InforLorV4, Main, Merge, bibRecord, 00D081

Anytime scheduling with neural networks

Identifieur interne : 00D081 ( Main/Merge ); précédent : 00D080; suivant : 00D082

Anytime scheduling with neural networks

Auteurs : J.-M. Gallone [France] ; F. Charpillet [France] ; F. Alexandre [France]

Source :

RBID : Pascal:96-0350284

Descripteurs français

Pascal (Inist)
- Ordonnancement, Réseau neuronal, Modèle Hopfield, Complexité calcul, Problème NP dur, Solution optimale, Système informatique, Anytime algorithm.
Wicri :
- topic : Système informatique.

English descriptors

KwdEn :
- Computational complexity, Computer system, Hopfield model, NP hard problem, Neural network, Optimal solution, Scheduling.

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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Pascal:96-0350284

Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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Anytime scheduling with neural networks

Anytime scheduling with neural networks

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri