面向 hadoop 框架的成本感知优化资源配置 Map-Reduce 调度器

Archana Bhaskar, Rajeev Ranjan
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引用次数: 0

摘要

分布式数据处理模型一直是数据密集型应用的主要组成部分之一;此外,由于技术的进步,产生了大量不同性质的数据。Hadoop MapReduce 框架负责在开源框架中采用易于部署的机制。现有的 Hadoop MapReduce 框架具有较长的运行时间和较高的输入/输出开销,这主要会影响模型的成本。因此,本研究工作提出了一种优化的成本感知资源配置 MapReduce 模型,也称为高性价比资源配置 MapReduce(CRP-MR)模型。CRP-MR 模型引入了两种综合方法来最小化成本;首先,该模型提出了 Hadoop MapReduce(HMR)调度器中的最优资源优化和最优输入/输出优化清洗。考虑到生物信息学数据集,对 CRP-MR 进行了评估,结果显示 CRP-MR 比现有模型表现更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cost-aware optimal resource provisioning Map-Reduce scheduler for hadoop framework
Distributed data processing model has been one of the primary components in the case of data-intensive applications; furthermore, due to advancements in technologies, there has been a huge volume of data generation of diverse nature. Hadoop map reduce framework is responsible for adopting the ease of deployment mechanism in an open-source framework. The existing Hadoop MapReduce framework possesses high makespan time and high Input/Output overhead and it mainly affects the cost of a model. Thus, this research work presents an optimized cost aware resource provisioning MapReduce model also known as the cost-effective resource provisioning MapReduce (CRP-MR) model. CRP-MR model introduces the two integrated approaches to minimize the cost; at first, this model presents the optimal resource optimization and optimal Input/Output optimization cleansing in the Hadoop MapReduce (HMR) scheduler. CRP-MR is evaluated considering the bioinformatics dataset and CRP-MR performs better than the existing model. 
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