Reference : Multi-objective Robust Static Mapping of Independent Tasks on Grids

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/16700

Title :	Multi-objective Robust Static Mapping of Independent Tasks on Grids
Language :	English
Author, co-author :	Dorronsoro, Bernabé [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Cañero, J. Alberto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Maciejewski, Anthony A. [Colorado State University]
	Siegel, Howard Jay [Colorado State University]
Publication date :	2010
Main document title :	World Conference in Computational Intelligence (WCCI)
Publisher :	IEEE
Pages :	3389-3396
Peer reviewed :	Yes
On invitation :	No
Audience :	International
ISBN :	978-1-4244-8126-2
Event name :	World Conference in Computational Intelligence (WCCI)
Event date :	July 2010
Event place (city) :	Barcelona
Event country :	Spain
Abstract :	[en] We study the problem of efficiently allocating incoming independent tasks onto the resources of a Grid system. Typically, it is assumed that the estimated time to compute each task on every machine is known. We are making the same assumption in this work, but we allow the existence of inaccuracies in these values. Our schedule will be robust versus such inaccuracies, ensuring that even when the estimated time to compute all the tasks is increased by a given percentage, the makespan of the schedule (i.e., the time when the last machine finishes its tasks) will not grow behind that percentage. We propose a new multi-objective definition of the problem, optimizing at the same time the makespan of the schedule and its robustness. Four well-known multi-objective evolutionary algorithms are used to find competitive results to the new problem. Finally, a new population initialization method for scheduling problems is proposed, leading to more efficient and accurate algorithms.
Research centres :	University of Luxembourg: High Performance Computing - ULHPC
Permalink :	http://hdl.handle.net/10993/16700
Commentary :	Proceedings of the IEEE Congress on Evolutionary Computation (CEC), part of World Conference in Computational Intelligence (WCCI)

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