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10.17148/IARJSET.202 0.7318.pdf

IARJSET.2020.7318-1.pdf

🔗 Source: iarjset.com

📊 Size: 527 KB

👤 Author: Computer-PC

⬇️ Downloads: 83

Summary

A survey on job schedulers of MapReduce is presented, focusing on big data analysis and Hadoop, an open-source implementation of the MapReduce framework. The paper discusses the importance of efficient data utilization, the role of MapReduce in large-scale data-parallel applications, and the architecture of Hadoop, including HDFS and MapReduce. Various job schedulers of MapReduce are surveyed, including LATE (Longest Approximate Time to End), which is designed to improve the responsiveness of jobs. The paper aims to provide an overview of different job schedulers and their characteristics, highlighting the need for efficient scheduling algorithms in big data processing. Keywords: Cloud Computing, HDFS, MapReduce, Hadoop.

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Description

A survey on job schedulers of MapReduce is presented, focusing on big data analysis and Hadoop, an open-source implementation of the MapReduce framework.

Technical Information

File Format: PDF
File Size: 527 KB
Pages: 7
Language: EN
Author: Computer-PC
Total Downloads: 83
Last Updated: 2 days ago

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