A Real-Time Scheduler Using Neural Networks for Scheduling Independent and Nonpreemptable Tasks with Deadlines and Resource Requirements


IEICE TRANSACTIONS on Information and Systems   Vol.E76-D   No.8   pp.947-955
Publication Date: 1993/08/25
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Bio-Cybernetics
real-time systems,  scheduling,  neural network applications,  Hopfield-Tank models,  

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This paper describes a neural network scheduler for scheduling independent and nonpreemptable tasks with deadlines and resource requirements in critical real-time applications, in which a schedule is to be obtained within a short time span. The proposed neural network scheduler is an integrate model of two Hopfield-Tank neural network medels. To cope with deadlines, a heuristic policy which is modified from the earliest deadling policy is embodied into the proposed model. Computer simulations show that the proposed neural network scheduler has a promising performance, with regard to the probability of generating a feasible schedule, compared with a scheduler that executes a conventional algorithm performing the earliest deadline policy.