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Reseach Article

Survey on MapReduce Scheduling Algorithms

by Liya Thomas, Syama R
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 95 - Number 23
Year of Publication: 2014
Authors: Liya Thomas, Syama R

Liya Thomas, Syama R . Survey on MapReduce Scheduling Algorithms. International Journal of Computer Applications. 95, 23 ( June 2014), 9-13. DOI=10.5120/16733-6903

@article{ 10.5120/16733-6903,
author = { Liya Thomas, Syama R },
title = { Survey on MapReduce Scheduling Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 95 },
number = { 23 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 9-13 },
numpages = {9},
url = { },
doi = { 10.5120/16733-6903 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:20:12.042658+05:30
%A Liya Thomas
%A Syama R
%T Survey on MapReduce Scheduling Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 95
%N 23
%P 9-13
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

MapReduce is a programming model used by Google to process large amount of data in a distributed computing environment. It is usually used to perform distributed computing on clusters of computers. Computational processing of data stored on either a file system or a database usually occurs. MapReduce takes the advantage of locality of data, processing data on or near the storage areas, thereby avoiding unnecessary data transmission. The simplicity of the programming model and the automatic handling of node failures hiding the complexity of fault tolerance make MapReduce to be used for both commercial and scientific applications. As MapReduce clusters have become popular these days, their scheduling is one of the important factor which is to be considered. In order to achieve good performance a MapReduce scheduler must avoid unnecessary data transmission. Hence different scheduling algorithms for MapReduce are necessary to provide good performance. This paper provides an overview of four different scheduling algorithms for MapReduce namely; Scheduling algorithm in Hadoop, Longest Approximate Time to End (LATE) MapReduce scheduling algorithm, Self-Adaptive MapReduce(SAMR) scheduling algorithm and Enhanced Self-Adaptive MapReduce scheduling algorithm(ESAMR). An overview of these techniques is provided through this paper. Advantages and disadvantages of these algorithms are identified.

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Index Terms

Computer Science
Information Sciences


MapReduce Programming model Scheduling algorithms