Year

2015

Season

Spring

Paper Type

Master's Thesis

College

College of Computing, Engineering & Construction

Degree Name

Master of Science in Computer and Information Sciences (MS)

Department

Computing

NACO controlled Corporate Body

University of North Florida. School of Computing

Committee Chairperson

Dr. Sanjay P. Ahuja

Second Advisor

Dr. Roger Eggen

Rights Statement

http://rightsstatements.org/vocab/InC/1.0/

Third Advisor

Dr. Sandeep Reddivari

Fourth Advisor

Dr. Asai Asaithambi

Fifth Advisor

Dr. Ching-Hua Chuan

Department Chair

Dr. Roger Eggen

College Dean

Dr. Mark A. Tumeo

Abstract

Cloud computing is a relatively new form of computing which uses virtualized resources. It is dynamically scalable and is often provided as pay for use service over the Internet or Intranet or both. With increasing demand for data storage in the cloud, the study of data-intensive applications is becoming a primary focus. Data intensive applications are those which involve high CPU usage, processing large volumes of data typically in size of hundreds of gigabytes, terabytes or petabytes. The research in this thesis is focused on the Amazon’s Elastic Cloud Compute (EC2) and Amazon Elastic Map Reduce (EMR) using HiBench Hadoop Benchmark suite. HiBench is a Hadoop benchmark suite and is used for performing and evaluating Hadoop based data intensive computation on both these cloud platforms. Both quantitative and qualitative comparisons of Amazon EC2 and Amazon EMR are presented. Also presented are their pricing models and suggestions for future research.

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