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Accelerating Hadoop Map-Reduce for small/intermediate data sizes using the Comet coordination framework

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Text
TitleInfo (ID = T-1)
Title
Accelerating Hadoop Map-Reduce for small/intermediate data sizes using the Comet coordination framework
SubTitle
PartName
PartNumber
NonSort
Identifier (displayLabel = ); (invalid = )
ETD_1975
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051790
Language (objectPart = )
LanguageTerm (authority = ISO639-2); (type = code)
eng
Genre (authority = marcgt)
theses
Subject (ID = SBJ-1); (authority = RUETD)
Topic
Electrical and Computer Engineering
Subject (ID = SBJ-2); (authority = ETD-LCSH)
Topic
Electronic data processing--Distributed processing
Subject (ID = SBJ-3); (authority = ETD-LCSH)
Topic
File organization (Computer science)
Abstract
MapReduce has been emerging as a popular programming paradigm for data intensive computing in clustered environments. MapReduce as a framework for solving embarrassingly parallel problems has been extensively used on large clusters. These frameworks support ease of computation for petabytes of data mostly through the use of a distributed file system example the Google File System – used by the proprietary ‘Google Map-Reduce’.
In the "Map", the master node takes the input, divides it into smaller sub-problems, and distributes those to worker nodes. The worker node processes that smaller problem, and passes the answer back to its master node. In the "Reduce", the master node then takes the answers of the sub-problems and combines them to get the final output after reduces. The advantage of MapReduce is that, it allows for distributed processing of the map and reduction operations, assuming each operation is independent of the other, all can be executed in parallel.
We found that file writes and reads to the distributed file system, have an overhead especially for smaller data sizes of the order of few tens of GB’s. Our solution provides the MapReduce framework built over Comet framework utilizing TCP sockets for communication and coordination and uses in-memory operations for data whenever possible. The objective of this thesis is to
(1) understand the behaviors and limitations of MapReduce in the case of small-moderate datasets
(2) develop coordination and interaction framework to complement MapReduce-Hadoop to address these shortcomings
(3) demonstrate and evaluate using a real world application
In this thesis we use Comet and its services to build a MapReduce infrastructure that address the above requirements - specifically enable pull based scheduling of Map tasks as well as stream based coordination and data exchange. The framework is based on the master-worker concept. Comet is a decentralized (peer-to-peer) computational infrastructure that supports applications having high computational requirement.
Our System’s interfaces are similar to the Hadoop MapReduce framework, to make applications built on Hadoop easily portable to Comet-based framework. The details of the implementation and evaluation of an actual pharmaceutical problem, with its results have been described. We found that out solution can be used to accelerate the computations of medium sized data by delaying or avoiding the use of distributed file reads and writes.
PhysicalDescription
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electronic resource
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vii, 59 p. : ill.
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application/pdf
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text/xml
Note (type = degree)
M.S.
Note (type = bibliography)
Includes bibliographical references (p. 58-59)
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by Shivangi Chaudhari
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Chaudhari
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Shivangi
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1982
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author
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Shivangi Chaudhari
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Parashar
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Manish
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chair
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Advisory Committee
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Manish Parashar
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Gajic
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Zoran
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internal member
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Advisory Committee
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Zoran Gajic
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Pompili
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Dario
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internal member
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Advisory Committee
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Dario Pompili
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Rutgers University
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degree grantor
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Graduate School - New Brunswick
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school
OriginInfo
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2009
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2009-10
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xx
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3M045NS
Genre (authority = ExL-Esploro)
ETD graduate
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RightsDeclaration (AUTHORITY = GS); (ID = rulibRdec0006)
The author owns the copyright to this work
Copyright
Status
Copyright protected
Notice
Note
Availability
Status
Open
Reason
Permission or license
Note
RightsHolder (ID = PRH-1); (type = personal)
Name
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Chaudhari
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Shivangi
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Copyright holder
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DateTime
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Name
Shivangi Chaudhari
Affiliation
Rutgers University. Graduate School - New Brunswick
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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