Fast heuristic scheduling based on neural networks for real-time systems

Ruck Thawonmas*, Goutam Chakraborty, Norio Shiratori

*この研究の対応する著者

    研究成果査読

    3 被引用数 (Scopus)

    抄録

    As most of the real-time scheduling problems are known as hard problems, approximate or heuristic scheduling approaches are extremely required for solving these problems. This paper presents a new heuristic scheduling approach based on a modified Hopfield-Tank neural network to schedule tasks with deadlines and resource requirements in a multiprocessor system. In this approach, fast heuristic scheduling is achieved by performing a heuristic scheduling policy in conjunction with backtracking on the neural network. The results from our previous work, using the same neural network architecture without backtracking, are included here as a case with zero backtracking. Extensive simulation, which includes comparison with the conventional heuristic approach, is used to validate the effectiveness of our approach.

    本文言語English
    ページ(範囲)289-304
    ページ数16
    ジャーナルReal-Time Systems
    9
    3
    出版ステータスPublished - 1995 11

    ASJC Scopus subject areas

    • 計算理論と計算数学
    • 理論的コンピュータサイエンス

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