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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YARN Schedulers for Hadoop MapReduce Jobs Design Goals, Issues and Taxonomy.pdf - Published Version
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YARN Schedulers for Hadoop MapReduce Jobs Design Goals, Issues and Taxonomy.pdf - Published Version
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Official URL: https://doi.org/10.2174/2666255816666220831125012
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 |
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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 |