Abstract
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 language | English |
|---|---|
| Pages (from-to) | 289-304 |
| Number of pages | 16 |
| Journal | Real-Time Systems |
| Volume | 9 |
| Issue number | 3 |
| Publication status | Published - 1995 Nov |
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ASJC Scopus subject areas
- Computational Theory and Mathematics
- Theoretical Computer Science
Cite this
Fast heuristic scheduling based on neural networks for real-time systems. / Thawonmas, Ruck; Chakraborty, Goutam; Shiratori, Norio.
In: Real-Time Systems, Vol. 9, No. 3, 11.1995, p. 289-304.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Fast heuristic scheduling based on neural networks for real-time systems
AU - Thawonmas, Ruck
AU - Chakraborty, Goutam
AU - Shiratori, Norio
PY - 1995/11
Y1 - 1995/11
N2 - 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.
AB - 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.
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M3 - Article
AN - SCOPUS:0029410944
VL - 9
SP - 289
EP - 304
JO - Real-Time Systems
JF - Real-Time Systems
SN - 0922-6443
IS - 3
ER -