CFP last date
20 June 2024
Call for Paper
July Edition
IJCA solicits high quality original research papers for the upcoming July edition of the journal. The last date of research paper submission is 20 June 2024

Submit your paper
Know more
Reseach Article

Dynamic Capacity Scheduling in Hadoop

by Shivani Thakur, Rupinder Singh, Sugandha Sharma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 125 - Number 15
Year of Publication: 2015
Authors: Shivani Thakur, Rupinder Singh, Sugandha Sharma
10.5120/ijca2015906178

Shivani Thakur, Rupinder Singh, Sugandha Sharma . Dynamic Capacity Scheduling in Hadoop. International Journal of Computer Applications. 125, 15 ( September 2015), 25-28. DOI=10.5120/ijca2015906178

@article{ 10.5120/ijca2015906178,
author = { Shivani Thakur, Rupinder Singh, Sugandha Sharma },
title = { Dynamic Capacity Scheduling in Hadoop },
journal = { International Journal of Computer Applications },
issue_date = { September 2015 },
volume = { 125 },
number = { 15 },
month = { September },
year = { 2015 },
issn = { 0975-8887 },
pages = { 25-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume125/number15/22510-2015906178/ },
doi = { 10.5120/ijca2015906178 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:16:15.594661+05:30
%A Shivani Thakur
%A Rupinder Singh
%A Sugandha Sharma
%T Dynamic Capacity Scheduling in Hadoop
%J International Journal of Computer Applications
%@ 0975-8887
%V 125
%N 15
%P 25-28
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Hadoop mapreduce could be a powerful data processing technique giant for large information analysis on distributed artifact between clusters like clouds. In this paper we proposed the improved capacity scheduler to improve the existing scheduler issues that help the scheduler to execute the task in less time. We introduced pipeline and queue management in our proposed work for improving the performance of hadoop. Our experimental result show that our strategies can result in about 29 to 50 % decrease in average response time.

References
  1. Hadoop, the Apache Software Foundation, May 2012, 1.0.3.
  2. Thusoo,Ashish et al. “Hive : a warehousing solution over map-reduce framework.” Proceedings of the VLDB Endowment 2.2 pp. 1626-1629,2012.
  3. Hadoop: The Definitive Guide, ed. Third Tokyo: yahoo press [as accessed on Jan 2015].
  4. Shvachko, Konstantin, et al. "The Hadoop Distributed File System.”Mass Storage Systems and Technologies, IEEE, 2010.
  5. Nicolae, Bogdan, et al."BlobSeer: Bringing High Throughput Under Heavy Concurrency To Hadoop Map-Reduce Applications.”Parallel & Distributed Processing (IPDPS), IEEE, 2010.
  6. Xu, Guanghui, Feng Xu, and Hongxu Ma.”Deploying and Researching Hadoop in Virtual Machines.”IEEE, 2012.
  7. Kurazumi, Shiori, et al. "Dynamic Processing Slot Scheduling For I/O.” ICNC. 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Mapreduce HDFS Hadoop Scheduler.