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 |
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Theoretical Computer Science