SciELO - Scientific Electronic Library Online

 
vol.19 issue1Saving Time for Object Finding with a Mobile Manipulator Robot in 3D EnvironmentClassification of Encephalographic Signals using Artificial Neural Networks author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • Have no similar articlesSimilars in SciELO

Share


Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.19 n.1 México Jan./Mar. 2015

http://dx.doi.org/10.13053/CyS-19-1-1964 

Artículos

 

Algoritmo aleatorizado basado en distribuciones deslizantes para el problema de planificación en sistemas Grid

 

Randomized Algorithm based on Sliding Distributions for the Scheduling Problem in Grid Systems

 

Héctor J. Selley-Rojas1,2, Jesús García-Díaz1, Manuel A. Soto-Ramos1, Felipe R. Menchaca-García3 and Rolando Menchaca-Mendez1

 

1 Instituto Politécnico Nacional, Centro de Investigación en Computación, México DF, México. hector.selley@gmail.com, jesgadiaz@gmail.com, yakovichs@gmail.com, rmen@cic.ipn.mx

2 Universidad Anáhuac Norte, Facultad de Ingeniería, México DF, México.

3 Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, México DF, México. fmenchac@gmail.com

Autor de correspondencia es Héctor J. Selley-Rojas.

 

Article received on 28/04/2014.
Accepted on 09/10/2014.

 

Resumen

En este artículo se presenta un algoritmo aleatorizado para el problema de planificación de tareas compuestas por procesos con restricciones de precedencia en ambientes distribuidos tipo Grid. El algoritmo aleatorizado propuesto esta basado en una nueva técnica que hemos denominado como de distribuciones deslizantes, la cual busca combinar las ventajas de los algoritmos de aproximación deterministas y de los algoritmos aleatorizados tipo Montecarlo. El objetivo es proveer un algoritmo que con alta probabilidad entregue soluciones p-aproximadas, pero que al mismo tiempo tenga la capacidad de analizar el vecindario extendido de dichas soluciones para escapar de máximos o mínimos locales. En el artículo se demuestra que el algoritmo propuesto es correcto y se caracteriza de manera formal su complejidad temporal. Así mismo, se evalúa el desempeño del algoritmo por medio de una serie de experimentos basados en simulaciones. Los experimentos muestran que el algoritmo propuesto logra en general un desempeño superior al de los algoritmos que componen el estado del arte en planificación en sistemas Grid. Las métricas de desempeño utilizadas son retardo promedio, retardo máximo y utilización de la Grid.

Palabras clave: Optimización combinatoria, algoritmo aleatorio, sistemas Grid, planificación de tareas.

 

Abstract

In this paper we present a randomized algorithm for the online version of the Job Shop problem where jobs are composed of processes with precedence constraints and processors are organized in a Grid topology. The proposed randomized algorithm is based on a new technique that we have denominated as sliding distributions, which aims at combining the advantages of the deterministic approximation algorithms with those of the Montecarlo randomized algorithms. The objective is to provide an algorithm that delivers p-approximated solutions with high probability, but at the same time, is able to investigate an extended neighborhood of such solutions so that it can escape from local extrema. We formally characterize the temporal complexity of the proposed algorithm and show that it is correct. We also evaluate the performance of the proposed algorithm by means of a series of simulation-based experiments. The results show that the proposed algorithm outperforms the traditional state of the art algorithms for scheduling in Grid systems. The performance metrics are average delay, maximum delay, and Grid utilization.

Keywords: Combinatorial optimization, randomized algorithm, Grid systems, scheduling.

 

DESCARGAR ARTÍCULO EN FORMATO PDF

 

Referencias

1. Lingo section [online], http://www.lindo.com/.

2. The standard performance evaluation corporation (SPEC) [online], http://www.spec.org/.

3. Abraham, A., Buyya, R., & Nath, B. (2000). Nature's heuristics for scheduling jobs on computational grids. The 8th IEEE International Conference on Advanced Computing and Communications (ADCOM 2000), pp. 45-52.         [ Links ]

4. Ambrust, M., Fox, A., Griffith, G., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., & Zaha-ria, M. (2010). A view of cloud computing. Communications of the ACM, Vol. 53, No. 4, pp. 50-58.         [ Links ]

5. Blythe, J., Deelman, E., Gil, Y., Kesselman, C., Agarwal, A., Metha, G., & Vahi, K. (2003). The role of planning in grid computing. ICAPS-03 Proceedings, American Asociation for Artificial Intelligence, pp. 154-163.         [ Links ]

6. Braunt, T. D., Siegel, H. J., Beck, N., Boloni, L. L., Maheswaran, M., Reuther, A. I., Robertson, J. A., Theys, M. D., Yao, B., Hensgen, D., & Freund, R. F. (2001). A comparison study of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. Journal of Parallel and Distributed Computing, Vol. 61, pp. 810-837.         [ Links ]

7. Deng, X. & Zhang, Y. (1999). Minimizing mean response time in batch processing system. The 5th Annual International Conference on Computing and Combinatorics, pp. 231-240.         [ Links ]

8. Di Martino, V. & Mililotti, M. (2004). Sub optimal scheduling in a grid using genetic algorithms. Parallel Computing, Vol. 30, No. 5, pp. 553-565.         [ Links ]

