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

Ruck Thawonmas*, Goutam Chakraborty, Norio Shiratori

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    3 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)289-304
    Number of pages16
    JournalReal-Time Systems
    Issue number3
    Publication statusPublished - 1995 Nov

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Theoretical Computer Science


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