Task Selection for Scheduling using Hadoop Scheduler
D C Vinutha1, G T Raju2
1D C Vinutha, Associate Professor, Department of ISE, Vidyavardhaka College of Engineering, Mysuru (Karnataka), India.
2G T Raju, Vice-Principal, Professor & Head, Department of Computer Science & Engineering, RNS Institute of Technology, Bengaluru (Karnataka), India.
Manuscript received on 09 December 2019 | Revised Manuscript received on 17 December 2019 | Manuscript Published on 31 December 2019 | PP: 708-710 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10201292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1020.1292S19
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: MapReduce is a prevalent model for data intensive applications. This covers the difficulties of parallel programming and provides an abstract environment. Hadoop is a benchmark for Big Data storage by being able to provide load balancing, scalable and fault tolerance operation. Hadoop output is mainly dependent on scheduler. Various algorithms for scheduling [6-10]have been suggested for various types of environments, applications and workload. In this work new task selection method is developed to facilitate the scheduler, if a node has several local tasks. Experimental result shows an improvement of 20% in respect of locality and fairness.
Keywords: Map Reduce, Hadoop Fair Scheduler, LATE.
Scope of the Article: Data Visualization using IoT