Reference : Scalable, Low complexity, and fast greedy scheduling heuristics for highly heterogene...

	Document type :	Scientific journals : Article
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/17824

Title :	Scalable, Low complexity, and fast greedy scheduling heuristics for highly heterogeneous distributed computing systems
Language :	English
Author, co-author :	Diaz, Cesar [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Pecero, Johnatan [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Bouvry, Pascal [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	22-Oct-2013
Journal title :	Journal of Supercomputing
Publisher :	Springer Science & Business Media B.V.
Peer reviewed :	Yes (verified by ORBilu)
Audience :	International
ISSN :	0920-8542
Keywords :	[en] Scalability ; Scheduling ; Heterogeneous computing systems
Abstract :	[en] Forheterogeneousdistributedcomputingsystems,importantdesignissues are scalability and system optimization. Given such systems, it is crucial to develop low computational complexity algorithms to schedule tasks in a manner that exploits the heterogeneity of the resources and applications. In this paper, we report and evalu- ate three scalable, and fast scheduling heuristics for highly heterogeneous distributed computing systems. We conduct a comprehensive performance evaluation study us- ing simulation. The benchmarking outlines the performance of the schedulers, rep- resenting scalability, makespan, flowtime, computational complexity, and memory utilization. The set of experimental results shows that our heuristics perform as good as the traditional approaches, for makespan and flowtime, while featuring lower com- plexity, lower running time, and lower used memory. The experimental results also detail the various scenarios under which certain algorithms excel and fail.
Target :	Researchers
Permalink :	http://hdl.handle.net/10993/17824
DOI :	10.1007/s11227-013-1038-0

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