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Parallel multiobjective evolutionary algorithms for batch scheduling in heterogeneous computing and grid systems

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This article presents six parallel multiobjective evolutionary algorithms applied to solve the scheduling problem in distributed heterogeneous computing and grid systems. The studied evolutionary algorithms follow an explicit multiobjective approach to tackle the simultaneous optimization of a system-related (i.e. makespan) and a user-related (i.e. flowtime) objectives. Parallel models of the proposed methods are developed in order to efficiently solve the problem. The experimental analysis demonstrates that the proposed evolutionary algorithms are able to efficiently compute accurate results when solving standard and new large problem instances. The best of the proposed methods outperforms both deterministic scheduling heuristics and single-objective evolutionary methods previously applied to the problem.

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References

  1. Abraham, A., Buyya, R., Nath, B.: Nature heuristics for scheduling jobs on computational grids. In: Proc. of 8th IEEE Int. Conf. on Advanced Computing and Communications, pp. 45–52 (2000)

    Google Scholar 

  2. Abraham, A., Liu, H., Grosan, C., Xhafa, F.: Nature inspired meta-heuristics for grid scheduling: single and multi-objective optimization approaches. In: Metaheuristics for Scheduling in Distributed Computing Environments, vol. 146, pp. 247–272. Springer, Berlin (2008)

    Chapter  Google Scholar 

  3. Alba, E.: Parallel Metaheuristics: A New Class of Algorithms. Wiley-Interscience, New York (2005)

    Book  MATH  Google Scholar 

  4. Alba, E., Dorronsoro, B.: Cellular Genetic Algorithms. Operations Research/Computer Science Interfaces, vol. 42. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  5. Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Trans. Evol. Comput. 6(5), 443–462 (2002)

    Article  Google Scholar 

  6. Ali, S., Siegel, H., Maheswaran, M., Ali, S., Hensgen, D.: Task execution time modeling for heterogeneous computing systems. In: Proc. of the 9th Heterogeneous Computing Workshop, p. 185. IEEE Computer Society, Washington (2000)

    Google Scholar 

  7. Arroyo, J., de Souza, A.: A GRASP heuristic for the multi-objective permutation flowshop scheduling problem. Int. J. Adv. Manuf. Technol. 55, 741–753 (2011)

    Article  Google Scholar 

  8. Bäck, T., Fogel, D., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. Oxford University Press, London (1997)

    MATH  Google Scholar 

  9. Berman, F., Fox, G., Hey, A.: Grid Computing: Making the Global Infrastructure a Reality. Wiley, New York (2003)

    Google Scholar 

  10. Braun, T., Siegel, H., Beck, N., Bölöni, L., Maheswaran, M., Reuther, A., Robertson, J., Theys, M., Yao, B., Hensgen, D., Freund, R.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib Comput 61(6), 810–837 (2001)

    Article  Google Scholar 

  11. Chandrasekaran, S., Ponnambalam, S., Suresh, R., Vijayakumar, N.: Multiobjective Particle Swarm Optimization algorithm for scheduling in flowshop to minimize makespan, total flowtime and completion time variance. In: IEEE Congress on Evolutionary Computation, pp. 4012–4018 (2007)

    Google Scholar 

  12. Chiang, T., Cheng, H., Fu, L.: NNMA: an effective memetic algorithm for solving multiobjective permutation flow shop scheduling problems. Expert Syst. Appl. 38, 5986–5999 (2011)

    Article  Google Scholar 

  13. Chitra, P., Rajaram, R., Venkatesh, P.: Application and comparison of hybrid evolutionary multiobjective optimization algorithms for solving task scheduling problem on heterogeneous systems. Appl. Soft Comput. 11(2), 2725–2734 (2011)

    Article  Google Scholar 

  14. Coello, C., Van Veldhuizen, D., Lamont, G.: Evolutionary Algorithms for Solving Multi-objective Problems. Kluwer Academic, New York (2002)

    Book  MATH  Google Scholar 

  15. De Falco, I., Della Cioppa, A., Maisto, D., Scafuri, U., Tarantino, E.: A multiobjective extremal optimization algorithm for efficient mapping in grids. In: Advances in Soft Computing, vol. 58, pp. 367–377. Springer, Berlin (2009)

    Google Scholar 

  16. Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

  17. Deb, K., Agrawal, S., Pratab, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Proc. of the Parallel Problem Solving from Nature VI Conference. Lecture Notes in Computer Science, vol. 1917, pp. 849–858. Springer, Berlin (2000)

    Chapter  Google Scholar 

  18. Dorronsoro, B., Bouvry, P., Cañero, J., Maciejewski, A., Siegel, H.: Multi-objective robust static mapping of independent tasks on grids. In: IEEE Congress on Evolutionary Computation (CEC), Part of the World Congress on Computational Intelligence (WCCI), pp. 3389–3396 (2010)

    Google Scholar 

  19. Durillo, J., Nebro, A., Luna, F., Dorronsoro, B., Alba, E.: jMetal: a Java framework for developing multi-objective optimization metaheuristics. Tech. rep. ITI-2006-10, University of Málaga (2006)

