system of Hadoop.It can be used to improve the utilization of memory,I/O,network,disk and other resources of distributed cluster.However,there are many configuration parameters in YARN.Due to this reason,manual tuning of Hadoop performance to get the best performance is difficult and timeconsuming.Based on the existing YARN resource scheduler,a successive approximation closed-loop feedback control method is proposed.This method can dynamically tune the parallel number of MapReduce(MR)jobs in the running state of the cluster,and eliminating the process of manual adjustment of parameters.Experiments show that the proposed approach reduces the MR operation time for 53%and 14%based on capacity scheduler and fair scheduler,respectively,compared with the default configuration.

关 键 词：大数据 资源调度器 MAPREDUCE 闭环反馈控制 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]