A review of research on MapReduce scheduling algorithms in Hadoop

Author

Singh, Namrata ; Agrawal, Sanjay

Author_Institution

Dept. of Comput. Eng. & Applic., Nat. Inst. of Tech. Teachers´ Training & Res., Bhopal, India

fYear

2015

fDate

15-16 May 2015

Firstpage

637

Lastpage

642

Abstract

Big data has created an era of tera where bulk volume of data is being collected at escalating rates. Due to increase in storage capacities, processing power and availability of data, the size of global data is growing in zeta-bytes. Hadoop is one of the technologies in the big data landscape for analyzing the data through Hadoop Distributed File System and Map-Reduce. Job scheduling is an important activity for efficient management of cluster resources. Hadoop schedulers are pluggable components which assign resources to jobs. In a variety of schedulers, prominent are the default FIFO, Fair and Capacity schedulers. In this paper, a comprehensive survey of the various job scheduling algorithms has been performed. Also their comparative parametric analysis has been carried out by emphasizing the common key points in these schedulers.

Keywords

Big Data; parallel processing; resource allocation; scheduling; workstation clusters; FIFO scheduler; Hadoop distributed file system; MapReduce scheduling algorithms; big data; capacity scheduler; cluster resource management; data analysis; fair scheduler; job scheduling algorithms; processing power; storage capacities; Algorithm design and analysis; Computational modeling; Cooling; Scheduling; Scheduling algorithms; Time factors; Big Data; HDFS; Hadoop; Job Scheduling; JobTracker; MapReduce; TaskTracker;

fLanguage

English

Publisher

ieee

Conference_Titel

Computing, Communication & Automation (ICCCA), 2015 International Conference on

Conference_Location

Noida

Print_ISBN

978-1-4799-8889-1

Type

conf

DOI

10.1109/CCAA.2015.7148451

Filename

7148451