A Survey on Accelerated Mapreduce for Hadoop-Reference-Cited by-同舟云学术

A Survey on Accelerated Mapreduce for Hadoop

Published:2017-07-03 Issue:3 Volume:10 Page:597-602
ISSN:0974-6471
Container-title:Oriental journal of computer science and technology
language:
Short-container-title:Orient. J. Chem

Author:

Tiwari Jyotindra¹,Pawar Dr. Mahesh²,Pandey Dr. Anjajana¹

Affiliation:

1. School of Information Technology, RGPV, Airport Bypass Road, Gandhi Nagar, Bhopal, Madhya Pradesh 462036, India.

2. Department of IT, UIT RGPV, Airport Bypass Road, Gandhi Nagar, Bhopal, Madhya Pradesh 462036, India.

Abstract

Big Data is defined by 3Vs which stands for variety, volume and velocity. The volume of data is very huge, data exists in variety of file types and data grows very rapidly. Big data storage and processing has always been a big issue. Big data has become even more challenging to handle these days. To handle big data high performance techniques have been introduced. Several frameworks like Apache Hadoop has been introduced to process big data. Apache Hadoop provides map/reduce to process big data. But this map/reduce can be further accelerated. In this paper a survey has been performed for map/reduce acceleration and energy efficient computation in quick time.

Publisher

Oriental Scientific Publishing Company

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference19 articles.

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4. CrossRef

5. J. Zhu, Li Juanjuan, E. Hardesty, H. Jiang and L. Kuan-Ching. “GPU-in-Hadoop: Enabling MapReduce across distributed heterogeneous platforms”, IEEE/ACIS 13th International Conference of Computer and Information Science (ICIS). 2014:321-326.