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dc.date.accessioned 2012-11-09T12:15:42Z
dc.date.available 2012-11-09T12:15:42Z
dc.date.issued 1997
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/23931
dc.description.abstract We are given a nite set of jobs of equal processing times with readiness times and tails and a set of identical processors. The aim is to schedule the given set of jobs on the given set of processors to minimize the total processing time (or makespan). An algorithm for that problem with the time complexity O(n logn) was proposed earlier in [10]. This algorithm improves the running time of the previously known best algorithm [9] under the assumption that the tails of all jobs are bounded by some constant. In this paper we show that an algorithm based on the ideas of the algorithm from [10] can be constructed in which the above restriction is removed. en
dc.language en es
dc.subject Scheduling es
dc.subject identical machines en
dc.subject Algorithms es
dc.subject readiness time en
dc.subject tail en
dc.subject Parallel processing es
dc.subject Distributed es
dc.subject computational complexity en
dc.title A job allocation algorithm for parallel processors en
dc.type Objeto de conferencia es
sedici.creator.person Vakhania, Nodari es
sedici.description.note Eje: Procesamiento distribuido y paralelo. Tratamiento de señales es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Red de Universidades con Carreras en Informática (RedUNCI) es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
sedici.date.exposure 1997
sedici.relation.event III Congreso Argentino de Ciencias de la Computación es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)