Research Article
Asymmetric Key-Value Split Pattern Assumption over MapReduce Behavioral Model
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 86 - Issue 10 |
| Published: January 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 },
year = { 2014 },
volume = { 86 },
number = { 10 },
pages = { 30-34 },
doi = { 10.5120/15023-3311 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2014
%A Ravi Prakash G
%A Kiran M
%A Saikat Mukherjee
%T Asymmetric Key-Value Split Pattern Assumption over MapReduce Behavioral Model%T
%J International Journal of Computer Applications
%V 86
%N 10
%P 30-34
%R 10.5120/15023-3311
%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
- 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.
- 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.
- 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.
- 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.
- Berli'nska, J. , Drozdowski, M. : Scheduling divisible MapReduce computations. J. Parallel Distrib. Comput 71(3), 450-459 (2011).
- 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).
- 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.
- 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
Keywords