Kashyap Barua

Work place: School of Computer Engineering, KIIT University, Bhubaneswar, India

E-mail: kashyapbarua@gmail.com


Research Interests: Computational Science and Engineering, Data Structures and Algorithms


Kashyap Barua was born on the 26th of November, 1994 in Assam. He is pursuing B.Tech degree in Computer Science & Engineering from KIIT University, Bhubaneswar, India. His field of research includes Big Data Analytics, Data Science and Machine Learning.
He has worked as a software developer intern at Zaloni, Guwahati. He is also the student coordinator of EMC2 Academic Alliance at KIIT University. His paper on “A Proposal for Shelf Placement Optimization for Retail Industry using Big Data Analytics” was accepted at Data Science Congress 2017. He has also had his article on “Trends in Big Data” published in CSI Communications.
Kashyap Barua is the member of the IET (The Institution of Engineering and Technology).

Author Articles
A Proposal for High Availability of HDFS Architecture based on Threshold Limit and Saturation Limit of the Namenode

By Sabyasachi Chakraborty Kashyap Barua Manjusha Pandey Siddharth Rautaray

DOI: https://doi.org/10.5815/ijieeb.2017.06.04, Pub. Date: 8 Nov. 2017

Big Data which is one of the newest technologies in the present field of science and technology has created an enormous drift of technology to a salient data architecture. The next thing that comes right after big data is Hadoop which has motivated the complete Big Data Environment to its jurisdiction and has reinforced the complete storage and analysis of big data. This paper discusses a hierarchical architecture of Hadoop Nodes namely Namenodes and Datanodes for maintaining a High Availability Hadoop Distributed File System. The High Availability Hadoop Distributed File System architecture establishes itself onto the two fundamental model of Hadoop that is Master-Slave Architecture and elimination of single point node failure. The architecture will be of such utilization that there will be an optimum load on the data nodes and moreover there will be no loss of any data in comparison to the size of data.

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