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Sensors 2009, 9(7), 5339-5350; doi:10.3390/s90705339

Metaheuristic Based Scheduling Meta-Tasks in Distributed Heterogeneous Computing Systems

1
Islamic Azad University, Ramsar Branch, Ramsar, Iran
2
Machine Intelligence Research Labs (MIR Labs), Auburn, Washington 98071-2259, USA
3
Norwegian Center of Excellence, Center of Excellence for Quantifiable Quality of Service, Norwegian University of Science and Technology, O.S. Bragstads plass 2E, N-7491 Trondheim, Norway
4
Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava, Czech Republic
*
Author to whom correspondence should be addressed.
Received: 27 April 2009 / Revised: 3 July 2009 / Accepted: 6 July 2009 / Published: 7 July 2009
(This article belongs to the Section Chemical Sensors)
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Abstract

Scheduling is a key problem in distributed heterogeneous computing systems in order to benefit from the large computing capacity of such systems and is an NP-complete problem. In this paper, we present a metaheuristic technique, namely the Particle Swarm Optimization (PSO) algorithm, for this problem. PSO is a population-based search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles fly in problem search space to find optimal or near-optimal solutions. The scheduler aims at minimizing makespan, which is the time when finishes the latest task. Experimental studies show that the proposed method is more efficient and surpasses those of reported PSO and GA approaches for this problem. View Full-Text
Keywords: distributed heterogeneous computing systems; particle swarm optimization; scheduling distributed heterogeneous computing systems; particle swarm optimization; scheduling
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Izakian, H.; Abraham, A.; Snášel, V. Metaheuristic Based Scheduling Meta-Tasks in Distributed Heterogeneous Computing Systems. Sensors 2009, 9, 5339-5350.

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