MapReduce scheduling algorithms in Hadoop: a systematic study

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Cloud Computing-Advances Systems and Applications Pub Date : 2023-10-10 DOI:10.1186/s13677-023-00520-9

Soudabeh Hedayati, Neda Maleki, Tobias Olsson, Fredrik Ahlgren, Mahdi Seyednezhad, Kamal Berahmand

引用次数: 0

Abstract

Abstract Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process that data. MapReduce is a parallel computing framework for processing large amounts of data on clusters. Scheduling is one of the most critical aspects of MapReduce. Scheduling in MapReduce is critical because it can have a significant impact on the performance and efficiency of the overall system. The goal of scheduling is to improve performance, minimize response times, and utilize resources efficiently. A systematic study of the existing scheduling algorithms is provided in this paper. Also, we provide a new classification of such schedulers and a review of each category. In addition, scheduling algorithms have been examined in terms of their main ideas, main objectives, advantages, and disadvantages.

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Hadoop中MapReduce调度算法的系统研究

Hadoop是一个用于在集群上存储和处理海量数据的框架。它使用Hadoop分布式文件系统(HDFS)存储数据，并使用MapReduce处理数据。MapReduce是一个并行计算框架，用于处理集群上的大量数据。调度是MapReduce最关键的方面之一。MapReduce中的调度非常关键，因为它会对整个系统的性能和效率产生重大影响。调度的目标是提高性能，最小化响应时间，并有效地利用资源。本文对现有的调度算法进行了系统的研究。此外，我们还提供了这种调度器的新分类，并对每个类别进行了回顾。此外，还对调度算法的主要思想、主要目标、优点和缺点进行了研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Cloud Computing-Advances Systems and Applications Computer Science-Computer Networks and Communications

CiteScore

6.80

自引率

7.50%

发文量

审稿时长

75 days

期刊介绍： The Journal of Cloud Computing: Advances, Systems and Applications (JoCCASA) will publish research articles on all aspects of Cloud Computing. Principally, articles will address topics that are core to Cloud Computing, focusing on the Cloud applications, the Cloud systems, and the advances that will lead to the Clouds of the future. Comprehensive review and survey articles that offer up new insights, and lay the foundations for further exploratory and experimental work, are also relevant.