  20. Durillo, J., Nebro, A., Luna, F., Alba, E.: A study of master-slave approaches to parallelize NSGA-II. In: Proc. of the 22nd IEEE Int. Symposium on Parallel and Distributed Processing, pp. 1–8 (2008)

    Google Scholar 

  21. El-Rewini, H., Lewis, T., Ali, H.: Task Scheduling in Parallel and Distributed Systems. Prentice-Hall, New York (1994)

    Google Scholar 

  22. Eshaghian, M.: Heterogeneous Computing. Artech House, Norwood (1996)

    Google Scholar 

  23. Eshelman, L.: The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination. In: Foundations of Genetics Algorithms, pp. 265–283. Morgan Kaufmann, San Mateo (1991)

    Google Scholar 

  24. Foster, I., Kesselman, C.: The Grid: Blueprint for a Future Computing Infrastructure. Morgan Kaufmann, San Mateo (1998)

    Google Scholar 

  25. Freund, R., Sunderam, V., Gottlieb, A., Hwang, K., Sahni, S.: Special issue on heterogeneous processing. J. Parallel Distrib. Comput. 21(3), 255–256 (1994)

    Article  Google Scholar 

  26. Garey, M., Johnson, D.: Computers and Intractability. Freeman, New York (1979)

    MATH  Google Scholar 

  27. Guo, S., Huang, H., Wang, Z., Xie, M.: Grid service reliability modeling and optimal task scheduling considering fault recovery. IEEE Trans. Reliab. 60(1), 263–274 (2011)

    Article  Google Scholar 

  28. Izakian, H., Abraham, A., Snasel, V.: Comparison of heuristics for scheduling independent tasks on heterogeneous distributed environments. In: Proc. of the 2009 Int. Joint Conf. on Computational Sciences and Optimization, pp. 8–12. IEEE Press, New York (2009)

    Chapter  Google Scholar 

  29. Jakob, W., Quinte, A., Stucky, K.U., Sub, W.: Fast multi-objective scheduling of jobs to constrained resources using a hybrid evolutionary algorithm. In: Parallel Problem Solving from Nature. Lecture Notes in Computer Science, vol. 5199, pp. 1031–1040. Springer, Berlin (2008)

    Chapter  Google Scholar 

  30. Knowles, J., Corne, D.: Approximating the nondominated front using the Pareto archived evolution strategy. Evol. Comput. 8(2), 149–172 (2000)

    Article  Google Scholar 

  31. Knowles, J.: A summary-attainment-surface plotting method for visualizing the performance of stochastic multiobjective optimizers. In: 5th Int. Conf. on Intelligent Systems Design and Applications (ISDA’05), Wroclaw, Poland, pp. 552–557 (2005)

    Chapter  Google Scholar 

  32. Krömer, P., Snásel, V., Platos, J., Abraham, A., Ezakian, H.: Evolving schedules of independent tasks by differential evolution. In: Intelligent Networking, Collaborative Systems and Applications, vol. 329, pp. 79–94 (2011)

    Chapter  Google Scholar 

  33. Kurowski, K., Oleksiak, A., Witkowski, M., Nabrzyski, J.: Distributed power management and control system for sustainable computing environments. In: Int. Green Computing Conference, pp. 365–372 (2010)

    Chapter  Google Scholar 

  34. Kwok, Y., Ahmad, I.: Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput. Surv. 31(4), 406–471 (1999)

    Article  Google Scholar 

  35. Lenstra, J., Shmoys, D., Tardos, E.: Approximation algorithms for scheduling unrelated parallel machines. Math. Program. 46, 259–271 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  36. Leung, J., Kelly, L., Anderson, J.: Handbook of Scheduling: Algorithms, Models, and Performance Analysis. CRC Press, Boca Raton (2004)

    MATH  Google Scholar 

  37. Liao, X., Deng, J., Li, X.: An evolutionary algorithm for constraint flow shops with multi-criteria optimization. In: Proc. of the 7th Int. Conf. on Machine Learning and Cybernetics, pp. 904–908 (2008)

    Google Scholar 

  38. Nebro, A., Durillo, J., Luna, F., Dorronsoro, B., Alba, E.: Design issues in a multiobjective cellular genetic algorithm. In: 4th Int. Conf. on Evolutionary Multi-criterion Optimization. Lecture Notes in Computer Science, vol. 4403, pp. 126–140. Springer, Berlin (2007)

    Chapter  Google Scholar 

  39. Nebro, A., Durillo, J., Luna, F., Dorronsoro, B., Alba, E.: MOCell: a cellular genetic algorithm for multiobjective optimization. Int. J. Intell. Syst. 24(7), 726–746 (2009)

    Article  MATH  Google Scholar 

  40. Nesmachnow, S.: Una versión paralela del algoritmo evolutivo para optimización multiobjetivo NSGA-II. In: X Congreso Argentino de Ciencias de Computación, pp. 1933–1944 (2004) (in Spanish)

