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Applied Computational Intelligence and Soft Computing
Volume 2010 (2010), Article ID 505194, 10 pages
http://dx.doi.org/10.1155/2010/505194
Research Article

A Distributed Bio-Inspired Method for Multisite Grid Mapping

1Institute of High Performance Computing and Networking, National Research Council of Italy, Via P. Castellino 111, 80131 Naples, Italy
2Natural Computation Laboratory, DIIIE, University of Salerno, Via Ponte don Melillo 1, 84084 Fisciano (SA), Italy

Received 31 July 2009; Revised 8 January 2010; Accepted 20 March 2010

Academic Editor: Chuan-Kang Ting

Copyright © 2010 I. De Falco et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. F. Berman, “High-performance schedulers,” in The Grid: Blueprint for a Future Computing Infrastructure, I. Foster and C. Kesselman, Eds., pp. 279–307, Morgan Kaufmann, San Francisco, Calif, USA, 1998.
  2. G. Mateescu, “Quality of service on the grid via metascheduling with resource co-scheduling and co-reservation,” International Journal of High Performance Computing Applications, vol. 17, no. 3, pp. 209–218, 2003. View at Publisher · View at Google Scholar
  3. J. M. Schopf, “Ten actions when grid scheduling: the user as a grid scheduler,” in Grid Resource Management: State of the Art and Future Trends, pp. 15–23, Kluwer Academic Publishers, Norwell, Mass, USA, 2004.
  4. S. Fitzgerald, I. Foster, C. Kesselman, G. von Laszewski, W. Smith, and S. Tuecke, “A directory service for configuring high-performance distributed computations,” in Proceedings of the 6th IEEE International Symposium on High Performance Distributed Computing, pp. 365–375, IEEE Computer Society, Portland, Ore, USA, August 1997.
  5. K. Czajkowski, S. Fitzgerald, I. Foster, and C. Kesselman, “Grid information services for distributed resource sharing,” in Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing, pp. 181–194, San Francisco, Calif, USA, August 2001.
  6. I. Foster, “Globus toolkit version 4: software for service-oriented systems,” in Proceedings of IFIP International Conference on Network and Parallel Computing (NPC '05), vol. 3779 of Lecture Notes in Computer Science, pp. 2–13, Beijing, China, November-December 2005.
  7. L. Adzigogov, J. Soldatos, and L. Polymenakos, “EMPEROR: an OGSA grid meta-scheduler based on dynamic resource predictions,” Journal of Grid Computing, vol. 3, no. 1-2, pp. 19–37, 2005. View at Publisher · View at Google Scholar
  8. R. F. Freund, “Optimal selection theory for super concurrency,” in Supercomputing, pp. 699–703, IEEE Computer Society, Reno, Nev, USA, 1989.
  9. M. M. Eshaghian and M. E. Shaaban, “Cluster-m programming paradigm,” International Journal of High Speed Computing, vol. 6, no. 2, pp. 287–309, 1994.
  10. T. D. Braun, H. J. Siegel, N. Beck, et al., “A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems,” Journal of Parallel and Distributed Computing, vol. 61, no. 6, pp. 810–837, 2001. View at Publisher · View at Google Scholar
  11. K.-H. Kim and S.-R. Han, “Mapping cooperating grid applications by affinity for resource characteristics,” in Proceedings of the 13th International Conference on AIS, vol. 3397 of Lecture Notes in Artificial Intelligence, pp. 313–322, 2005.
  12. F. Dong and S. G. Akl, “Scheduling algorithms for grid computing: state of the art and open problems,” Tech. Rep. 2006-504, School of Computing, Queens University, Kingston, Canada, 2006.
  13. H. Singh and A. Youssef, “Mapping and scheduling heterogeneous task graphs using genetic algorithms,” in Proceedings of Heterogeneous Computing Workshop, pp. 86–97, IEEE Computer Society, Honolulu, Hawaii, USA, 1996.
  14. P. Shroff, D. W. Watson, N. S. Flan, and R. F. Freund, “Genetic simulated annealing for scheduling data-dependent tasks in heterogeneous environments,” in Proceedings of Heterogeneous Computing Workshop, pp. 98–104, IEEE Computer Society, Honolulu, Hawaii, USA, 1996.
  15. L. Wang, H. J. Siegel, V. P. Roychowdhury, and A. A. MacIejewski, “Task matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach,” Journal of Parallel and Distributed Computing, vol. 47, no. 1, pp. 8–22, 1997. View at Publisher · View at Google Scholar
  16. O. H. Ibarra and C. E. Kim, “Heuristic algorithms for scheduling independent tasks on non identical processors,” Journal of Association for Computing Machinery, vol. 24, no. 2, pp. 280–289, 1977.
  17. D. Fernandez-Baca, “Allocating modules to processors in a distributed system,” IEEE Transactions on Software Engineering, vol. 15, no. 11, pp. 1427–1436, 1989. View at Publisher · View at Google Scholar
  18. Y.-K. Kwok and I. Ahmad, “Efficient scheduling of arbitrary task graphs to multiprocessors using a parallel genetic algorithm,” Journal of Parallel and Distributed Computing, vol. 47, no. 1, pp. 58–77, 1997. View at Publisher · View at Google Scholar
  19. A. Abraham, R. Buyya, and B. Nath, “Nature's heuristics for scheduling jobs on computational grids,” in Proceedings of the 8th International Conference on Adavanced Computing and Communication, pp. 45–52, 2000.
