YARN Schedulers for Hadoop MapReduce Jobs: Design Goals, Issues and Taxonomy

Lokesh, S (2023) YARN Schedulers for Hadoop MapReduce Jobs: Design Goals, Issues and Taxonomy. Recent Advances in Computer Science and Communications, 16 (6): e310822208. pp. 44-55. ISSN 26662558

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Abstract

Big Data processing is a demanding task, and several big data processing frameworks have emerged during recent decades. The performance of these frameworks greatly dependent on resource management models.
Methods:

YARN is one of such models which acts as a resource management layer and provides computational resources for execution engines (Spark, MapReduce, storm, etc.) through its schedulers. The most important aspect of resource management is job scheduling.

Item Type: Article
Uncontrolled Keywords: Design goal; Energy-consumption; Fair scheduling; Hadoop map reduce; Hadoop MapReduce; Map-reduce; Resource management; Scheduling issue; Virtualizations; YARN scheduler
Subjects: A Artificial Intelligence and Data Science > Big Data
C Computer Science and Engineering > Data Science
C Computer Science and Engineering > Cloud Computing
Divisions: Computer Science and Engineering
Depositing User: Users 5 not found.
Date Deposited: 25 Jul 2024 06:50
Last Modified: 14 Aug 2024 08:14
URI: https://ir.psgitech.ac.in/id/eprint/852

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