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Genetic algorithm in finding Pareto frontier of optimizing data transfer versus job execution in grids
Author(s) -
Taheri Javid,
Zomaya Albert Y.,
Khan Samee U.
Publication year - 2012
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.2960
Subject(s) - pareto principle , computer science , multi objective optimization , frontier , scheduling (production processes) , genetic algorithm , mathematical optimization , benchmark (surveying) , pareto optimal , job shop scheduling , algorithm , mathematics , machine learning , operating system , schedule , archaeology , geodesy , history , geography
Summary This work presents a genetic algorithm (GA)‐based optimization technique, called GA‐ParFnt, to find the Pareto frontier for optimizing data transfer versus job execution time in grids. As the performance of a generic GA is not suitable to find such Pareto relationship, major modifications are applied to it so that it can efficiently discover such relationship. The frontier curve representing this relationship is then matched against performance of several scheduling techniques—for both data intensive and computationally intensive applications—to measure their overall performances. Results show that few of these algorithms are far from the Pareto front despite their claims of being efficient in optimizing their targeted objectives. Results also provide invaluable insights into this formidable problem and should aid in the design of future schedulers. Copyright © 2012 John Wiley & Sons, Ltd.