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


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.


Neural Network System Performance Schedule Problem Network Architecture Schedule Policy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  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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