Real-time scheduler using neural networks for scheduling independent and nonpreemptable tasks with deadlines and resource requirements

Ruck Thawonmas, Norio Shiratori, Shoichi Noguchi

    Research output: Contribution to journalArticle

    7 Citations (Scopus)

    Abstract

    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 models. To cope with deadlines, a heuristic policy which is modified from the earliest deadline 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.

    Original languageEnglish
    Pages (from-to)947-955
    Number of pages9
    JournalIEICE Transactions on Information and Systems
    VolumeE76-D
    Issue number8
    Publication statusPublished - 1993 Aug

    ASJC Scopus subject areas

    • Computer Graphics and Computer-Aided Design
    • Information Systems
    • Software

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