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