MapReduce scheduling algorithms in Hadoop: a systematic study

Soudabeh Hedayati

doi:10.1186/s13677-023-00520-9

MapReduce scheduling algorithms in Hadoop: a systematic study

2023-10-10

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DOI：10.1186/s13677-023-00520-9

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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.

Keywords:

Distributed systems

Resource allocation

Scheduling algorithms

Hadoop

MapReduce

Fair scheduling

MapReduce scheduling algorithms in Hadoop: a systematic study

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