Persistent Link:
http://hdl.handle.net/10150/244396
Title:
Optimizing MongoDB-Hadoop Performance with Record Grouping
Author:
Justice, Matthew Adam
Issue Date:
May-2012
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Computational cloud computing is more important than ever. Since time is literally money on cloud platforms, performance is the primary focus of researchers and programmers alike. Although distributed computing platforms today do a fine job of optimizing most types of workflows, there are some types, specifically those which are not computation-oriented, that are left out. After introducing important players in the world of computational cloud computing, this paper explores a possible performance enhancement for these types of workflows by reducing the overhead that platform designers assumed was acceptable. The enhancement is tested in two environments: an actual distributed computing platform and an environment that simulates that platform. Along the way it becomes clear that computational cloud computing is far from perfect and its use can often deliver surprising results. Regardless, the presented solution remains viable and is capable of increasing the performance of particular types of jobs by up to twenty percent.
Type:
text; Electronic Thesis
Degree Name:
B.S.
Degree Level:
bachelors
Degree Program:
Honors College; Computer Science
Degree Grantor:
University of Arizona

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleOptimizing MongoDB-Hadoop Performance with Record Groupingen_US
dc.creatorJustice, Matthew Adamen_US
dc.contributor.authorJustice, Matthew Adamen_US
dc.date.issued2012-05-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractComputational cloud computing is more important than ever. Since time is literally money on cloud platforms, performance is the primary focus of researchers and programmers alike. Although distributed computing platforms today do a fine job of optimizing most types of workflows, there are some types, specifically those which are not computation-oriented, that are left out. After introducing important players in the world of computational cloud computing, this paper explores a possible performance enhancement for these types of workflows by reducing the overhead that platform designers assumed was acceptable. The enhancement is tested in two environments: an actual distributed computing platform and an environment that simulates that platform. Along the way it becomes clear that computational cloud computing is far from perfect and its use can often deliver surprising results. Regardless, the presented solution remains viable and is capable of increasing the performance of particular types of jobs by up to twenty percent.en_US
dc.typetexten_US
dc.typeElectronic Thesisen_US
thesis.degree.nameB.S.en_US
thesis.degree.levelbachelorsen_US
thesis.degree.disciplineHonors Collegeen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorUniversity of Arizonaen_US
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