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Reseach Article

Asymmetric Key-Value Split Pattern Assumption over MapReduce Behavioral Model

by Ravi Prakash G, Kiran M, Saikat Mukherjee
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 86 - Number 10
Year of Publication: 2014
Authors: Ravi Prakash G, Kiran M, Saikat Mukherjee
10.5120/15023-3311

Ravi Prakash G, Kiran M, Saikat Mukherjee . Asymmetric Key-Value Split Pattern Assumption over MapReduce Behavioral Model. International Journal of Computer Applications. 86, 10 ( January 2014), 30-34. DOI=10.5120/15023-3311

@article{ 10.5120/15023-3311,
author = { Ravi Prakash G, Kiran M, Saikat Mukherjee },
title = { Asymmetric Key-Value Split Pattern Assumption over MapReduce Behavioral Model },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 86 },
number = { 10 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 30-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume86/number10/15023-3311/ },
doi = { 10.5120/15023-3311 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:03:52.558958+05:30
%A Ravi Prakash G
%A Kiran M
%A Saikat Mukherjee
%T Asymmetric Key-Value Split Pattern Assumption over MapReduce Behavioral Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 86
%N 10
%P 30-34
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Actual Quantifiability is a concept in MapReduce that is based on two assumptions: (1) every mapper is cautious, i. e. , does not exclude any reducer's key-value split pattern choice from consideration, and (2) every mapper respects the reducer's key-value split pattern preferences, i. e. , deems one reducer's key-value split pattern choice to be infinitely more likely than another whenever it premises the reducer to prefer the one to the other. In this paper we provide a new approach for actual quantifiability, by assuming that mappers have asymmetric key-value split pattern about the reducer's key-value utilities. We show that, if the uncertainty of each mapper about the reducer's key-value utilities vanishes gradually in some regular manner, then the key-value split pattern choices it can quantifiably make under common conjecture in quantifiability are all actually quantifiable in the original MapReduce with no uncertainty about the reducer's utilities.

References
  1. Kiran M. , Saikat Mukherjee and Ravi Prakash G. , Characterization of Randomized Shuffle and Sort Quantifiability in MapReduce Model, International Journal of Computer Applications, 51-58, Volume 79, No. 5, October 2013.
  2. Amresh Kumar, Kiran M. , Saikat Mukherjee and Ravi Prakash G. , Verification and Validation of MapReduce Program model for Parallel K-Means algorithm on Hadoop Cluster, International Journal of Computer Applications, 48-55, Volume 72, No. 8, June 2013.
  3. Kiran M. , Amresh Kumar, Saikat Mukherjee and Ravi Prakash G. , Verification and Validation of MapReduce Program Model for Parallel Support Vector Machine Algorithm on Hadoop Cluster, International Journal of Computer Science Issues, 317-325, Vol. 10, Issue 3, No. 1, May 2013.
  4. Aniruddha Basak, Irina Brinster and Ole J. Mengshoel. MapReduce for Bayesian Network Parameter Learning using the EM Algorithm, Proc. of Big Learning: Algorithms, Systems and Tools, 1-6, December 2012.
  5. Berli'nska, J. , Drozdowski, M. : Scheduling divisible MapReduce computations. J. Parallel Distrib. Comput 71(3), 450-459 (2011).
  6. Emanuel Vianna, Giovanni Comarela, Tatiana Pontes, Jussara Almeida, Virgilio Almeida, Kevin Wilkinson, Harumi Kuno, Umeshwar Dayal. Analytical Performance Models for MapReduce Workloads, Int J Parallel Prog 41:495-525 (2013).
  7. Erik B. Reed and Ole J. Mengshoel. Scaling Bayesian Network Parameter Learning with Expectation Maximization using MapReduce, Proc. of Big Learning: Algorithms, Systems and Tools, 1-5, December 2012.
  8. Ravi Prakash G, Kiran M and Saikat Mukherjee, On Randomized Preference Limitation Protocol for Quantifiable Shuffle and Sort Behavioral Implications in MapReduce Programming Model, Parallel & Cloud Computing, Vol. 3, Issue 1, Pages 1-14, January 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Mapper reducer key-value asymmetric split pattern utilities MapReduce behavioral actual quantifiability