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https://ptsldigital.ukm.my/jspui/handle/123456789/476120
Title: | Large-scale data processing using MapReduce in cloud computing |
Authors: | Samira Daneshyar (P53691) |
Supervisor: | Ahmed Moosajee Patel, Prof. Dr. |
Keywords: | Large-scale data processing MapReduce Cloud computing |
Issue Date: | 16-Nov-2012 |
Description: | The computer industry is being challenged to develop methods and techniques to process large datasets at optimum response times and minimum cost. The technical challenges in dealing with the increasing demand to handle vast quantities of data is daunting and on the rise. The objectives of this research are (i) to propose, define and develop a framework for large-scale data processing in order to run in a cloud computing environment; (ii) To demonstrate the effectiveness of the framework to perform automatic parallelization and distribution of computation over several processors. This study uses a new technique called MapReduce programming model to rapidly process large amount of data in parallel and in distributed fashion. This is achieved through using a combination of this model and cloud computing to perform parallel and distributed processing of large datasets over a cluster of machines. A MapReduce program is developed and prototyped in order to run and exercised in both standalone and cloud computing modes to show efficiency of using MapReduce and cloud computing to process data in parallel across a distributed cluster of machines. Finally, the result from experimental operation shows system performance of running the prototyped MapReduce program in cloud computing is better than in stand-alone mode with respect to the speed of processing, dataset storage usage, response time and cost efficiency.,Master/Sarjana |
Pages: | 109 |
Call Number: | QA76.585.D344 2012 3 |
Publisher: | UKM, Bangi |
Appears in Collections: | Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat |
Files in This Item:
File | Description | Size | Format | |
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ukmvital_73416+Source01+Source010.PDF Restricted Access | 1.36 MB | Adobe PDF | View/Open |
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