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A Survey on Enhanced MapReduce Spatial Hadoop in Cloud Computing

N.Vetrivelan 1 , C.Jasmin Selvi2

Section:Survey Paper, Product Type: Journal Paper
Volume-4 , Issue-4 , Page no. 266-271, Apr-2016

Online published on Apr 27, 2016

Copyright © N.Vetrivelan, C.Jasmin Selvi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: N.Vetrivelan, C.Jasmin Selvi, “A Survey on Enhanced MapReduce Spatial Hadoop in Cloud Computing,” International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.266-271, 2016.

MLA Style Citation: N.Vetrivelan, C.Jasmin Selvi "A Survey on Enhanced MapReduce Spatial Hadoop in Cloud Computing." International Journal of Computer Sciences and Engineering 4.4 (2016): 266-271.

APA Style Citation: N.Vetrivelan, C.Jasmin Selvi, (2016). A Survey on Enhanced MapReduce Spatial Hadoop in Cloud Computing. International Journal of Computer Sciences and Engineering, 4(4), 266-271.

BibTex Style Citation:
@article{Selvi_2016,
author = {N.Vetrivelan, C.Jasmin Selvi},
title = {A Survey on Enhanced MapReduce Spatial Hadoop in Cloud Computing},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2016},
volume = {4},
Issue = {4},
month = {4},
year = {2016},
issn = {2347-2693},
pages = {266-271},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=931},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=931
TI - A Survey on Enhanced MapReduce Spatial Hadoop in Cloud Computing
T2 - International Journal of Computer Sciences and Engineering
AU - N.Vetrivelan, C.Jasmin Selvi
PY - 2016
DA - 2016/04/27
PB - IJCSE, Indore, INDIA
SP - 266-271
IS - 4
VL - 4
SN - 2347-2693
ER -

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Abstract

Cloud Computing is creating as a new computational worldview shift. Hadoop-MapReduce has become a powerful Calculation Model alternately handling huge information on Dispersed thing equipment groups such as Clouds. In all Hadoop implementations, the shortcoming FIFO scheduler is accessible where employments are booked in FIFO request with support alternately other Need based schedulers also. In this paper we study distinctive scheduler changes conceivable with Hadoop and too given some guidelines on how to improve the Planning in Hadoop in Cloud Environments.

Key-Words / Index Term

Cloud Computing, Hadoop, HDFS, MapReduce

References

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[2] Ankita Kadre and S.R Yadav, "A Survey on Map Reduce Algorithm for Big data analysis using Hadoop, Pig and Hive utility tools", International Journal of Computer Sciences and Engineering, Volume-03, Issue-10, Page No (52-57), Oct -2015
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[4] Mantripatjit Kaur and Gurleen Kaur Dhaliwal, "Performance Comparison of Map Reduce and Apache Spark on Hadoop for Big Data Analysis", International Journal of Computer Sciences and Engineering, Volume-03, Issue-11, Page No (66-69), Nov -2015
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