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2015
ACM

ApproxHadoop: Bringing Approximations to MapReduce Frameworks

3 years 11 months ago
ApproxHadoop: Bringing Approximations to MapReduce Frameworks
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 runtim...
Iñigo Goiri, Ricardo Bianchini, Santosh Nag
Added 16 Apr 2016
Updated 16 Apr 2016
Type Journal
Year 2015
Where ASPLOS
Authors Iñigo Goiri, Ricardo Bianchini, Santosh Nagarakatte, Thu D. Nguyen
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