    Google Scholar 

  41. Nesmachnow, S., Iturriaga, S.: Multiobjective scheduling on distributed heterogeneous computing and grid environments using a parallel micro-chc evolutionary algorithm. In: Int. Conf. on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 134–141. IEEE Press, New York (2011)

    Chapter  Google Scholar 

  42. Nesmachnow, S., Cancela, H., Alba, E.: Heterogeneous computing scheduling with evolutionary algorithms. Soft Comput. 15(4), 685–701 (2010)

    Article  Google Scholar 

  43. Nesmachnow, S., Cancela, H., Alba, E.: A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling. Appl. Soft Comput. 12(2), 626–639 (2012)

    Article  MathSciNet  Google Scholar 

  44. Pettey, C.: Diffusion (cellular) models. In: Bäck, T., Fogel, D., Michalewicz, Z. (eds.) Handbook of Evolutionary Computation. Oxford Univ. Press, London (1997). Chap. C6.4, pp. C6.4:1–6

    Google Scholar 

  45. Rabanimotlagh, A.: An efficient ant colony optimization algorithm for multiobjective flow shop scheduling problem. World Acad. Sci., Eng. Technol. 75, 128–133 (2011)

    Google Scholar 

  46. Reichelt, D., Mönch, L.: Multiobjective scheduling of jobs with incompatible families on parallel batch machines. In: Proc. of the 6th European Conference on Evolutionary Computation in Combinatorial Optimization, pp. 209–221 (2006)

    Chapter  Google Scholar 

  47. Ritchie, G., Levine, J.: A hybrid ant algorithm for scheduling independent jobs in heterogeneous computing environments. In: Proc. of the 23rd Workshop of the UK Planning and Scheduling Special Interest Group, pp. 178–183 (2004)

    Google Scholar 

  48. Rodríguez, A., Nesmachnow, S.: MOE: un entorno de trabajo para optimización multiobjetivo con algoritmos evolutivos. Tech. rep. RT 09-21, Instituto de Computación, Facultad de Ingeniería, Universidad de la República, Uruguay (2009) (in Spanish)

  49. Salcedo-Sanzm, S., Xu, Y., Yao, X.: Hybrid meta-heuristics algorithms for task assignment in heterogeneous computing systems. Comput. Oper. Res. 33(3), 820–835 (2006)

    Article  Google Scholar 

  50. Wen, Y., Xu, H., Yang, J.: A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system. Inf. Sci. 181, 567–581 (2011)

    Article  Google Scholar 

  51. Xhafa, F.: A hybrid evolutionary heuristic for job scheduling in computational grids. In: Springer Verlag Series: Studies in Computational Intelligence, vol. 75 (2007). Chap. 10

    Google Scholar 

  52. Xhafa, F., Carretero, J., Abraham, A.: Genetic algorithm based schedulers for grid computing systems. Int. J. Innov. Comput. Inf. Control 3(5), 1–19 (2007)

    Google Scholar 

  53. Xhafa, F., Alba, E., Dorronsoro, B., Duran, B.: Efficient batch job scheduling in grids using cellular memetic algorithms. J. Math. Model. Algorithms 7(2), 217–236 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  54. Xhafa, F., Carretero, J., Alba, E., Dorronsoro, B.: Design and evaluation of tabu search method for job scheduling in distributed environments. In: Proc. of the 22th Int. Par. and Dist. Proc. Symposium, pp. 1–8. IEEE Press, New York (2008)

    Google Scholar 

  55. Ye, G., Rao, R., Li, M.: A multiobjective resources scheduling approach based on genetic algorithms in grid environment. In: Proc. of the 5th Int. Conf. on Grid and Cooperative Computing Workshops, pp. 504–509. IEEE Press, New York (2006)

    Google Scholar 

  56. Yu, J., Kirley, M., Buyya, R.: Multi-objective planning for workflow execution on grids. In: IEEE/ACM Int. Conf. on Grid Computing, pp. 10–17 (2007)

    Google Scholar 

  57. Zhong, L., Long, Z., Zhang, J., Song, H.: An efficient memetic algorithm for job scheduling in computing grid. In: Information and Automation, CCIS, vol. 86, pp. 650–656. Springer, Berlin (2011)

    Chapter  Google Scholar 

  58. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm. Tech. rep. 103, Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, Switzerland (2001)

  59. Zomaya, A., Teh, Y.: Observations on using genetic algorithms for dynamic load-balancing. IEEE Trans. Parallel Distrib. Syst. 12(9), 899–911 (2001)

    Article  Google Scholar 

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Acknowledgements

The work of S. Nesmachnow has been partially supported by Programa de Desarrollo de las Ciencias Básicas (PEDECIBA), Universidad de la República, Uruguay and Agencia Nacional de Investigación e Innovación (ANII), Uruguay.

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    Sergio Nesmachnow

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Nesmachnow, S. Parallel multiobjective evolutionary algorithms for batch scheduling in heterogeneous computing and grid systems. Comput Optim Appl 55, 515–544 (2013). https://doi.org/10.1007/s10589-012-9531-6

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