Real-Time Systems

, Volume 9, Issue 3, pp 289–304 | Cite as

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

  • Ruck Thawonmas
  • Goutam Chakraborty
  • Norio Shiratori
Article

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.

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References

  1. Aarts, E., and Korst, J. 1989.Simulated Annealing and Boltzmann Machines. New York: Wiley.Google Scholar
  2. Ae, T., and Aibara, R. 1990. Programmable real-time scheduler using a neurocomputer.The Journal of Real-Time Systems 1: 351–363.Google Scholar
  3. Burleson, W., Ko, J., Niehaus, D., Ramamritham, K., Stankovic, J. A., Wallace, G., and Weems, C. October 1993. The Spring scheduling co-processor: A scheduling accelerator.Proceedings of the IEEE International Conference on Computer Design.Google Scholar
  4. Garey, M. R., and Johnson, D. S. 1979.Computers and Intractability—A Guide to the Theory of NP-Completeness, San Francisco, CA: Freeman.Google Scholar
  5. Hopfield, J. J., and Tank, D. W. 1985. Neural computation of decisions in optimization problems.Biol. Cybern. 52: 141–152.Google Scholar
  6. Lee, K. C., Funabaki, N., and Takefuji, Y. 1992. A parallel improvement algorithm for the bipartite subgraph problem.IEEE Trans. Neural Networks 3(1): 139–145.Google Scholar
  7. Mead, C. 1989.Analog VLSI and Neural Systems. Reading, MA: Addison-Wesley.Google Scholar
  8. Niehaus, D., Ramamritham, K., Stankovic, J. A., Wallace, G., Weems, C., Burleson, W., and Ko, J. December 1993. The Spring scheduling co-processor: Design, use, and performance.Proceedings of the IEEE Real-Time Systems Symposium, pp. 106–111.Google Scholar
  9. Ramamritham, K., Stankovic, J. A., and Shiah, P. F. 1990. Efficient scheduling algorithms for real-time multiprocessor systems.IEEE Trans. Parallel and Distributed Systems 1(2): 184–194.Google Scholar
  10. Stankovic, J. A., and Ramamritham, K. 1991. The Spring kernel—A new paradigm for real-time systems.IEEE Software 8(3): 62–72.Google Scholar
  11. Tank, D. W., and Hopfield, J. J. 1986. Simple neural optimization networks—An A/D converter, signal decision circuit, and a linear programming circuit.IEEE Trans. Circuits Syst. CAS-33(5): 533–541.Google Scholar
  12. Thawonmas, R., Shiratori, N., and Noguchi, S. 1993. A real-time scheduler using neural networks for scheduling independent and nonpreemptable tasks with deadlines and resource requirements.IEICE Trans. Information and Systems 76-D(8): 947–955.Google Scholar
  13. Van Den Bout, D. E., and Miller, T. K. 1990. Graph partitioning using annealed neural networks.IEEE Trans. Neural Networks 1(2): 192–203.Google Scholar
  14. Zhao, W., Ramamritham, K., and Stankovic, J. A. 1987. Scheduling tasks with resource requirements in hard real-time systems.IEEE Trans. Software Eng. SE-13(5): 564–577.Google Scholar

Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Ruck Thawonmas
    • 1
  • Goutam Chakraborty
    • 2
  • Norio Shiratori
    • 2
  1. 1.Hitachi Research LaboratoryHitachi-shi, Ibaraki-kenJapan
  2. 2.Tohoku UniversityJapan

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