Task Assignment Strategies for a Complex Real-time Network System

  • Kim Hong-Ryeol (School of Information Engineering, Myongji University) ;
  • Oh Jae-Joon (School of Information Engineering, Myongji University) ;
  • Kim Dae-Won (School of Information Engineering, Myongji University)
  • 발행 : 2006.10.01

초록

In this paper, a study on task assignment strategies for a complex real-time network system is presented. Firstly, two task assignment strategies are proposed to improve previous strategies. The proposed strategies assign tasks with meeting end-to-end real-time constraints, and also with optimizing system utilization through period modulation of the tasks. Consequently, the strategies aim at the optimizationto optimize of system performance with while still meeting real-time constraints. The proposed task assignment strategies are devised using the genetic algorithmswith heuristic real-time constraints in the generation of new populations. The strategies are differentiated by the optimization method of the two objectives-meeting end-to-end real-time constraints and optimizing system utilization: the first one has sequential genetic algorithm routines for the objectives, and the second one has one multiple objective genetic algorithm routine to find a Pareto solution. Secondly, the performances of the proposed strategies and a well-known existing task assignment strategy using the BnB(Branch and Bound) optimization are compared with one other through some simulation tests. Through the comparison of the simulation results, the most adequate task assignment strategies are proposed for some as system requirements-: the optimization of system utilization, the maximization of running tasktasks, and the minimization of the number of network node nodesnumber for a network system.

키워드

참고문헌

  1. H. Kim, J. Kim, and D. Kim, 'Development of coordinated scheduling strategy with end-to-end respose time analysis for the CAN-based distributed control systems,' Proc. of IEEE/RSJ International Conference on Intelligent Robot and System, pp. 2099-2104, 2004
  2. CAN Specification Version 2.0, Robert Bosch GmbH, 1991
  3. J. Lehoczky, L. Sha, and Y. Ding, 'The rate monotonic scheduling algorithm: Exact characterization and average case behavior,'  Proc. of IEEE Real-Time Systems Symposium, 1989
  4. C. L. Liu and J. W. Layland, 'Scheduling algorithms for multiprogramming in a hardrealtime environment,' Journal of the ACM, vol. 20, no. 1, pp. 46-61, 1973 https://doi.org/10.1145/321738.321743
  5. M. Spuri and G. Buttazzo, 'Scheduling aperiodic tasks in dynamic priority systems,' Journal of Real-time Systems, vol. 10, pp. 179-210, 1996 https://doi.org/10.1007/BF00360340
  6. K. Tindell and A. Burns, 'Guaranteed message latencies for distributed safety-critical hard real time control networks,' Technical Report YCS 94-229, Dept. Computer Science, Univ. of York, York, UK, 1994
  7. M. Di Natale, 'Scheduling the CAN bus with earliest deadline techniques,' Proc. of the IEEE Real-Time Symposium, pp. 259-268, 2000
  8. K. M. Zuberi and K. G. Shin, 'Non-preemptive scheduling of messages on controller area network for real-time control applications,' Proc. of Real-Time Technology and Applications Symposium, pp. 240-249, 1995
  9. J. B. Sinclair, 'Efficient computation of optimal assignments for distributed tasks,' Journal of Parallel and Distributed Computing, vol. 4, pp. 342-362, 1987 https://doi.org/10.1016/0743-7315(87)90024-4
  10. V. M. Lo, 'Heuristic Algorithms for task assignment in distributed systems,' IEEE Trans. on Computers, vol. 37, no. 11, pp. 1384-1397, 1988 https://doi.org/10.1109/12.8704
  11. W. W. Chu and L. M. Lan, 'Task allocation and precedence relations for distributed real-time system,' IEEE Trans. on Computers, vol. 36, no. 6, pp. 667-679, 1987 https://doi.org/10.1109/TC.1987.1676960
  12. W. H. Kohler and K. Steiglitz, 'Computer and job-shop scheduling theory,' Enumerative and Iterative Computational Approach, pp. 229-287, John Wiley & Sons, 1976
  13. W. W. Chu and K. Leung, 'Module replication and assignment for real-time distributed processing systems,' Proc. of the IEEE, vol. 75, no. 5, pp. 547-562, 1987 https://doi.org/10.1109/PROC.1987.13772
  14. D.-T. Peng, K. G. Shin, and T. F. Abdelzaher, 'Assignment and scheduling communicating periodic tasks in distributed real-time systems,'  IEEE Trans. on Software Engineering, vol. 23, no. 12, pp. 745-758, 1997 https://doi.org/10.1109/32.637388
  15. M. A. Moncusi, J. M. Banus, J. Labarta, and A. Arenas, 'A new heuristic algorithm to assign priorities and resources to tasks with end-to-end deadlines,' Proc. of International Conference on Parallel and Distributed Processing Techniques and Applications, vol. IV, pp. 2102-2108, 2001
  16. K. Tindell, A. Burns, and A. J. Wellings, 'Allocating real-time tasks (an np-hard problem made easy),' Journal of Real-Time Systems, vol. 4, no. 2, pp. 145-165, 1992 https://doi.org/10.1007/BF00365407
  17. T. M. Chung and H. G. Dietz, 'Adaptive genetic algorithm: Scheduling hard real-time control programs with arbitrary timing constraints,' Technical Report of Purde University, 1995
  18. R. Nossal and T. M. Galla, 'Solving NP-complete problems in real-time system design by multichromosome genetic algorithms,' Proc. of SIGPLAN Workshop on Languages, Compilers, and Tools for Real-Time Systems, pp. 68-76, 1997
  19. A. S. Wu, H. Yu, S. Jin, K. Lin, and G. Schivone, 'An incremental genetic approach to multiprocessor scheduling,' IEEE Trans. on Parallel and Distributed Systems, vol. 15, no. 9, pp. 824-834, 2004 https://doi.org/10.1109/TPDS.2004.38
  20. Y.-H. Lee and C. Chen, 'A modified genetic algorithm for task scheduling in multiprocessor systems,' Proc. of Workshops on Compiler Techniques for High-Performance Computing, 2003
  21. T. C. Lueth and T. Laengle, 'Task description, decomposition, and allocation in a distributed autonomous multi-agent robot system,' Proc. of International Conference of Intelligent Robots and Systems, pp. 1516- 1523, 1994
  22. P. Altenbernd, C. Ditze, P. Laplante, and W. Halang, 'Allocation of Periodic real-time tasks,' Proc. of IFAC/IFIP Workshop, 1995
  23. 'Alpha Linux Ready Systems.' Available: http://h18002.www1.hp.com/alphaserver/linux/
  24. T. W. Kuo and A. K. Mok, 'Incremental reconfiguration and load adjustment in adaptive real-time systems,' IEEE Trans. on Computers, vol. 46, no. 12, pp. 1313-1324, 1997 https://doi.org/10.1109/12.641932
  25. K. J. Astrom and B. Wittenmark, Computer- Controlled Systems, Prentice Hall, 1997
  26. C. L. Hwang and K. Yoon, Multiple Attribute Decision Making, Methods and Application, a State-of-Art Survey, Springer-Verlag, 1981
  27. J.-H. Chen, 'Theoretical analysis of multiobjective genetic algorithms - convergence time, population sizing and disequilibrium,' Report for IEEE NNS Walter Karplus Research Grant, 2003
  28. H. Tamaki, H. Kita, and S. Kobayasi, 'Multiobjective optimization by genetic algorithms: A Review,' Proc. of the IEEE International Conference on Evolutionary Computation, pp. 517-522, 1996