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Multiprocessor Scheduling and Rescheduling with Use of Cellular Automata and Artificial Immune System Support
March 2006 (vol. 17 no. 3)
pp. 253-262

Abstract—The paper presents cellular automata (CA)-based multiprocessor scheduling system, in which an extraction of knowledge about scheduling process occurs and this knowledge is used while solving new instances of the scheduling problem. There are three modes of the scheduler: learning, normal operating, and reusing. In the learning mode, a genetic algorithm is used to discover CA rules suitable for solving instances of a scheduling problem. In the normal operating mode, discovered rules are able to find automatically, without a calculation of a cost function, an optimal or suboptimal solution of the scheduling problem for any initial allocation of program tasks in a multiprocessor system. In the third mode, previously discovered rules are reused with support of an artificial immune system (AIS) to solve new instances of the problem. We present a number of experimental results showing the performance of the CA-based scheduler.

[1] J. Blazewicz, K.H. Ecker, G. Schmidt, J. Weglarz, Scheduling in Computer and Manufacturing Systems. Springer, 1994.
[2] L.N. de Castro and J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach. Springer, 2002.
[3] R. Das, M. Mitchell, and J.P. Crutchfield, “A Genetic Algorithm Discovers Particle-Based Computation in Cellular Automata,” Parallel Problem Solving from Nature— PPSN III, Y. Davidor et al., eds., LNCS 866, Springer, pp. 344-353, 1994.
[4] H. El-Rewini, T.G. Lewis, and H.H. Ali, Task Scheduling in Parallel and Distributed Systems. PTR Prentice Hall, 1994.
[5] J.D. Farmer, N.H. Packard, and A.S. Perelson, “The Immune System, Adaptation, and Machine Learning,” Physica D, vol. 22, pp. 187-204, 1986.
[6] S. Forrest, B. Javornik, R. Smith, and A.S. Perelson, “Using Genetic Algorithms to Explore Pattern Recognition in the Immune System,” Evolutionary Computation, vol. 1, pp. 191-211, 1993.
[7] M.R. Garey and D.S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Company, 1979.
[8] IEEE Trans. Evolutionary Computation, special issue on artificial immune systems, vol. 6, no. 3, 2002.
[9] E. Hart and P. Ross, “An Immune System Approach to Scheduling in Changing Environments,” GECCO-99: Proc. Genetic and Evolutionary Computation Conf., pp. 1559-1566, 1999.
[10] A.A. Khan, C.L. McCreary, and M.S. Jones, “A Comparison of Multiprocessor Scheduling Heuristics,” Proc. Int'l Conf. Parallel Processing, vol. 2, pp. 243-250, 1994.
[11] Y.K. Kwok and I. Ahmad, “Benchmarking the Task Graph Scheduling Algorithms,” Proc. 1998 IPPS/SPDP Symp., pp. 531-537, 1998.
[12] M. Mitchell, “Computation in Cellular Automata,” Non-Standard Computation, T. Gramb et al., eds., Wiley-VCH, pp. 95-140, 1998.
[13] M. Mitchell and S. Forrest, “Genetic Algorithms and Artificial Life,” Artificial Life. An Overview, G. Langton, ed., MIT Press, 1995.
[14] Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs. Springer, 1992.
[15] B.J. Overeinder, Distributed Event-Driven Simulation— Scheduling Strategies and Resource Management, PhD thesis, Univ. in Amsterdam, Holland, 2000.
[16] S. Saleh and A.Y. Zomaya, “Multiprocessor Scheduling Using Mean-Field Annealing,” Parallel and Distributed Processing, LNCS 1388, Springer, pp. 288-296, 1998.
[17] A. Schoneveld, Parallel Complex Systems Simulation, PhD thesis, Univ. in Amsterdam, Holland, 1999.
[18] F. Seredynski, “Scheduling Tasks of a Parallel Program in Two-Processor Systems with Use of Cellular Automata,” Future Generation Computer Systems, vol. 14, Elsevier, pp. 351-364, 1998.
[19] F. Seredynski and A.Y. Zomaya, “Sequential and Parallel Cellular Automata-Based Scheduling Algorithms,” IEEE Trans. Parallel and Distributed Systems, vol. 13, no. 10, pp. 1009-1023, 2002.
[20] M. Sipper, Evolution of Parallel Cellular Machines, The Cellular Programming Approach, LNCS 1194, Springer, 1997.
[21] R. Subrata and A.Y. Zomaya, “Evolving Cellular Automata for Location Management in Mobile Computing,” IEEE Trans. Parallel and Distributed Systems, vol. 14, no. 1, pp. 13-26, Jan. 2003.
[22] M. Tomassini, M. Sipper, and M. Perrenoud, “On the Generation of High-Quality Random Numbers by Two-Dimensional Cellular Automata,” IEEE Trans. Computers, vol. 49, no. 10, pp. 1140-1151, 2000.
[23] L. Wang, H.J. Siegel, V.P. Roychowolhury, and A.A. Maciejewski, “Task Matching and Scheduling in Heterogeneous Computing Environments Using a Genetic-Algorithm-Based Approach,” J. Parallel and Distributed Computing, vol. 47, no. 1, pp. 8-22, 1997.
[24] S. Wolfram, “Universality and Complexity in Cellular Automata,” Physica D, vol. 10, pp. 1-35, 1984.
[25] D.H. Wolpert and W.G. Macready, “No Free Lunch Theorems for Optimization,” IEEE Trans. Evolutionary Computation, vol. 1, pp. 67-82, 1997.

Index Terms:
Multiprocessor scheduling, cellular automata, genetic algorithm, artificial immune system.
Citation:
Anna Swiecicka, Franciszek Seredynski, Albert Y. Zomaya, "Multiprocessor Scheduling and Rescheduling with Use of Cellular Automata and Artificial Immune System Support," IEEE Transactions on Parallel and Distributed Systems, vol. 17, no. 3, pp. 253-262, March 2006, doi:10.1109/TPDS.2006.38
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