Anytime scheduling with neural networks
Identifieur interne : 000D32 ( PascalFrancis/Checkpoint ); précédent : 000D31; suivant : 000D33Anytime scheduling with neural networks
Auteurs : J.-M. Gallone [France] ; F. Charpillet [France] ; F. Alexandre [France]Source :
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Abstract
Scheduling techniques have been intensively studied by several research communities and have been applied to a wide range of applications in computer and manufacturing environments. In computer systems, scheduling is an important approach to address real-time constraints associated with a set of computing tasks to be executed on one or several computers. Most of the scheduling problems are NP-Hard, which is why heuristic and approximation algorithms must be used for large problems. Obviously these methods are of interest when they provide near optimal solutions with a polynomial computational complexity. This paper presents results for scheduling a set of non preemptive tasks by using a Hopfield neural network model. We present in this paper how this approach can solve scheduling problems following the "anytime" paradigm.
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In computer systems, scheduling is an important approach to address real-time constraints associated with a set of computing tasks to be executed on one or several computers. Most of the scheduling problems are NP-Hard, which is why heuristic and approximation algorithms must be used for large problems. Obviously these methods are of interest when they provide near optimal solutions with a polynomial computational complexity. This paper presents results for scheduling a set of non preemptive tasks by using a Hopfield neural network model. We present in this paper how this approach can solve scheduling problems following the "anytime" paradigm.</div></front></TEI><inist><standard h6="B"><pA><fA08 i1="01" i2="1" l="ENG"><s1>Anytime scheduling with neural networks</s1></fA08><fA09 i1="01" i2="1" l="ENG"><s1>ETFA '95 : 1995 INRIA/IEEE symposium on emerging technologies and factory automation</s1></fA09><fA11 i1="01" i2="1"><s1>GALLONE (J.-M.)</s1></fA11><fA11 i1="02" i2="1"><s1>CHARPILLET (F.)</s1></fA11><fA11 i1="03" i2="1"><s1>ALEXANDRE (F.)</s1></fA11><fA14 i1="01"><s1>CRIN-CNRS & INRIA Lorraine, BP 239</s1><s2>54506 Vandoœuvre-lès-Nancy</s2><s3>FRA</s3><sZ>1 aut.</sZ><sZ>2 aut.</sZ><sZ>3 aut.</sZ></fA14><fA18 i1="01" i2="1"><s1>INRIA. Centre de Rocquencourt</s1><s2>Rocquencourt</s2><s3>FRA</s3><s9>patr.</s9></fA18><fA18 i1="02" i2="1"><s1>IEEE. 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Gallone</name></region><name sortKey="Alexandre, F" sort="Alexandre, F" uniqKey="Alexandre F" first="F." last="Alexandre">F. Alexandre</name><name sortKey="Charpillet, F" sort="Charpillet, F" uniqKey="Charpillet F" first="F." last="Charpillet">F. Charpillet</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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