  20. S. Kim and J. B. Weissman, “A genetic algorithm based approach for scheduling decomposable data grid applications,” in Proceedings of the International Conference on Parallel Processing (ICPP '04), pp. 406–413, Montreal, Canada, August 2004.
  21. A. Bose, B. Wickman, and C. Wood, “MARS: a metascheduler for distributed resources in campus grids,” in Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing (GRID '04), pp. 110–118, IEEE Computer Society, Pittsburgh, Pa, USA, November 2004.
  22. S. Song, Y.-K. Kwok, and K. Hwang, “Security-driven heuristics and a fast genetic algorithm for trusted grid job scheduling,” in Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS '05), p. 65, Denver, Colo, USA, April 2005.
  23. K. Price and R. Storn, “Differential evolution,” Dr. Dobb's Journal, vol. 22, no. 4, pp. 18–24, 1997.
  24. R. Storn and K. Price, “Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997.
  25. G. Shao, F. Berman, and R. Wolski, “Master/slave computing on the grid,” in Proceedings of the 9th Heterogeneous Computing Workshop, pp. 3–16, IEEE Computer Society, Cancun, Mexico, 2000.
  26. N. Ranaldo and E. Zimeo, “An economy-driven mapping heuristic for hierarchical master-slave applications in grid systems,” in Proceedings of the 20th International Parallel and Distributed Processing Symposium (IPDPS '06), Rhodes Island, Greece, 2006. View at Publisher · View at Google Scholar
  27. S. Das, A. Abraham, and A. Konar, “Particle swarm optimization and differential evolution algorithms: technical analysis, applications and hybridization perspectives,” in Studies in Computational Intelligence, Y. Liu, et al., Ed., vol. 116, pp. 1–38, Springer, Berlin, Germany, 2008.
  28. A. Nobakhti and H. Wang, “A simple self-adaptive differential evolution algorithm with application on the ALSTOM gasifier,” Applied Soft Computing Journal, vol. 8, no. 1, pp. 350–370, 2008. View at Publisher · View at Google Scholar
  29. S. Das, A. Abraham, U. K. Chakraborty, and A. Konar, “Differential evolution using a neighborhood-based mutation operator,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 3, pp. 526–553, 2009. View at Publisher · View at Google Scholar
  30. R. F. Freund and H. J. Siegel, “Heterogeneous processing,” IEEE Computer, vol. 26, no. 6, pp. 13–17, 1993.
  31. A. Khokhar, V. K. Prasanna, M. Shaaban, and C. L. Wang, “Heterogeneous computing: challenges and opportunities,” IEEE Computer, vol. 26, no. 6, pp. 18–27, 1993.
  32. H. J. Siegel, J. K. Antonio, R. C. Metzger, M. Tan, and Y. A. Li, “Heterogeneous computing,” in Parallel and Distributed Computing Handbook, A. Y. Zomaya, Ed., pp. 725–761, McGraw-Hill, New York, NY, USA, 1996.
  33. V. S. Sunderam, “Design issues in heterogeneous network computing,” in Proceedings of the Workshop on Heterogeneous Processing, pp. 101–112, IEEE Computer Society, Beverly Hills, Calif, USA, 1992.
  34. R. Wolski, N. T. Spring, and J. Hayes, “Network weather service: a distributed resource performance forecasting service for metacomputing,” Future Generation Computer Systems, vol. 15, no. 5, pp. 757–768, 1999. View at Publisher · View at Google Scholar
  35. L. Gong, X.-H. Sun, and E. F. Watson, “Performance modeling and prediction of nondedicated network computing,” IEEE Transactions on Computers, vol. 51, no. 9, pp. 1041–1055, 2002.
  36. E. Cantú-Paz, “A summary of research on parallel genetic algorithms,” Tech. Rep. 95007, University of Illinois, Urbana-Champaign, Ill, USA, July 1995.
  37. H. Mühlenbein, “Evolution in time and space—the parallel genetic algorithm,” in Foundation of Genetic Algorithms, pp. 316–337, Morgan Kaufmann, San Francisco, Calif, USA, 1992.
  38. M. Snir, S. Otto, S. Huss-Lederman, D. Walker, and J. Dongarra, MPI: The Complete Reference, Vol. 1—The MPI Core, MIT Press, Cambridge, Mass, USA, 1998.
  39. N. T. Karonis, B. Toonen, and I. Foster, “MPICH-G2: a grid-enabled implementation of the Message Passing Interface,” Journal of Parallel and Distributed Computing, vol. 63, no. 5, pp. 551–563, 2003. View at Publisher · View at Google Scholar
  40. T. Kielmann, H. E. Bal, J. Maassen, et al., “Programming environments for high-performance grid computing: the Albatross project,” Future Generation Computer Systems, vol. 18, no. 8, pp. 1113–1125, 2002. View at Publisher · View at Google Scholar
  41. G. E. Fagg, K. S. London, and J. J. Dongarra, “MPI connect: managing heterogeneous MPI applications interoperation and process control,” in Recent Advances in Parallel Virtual Machine and Message Passing Interface, vol. 1497 of Lecture Notes in Computer Science, pp. 93–96, Springer, New York, NY, USA, 1998.
  42. B. Bierbaum, C. Clauss, T. Eickermann, et al., “Reliable orchestration of distributed MPI-applications in a UNICORE-based grid with MetaMPICH and MetaScheduling,” in Proceedings of the 13th European PVM/MPI User's Group Meeting, vol. 4192 of Lecture Notes in Computer Science, pp. 174–183, Bonn, Germany, September 2006.