A REVIEW OF ADAPTIVE REPLICATION MANAGEMENT IN HDFS BASED ON SUPERVISED LEARNING

Authors

  • Vaishali pandey (Department of SoI,T RGPV ,BHOPAL,INDIA)
  • Varsha Sharma (Department of SoIT, RGPV ,BHOPAL,INDIA)

Keywords:

Replication, HDFS, Proactive Prediction, Optimization, Bayesian Learning, Gaussian Process

Abstract

Apache Hadoop is very popular now-a-days. Hadoop Distributed file system (HDFS) is that the Heart of
Apache Hadoop that is reliable and extremely accessible. At the heart of Apache Hadoop, the Hadoop Distributed file
system (HDFS) provides the dependability and high availability for computation by applying a static replication by
default. This paper explains the dynamic approach to replicate data Files supported supervised Learning. Hadoop, an
open source implementation of the MapReduce framework, has been wide used for process massive-scale information in
parallel. Since Hadoop uses a distributed file system, referred to as HDFS, the data locality drawback usually happens)
and this drawback leads to the decrease in performance.

Downloads

Published

2018-04-25

How to Cite

Vaishali pandey, & Varsha Sharma. (2018). A REVIEW OF ADAPTIVE REPLICATION MANAGEMENT IN HDFS BASED ON SUPERVISED LEARNING. International Journal of Advance Engineering and Research Development (IJAERD), 5(4), 570–576. Retrieved from https://www.ijaerd.org/index.php/IJAERD/article/view/3098

Issue

Vol. 5 No. 4 (2018): Volume 5 Issue 4 April 2018

Section

Articles