Author:
Hedayati Soudabeh,Maleki Neda,Olsson Tobias,Ahlgren Fredrik,Seyednezhad Mahdi,Berahmand Kamal
Abstract
AbstractHadoop 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.
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Software
Cited by
2 articles.
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