9. Dong, F. & Akl, S. G. (2006). Scheduling algorithms for grid computing: State of the art and open problems. Technical Report 504, Queen's University.         [ Links ]

10. Entezari, R. & Movaghar, A. (2012). A probabilistic task scheduling method for grid environments. Future Generation Computer Systems, Vol. 28, pp. 513-524.         [ Links ]

11. Foster, I. & Kesselman, C. The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann.         [ Links ]

12. Foster, I. & Kesselman, C. (2002). What is the Grid? A Three Point Checklist. Argonne National Laboratory, University of Chicago.         [ Links ]

13. Foster, I. & Kesselman, C. (2003). The grid 2: Blueprint for a new computing infrastructure. Morgan Kaufmann.         [ Links ]

14. Gary, M. R. & Johnson, D. S. (1979). Computers and Intractability: A Guide to the Theory of NP-completeness. WH Freeman and Company, New York.         [ Links ]

15. Gil, Y., Deelman, E., Blythe, J., Kesselman, C., & Tangmunarunkit, H. (2004). Artificial intelligence and grids: Workflow planning and beyond. E-Science, pp. 27-33.         [ Links ]

16. Goldberg, L. A., Paterson, M., Srinivasan, A., & Sweedyk, E. (1997). Better approximation guarantees for job-shop scheduling. Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, pp. 599-608.         [ Links ]

17. Hoos, H. H. & Stutzle, T. (2004). Stochastic local search: Foundations & applications. Morgan Kaufmann.         [ Links ]

18. Kasahara, H. & Narita, S. (1994). Practical multiprocessor scheduling algorithms for efficient parallel processing. IEEE Transactions on Computers, Vol. 33, No. 11, pp. 1023-1029.         [ Links ]

19. Khan, A. A., L., M. C., & Jones, M. S. (1994). A comparison of multiprocessor scheduling heuristics. Proceedings of the International Conference on Parallel Processing.         [ Links ]

20. Lenstra, J. K. & Kan, A. H. G. R. (1978). Complexity of scheduling under precedence constraints. Operations Research, Vol. 26, No. 1, pp. 22-35.         [ Links ]

21. Lenstra, J. K., Shmoys, D. B., & Tardos, E. (1990). Approximation algorithms for scheduling unrelated parallel machines. Mathematical programming, Vol. 46, No. 1, pp. 259-271.         [ Links ]

22. Li, H. & Buyya, R. (2009). Model-based simulation and performance evaluation of grid scheduling strategies. Future Generation Computer Systems, Vol. 25, pp. 460-465.         [ Links ]

23. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, Vol. 21, No. 6, pp. 1087-1092.         [ Links ]

24. Mitzenmacher, M. & Upfal, E. (2005). Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Cambridge.         [ Links ]

25. Motwani, R. & Raghavan, P. (1995). Randomized algorithms. Cambridge University Press.         [ Links ]

26. Prado, R. P., García-Galan, S., & Munoz Exposito, J. E. (2011). KASIA approach vs. differential evolution in fuzzy rule-based meta-schedulers for grid computing. IEEE.         [ Links ]

27. Ritchie, G. & Levine, J. (2003). A fast, effective local search for scheduling independent jobs in heterogeneous computing environments. Technical report, Centre for Intelligent Systems and their Applications, University of Edinburgh.         [ Links ]

28. Sahu, R. & Chaturvedi, A. K. (2011). Many-objective comparison of twelve grid scheduling heuristics. International Journal of Computer Applications, Vol. 13, No. 6.         [ Links ]

29. Shmoys, D. B. & Tardos, E. (1993). An approximation algorithm for the generalized assignment problem. Mathematical Programming, Vol. 62, pp. 461-474.         [ Links ]

30. Tao, Y. & Gerasoulis, A. (1994). DSC: Scheduling parallel tasks on an unbounded number of processors. IEEE Transactions on Parallel and Distributed Systems, Vol. 5, No. 9, pp. 951-967.         [ Links ]

31. van Laarhoven, P. J. M., Aarts, H. L., & Karel, J. (1992). Job shop scheduling by simulated annealing. Operations Research, Vol. 40, No. 1, pp. 113-125.         [ Links ]

32. Wang, L., Tao, J., Kunze, M., Castellanos, A., Kramer, D., & Karl, W. (2008). Scientific cloud computing: Early definition and experience. HPCC'08, 10th IEEE International Conference on High Performance Computing and Communications, IEEE, pp. 825-830.         [ Links ]

33. Wang, L., Von Laszewski, G., Younge, A., He, X., Kunze, M., Tao, J., & Fu, C. (2010). Cloud computing: a perspective study. New Generation Computing, Vol. 28, No. 2, pp. 137-146.         [ Links ]

34. Xian-He, S. & Ming, W. (2005). GHS: A performance system of grid computing. Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium, IEEE.         [ Links ]

35. Zan, M., Guanwen, W., Yunhui, H., & Hongwei, L. (2010). Quantum genetic algorithm for scheduling jobs on computational grids. 2010 International Conference on Measuring Technology and Mecatronics Automation, IEEE.         [ Links ]

36. Zomaya, A. Y. & Teh, Y. H. (2001). Observations on using genetic algorithms for dynamic load-balancing. IEEE Transactions on Parallel and Distributed Systems, Vol. 12, No. 9, pp. 899–911.         [ Links ]

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License