dc.contributor.author | Izakian, Hesam | |
dc.contributor.author | Abraham, Ajith | |
dc.contributor.author | Snasel, Vaclav | |
dc.date.accessioned | 2015-09-30T08:18:48Z | |
dc.date.accessioned | 2015-11-18T14:58:44Z | |
dc.date.available | 2015-09-30T08:18:48Z | |
dc.date.available | 2015-11-18T14:58:44Z | |
dc.date.issued | 2009 | |
dc.identifier.citation | Sensors 2009, 9(7):5339-5350 | nb_NO |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/11250/2364631 | |
dc.description.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. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | MDPI | nb_NO |
dc.title | Metaheuristic Based Scheduling Meta-Tasks in Distributed Heterogeneous Computing Systems | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | en_GB |
dc.date.updated | 2015-09-30T08:18:48Z | |
dc.source.pagenumber | 5339-5350 | nb_NO |
dc.source.volume | 9 | nb_NO |
dc.source.journal | Sensors | nb_NO |
dc.source.issue | 7 | nb_NO |
dc.identifier.doi | 10.3390/s90705339 | |
dc.identifier.cristin | 351702 | |
dc.description.localcode | This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | nb_NO |