虚拟化环境下MapReduce的动态性能感知Reduce任务调度

Rathinaraja Jeyaraj, V. S. Ananthanarayana
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引用次数: 2

摘要

Hadoop MapReduce作为一种来自云的服务被各种研究和商业社区广泛使用。Hadoop MapReduce通常作为托管在云数据中心虚拟化环境中的服务提供。MapReduce的虚拟机集群被放置在Cloud Data-Center的机架上,以实现容错。但是,由于硬件的异构性和同址虚拟机的干扰,会导致同一任务的延迟变化,从而对虚拟机的动态/异构性能产生负面影响。此外,限制中间记录的数量和在正确的虚拟节点上放置reduce任务对于进一步减少MapReduce作业延迟也很重要。在本文中,我们引入了多级每节点组合器来最小化中间记录的数量,并引入了基于动态排名的MapReduce Job Scheduler来利用虚拟机的动态性能将reduce任务放置在合适的虚拟机上,从而最小化MapReduce作业延迟。为了进行实验和评估,我们在8台不同的物理机上启动了29台虚拟机,在PUMA数据集上运行wordcount作业。我们提出的方法将wordcount作业的总作业延迟提高了33%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Performance Aware Reduce Task Scheduling in MapReduce on Virtualized Environment
Hadoop MapReduce as a service from cloud is widely used by various research, and commercial communities. Hadoop MapReduce is typically offered as a service hosted on virtualized environment in Cloud Data-Center. Cluster of virtual machines for MapReduce is placed across racks in Cloud Data-Center to achieve fault tolerance. But, it negatively introduces dynamic/heterogeneous performance for virtual machines due to hardware heterogeneity and co-located virtual machine's interference, which cause varying latency for same task. Alongside, curbing number of intermediate records and placing reduce tasks on right virtual node are also important to minimize MapReduce job latency further. In this paper, we introduce Multi-Level Per Node Combiner to minimize the number of intermediate records and Dynamic Ranking based MapReduce Job Scheduler to place reduce tasks on right virtual machine to minimize MapReduce job latency by exploiting dynamic performance of virtual machines. To experiment and evaluate, we launched 29 virtual machines hosted in eight different physical machines to run wordcount job on PUMA dataset. Our proposed methodology improves overall job latency up to 33% for wordcount job.
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