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ApproxHadoop: Bringing Approximations to MapReduce Frameworks

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Language
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English
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technical report
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application/pdf
Extent
18 p.
Note (type = special display note)
Technical report DCS-TR-709
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School of Arts and Sciences (SAS) (New Brunswick)
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Computer Science (New Brunswick)
Name (type = personal)
NamePart (type = family)
Goiri
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Inigo
Affiliation
Microsoft Research
Role
RoleTerm (authority = marcrt); (type = text)
author
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NamePart (type = family)
Bianchini
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Ricardo
Description
Computer Science (New Brunswick)
Role
RoleTerm (authority = marcrt); (type = text)
author
Name (type = personal)
NamePart (type = family)
Santosh
NamePart (type = given)
Nagarakatte
Description
Computer Science (New Brunswick)
Role
RoleTerm (authority = marcrt); (type = text)
author
Name (type = personal)
NamePart (type = family)
Nguyen
NamePart (type = given)
Thu
Description
Computer Science (New Brunswick)
Role
RoleTerm (authority = marcrt); (type = text)
author
TitleInfo
Title
ApproxHadoop: Bringing Approximations to MapReduce Frameworks
Abstract (type = abstract)
Research has shown that approximate computing is effective at reducing the resource requirements, computation time, and/or energy consumption of large-scale computing. In this paper, we We propose and evaluate a framework for creating and running approximation-enabled MapReduce programs. Specifically, we propose approximation mechanisms that fit naturally into the MapReduce paradigm, including input data sampling, task dropping, and accepting and running a precise and a user-defined approximate version of the MapReduce code. We then show how to leverage statistical theories to compute error bounds for popular classes of MapReduce programs when approximating with input data sampling and/or task dropping. We implement the proposed mechanisms and error bound estimations in a prototype system called ApproxHadoop. Our evaluation uses MapReduce applications from different domains, including data analytics, scientific computing, video encoding, and machine learning. Our results show that ApproxHadoop can significantly reduce application execution time and/or energy consumption when the user is willing to tolerate small errors. For example, ApproxHadoop can reduce runtimes by up to 32× when the user can tolerate an error of 1% with 95% confidence. We conclude that our framework and system can make approximation easily accessible to many application domains using the MapReduce model.
OriginInfo
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2017-12
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Text
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Title
Computer Science (New Brunswick)
Identifier (type = local)
rucore21032500001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3CJ8J0P
RelatedItem (type = host)
TitleInfo
Title
Nguyen, Thu
Identifier (type = local)
rucore30246400001
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Open
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Technical

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Document
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1.5
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pdfTeX-1.40.15
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2015-06-27T11:57:08
DateCreated (point = end); (encoding = w3cdtf); (qualifier = exact)
2015-06-27T11:57:08
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