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Multi-objective Grid Scheduling

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Automated Scheduling and Planning

Part of the book series: Studies in Computational Intelligence ((SCI,volume 505))

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Abstract

Grid computing is a distributed paradigm that coordinates heterogeneous resources using decentralized control. Grid computing is commonly used by scientists for executing experiments. Scheduling jobs within Grid environments is a challenging task. Scientists often need to ensure not only a successful execution for their experiments but also they have to satisfy constraints such as deadlines or budgets. Both of these constraints, execution time and cost, are not trivial to satisfy, as they are conflict with each other, eg cheaper resources are usually slower than expensive ones. Hence, a multi-objective scheduling optimization is a more challenging task in Grid infrastructures. This chapter presents a new multi-objective approach, MOGSA (Multi-Objective Gravitational Search Algorithm), based on the gravitational search behaviour in order to optimize both objectives, execution time and cost, with the same importance and also at the same time. Two studies are carried out in order to evaluate the quality of this new approach for grid scheduling. Firstly, MOGSA is compared with the multiobjective standard and well-known NSGA-II (Non-Dominated Sorting Genetic Algorithm II) to prove the multi-objective optimization suitability of the proposed algorithm. Secondly two real grid schedulers (WMS and DBC) are also compared with MOGSA. TheWMS (WorkloadManagement System) is considered because of it is part of the most used European grid middleware - gLite - and also the DBC (Deadline Budget Constraint) algorithm from Nimrod-G participates in this evaluation due to its good performance keeping the deadline and budget per job. Results point out the superiority of MOGSA in all the studies carried out. MOGSA offers more quality solutions than NSGA-II and also better performance than current real schedulers.

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References

  1. Amorim, P., Günther, H.O., Almada-Lobo, B.: Multi-objective integrated production and distribution planning of perishable products. International Journal of Production Economics 138(1), 89–101 (2012)

    Article  Google Scholar 

  2. Buyya, R., Murshed, M.: Gridsim: a toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. Concurrency and Computation: Practice and Experience 14(13), 1175–1220 (2002)

    Article  MATH  Google Scholar 

  3. Buyya, R., Murshed, M., Abramson, D.: A deadline and budget constrained cost-time optimisation algorithm for scheduling task farming applications on global grids. In: Int. Conf. on Parallel and Distributed Processing Techniques and Applications, Las Vegas, Nevada, USA, pp. 2183–2189 (2002)

    Google Scholar 

  4. Castro, C., Crawford, B., Monfroy, E.: A genetic local search algorithm for the multiple optimisation of the balanced academic curriculum problem. In: Shi, Y., Wang, S., Peng, Y., Li, J., Zeng, Y. (eds.) MCDM 2009. CCIS, vol. 35, pp. 824–832. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  5. Coello, C., Van Veldhuizen, D., Lamont, G.: Evolutionary Algorithms for Solving Multi-Objective Problems. In: Genetic Algorithms and Evolutionary Computation. Kluwer (2002)

    Google Scholar 

  6. Côté, P., Wong, T., Sabourin, R.: Application of a hybrid multi-objective evolutionary algorithm to the uncapacitated exam proximity problem. In: Proceedings of the 5th International Conference on Practice and Theory of Automated Timetabling, pp. 151–167 (2004)

    Google Scholar 

  7. Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons (2001)

    Google Scholar 

  8. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast elitist multi-objective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)

    Article  Google Scholar 

  9. Foster, I., Kesselman, C., Tuecke, S.: The anatomy of the grid. In: Grid Computing-Making the Global Infrastructure a Reality. John Wiley Sons (2010)

    Google Scholar 

  10. Hamta, N., Ghomi, S.F., Jolai, F., Shirazi, M.A.: A hybrid pso algorithm for a multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect. International Journal of Production Economics (2012)

    Google Scholar 

  11. Ismayilova, N.A., Sagir, M., Gasimov, R.N.: A multiobjective faculty-course-time slot assignment problem with preferences. Mathematical and Computer Modelling 46(7-8), 1017–1029 (2007)

    Article  MathSciNet  Google Scholar 

  12. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  13. Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.): EMO 2003. LNCS, vol. 2632. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  14. Lei, D.: Multi-objective production scheduling: a survey. The International Journal of Advanced Manufacturing Technology 43(9-10), 926–938 (2009)

    Article  Google Scholar 

  15. Li, J., Burke, E.K., Curtois, T., Petrovic, S., Qu, R.: The falling tide algorithm: A new multi-objective approach for complex workforce scheduling. Omega 40(3), 283–293 (2012)

    Article  Google Scholar 

  16. Loukil, T., Teghem, J., Fortemps, P.: A multi-objective production scheduling case study solved by simulated annealing. European Journal of Operational Research 179(3), 709–722 (2007)

    Article  MATH  Google Scholar 

  17. Mansouri, S.A., Gallear, D., Askariazad, M.H.: Decision support for build-to-order supply chain management through multiobjective optimization. International Journal of Production Economics 135(1), 24–36 (2012)

    Article  Google Scholar 

  18. Mobasher, A.: Nurse scheduling optimization in a general clinic and an operating suite. PhD thesis, University of Houston (2012)

    Google Scholar 

  19. El Moudani, W., Cosenza, C.A.N., de Coligny, M., Mora-Camino, F.: A bi-criterion approach for the airlines crew rostering problem. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 486–500. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  20. Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems, 4th edn. Prentice-Hall (2012)

    Google Scholar 

  21. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: Gsa: A gravitational search algorithm. Information Sciences 179(13), 2232–2248 (2009)

    Article  MATH  Google Scholar 

  22. Silva, A., Burke, E.K.: A tutorial on multiobjective metaheuristics for scheduling and timetabling. In: Multiple Objective Meta-Heuristics. LNEMS. Springer (2004)

    Google Scholar 

  23. Silva, A., Burke, E.K., Petrovic, S.: An introduction to multiobjective metaheuristics for scheduling and timetabling. In: Grandibleux, X., Sevaux, M., Sörensen, K., T’Kindt, V. (eds.) Metaheuristic for Multiobjective Optimisation. LNEMS, vol. 535, pp. 91–129. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  24. Sulistio, A., Poduval, G., Buyya, R., Tham, C.: On incorporating differentiated levels of network service into gridsim. Future Gener. Comput. Syst. 23(4), 606–615 (2007)

    Article  Google Scholar 

  25. Talukder, A.K.A., Kirley, M., Buyya, R.: Multiobjective differential evolution for workflow execution on grids. In: MGC 2007: Proceedings of the 5th International Workshop on Middleware for Grid Computing, pp. 1–6. ACM, New York (2007)

    Google Scholar 

  26. Talukder, A.K.A., Kirley, M., Buyya, R.: Multiobjective differential evolution for scheduling workflow applications on global grids. Concurr. Comput. Pract. Exper. 21(13), 1742–1756 (2009)

    Article  Google Scholar 

  27. Tsuchiya, T., Osada, T., Kikuno, T.: Genetics-based multiprocessor scheduling using task duplication. Microprocessors and Microsystems 22(3-4), 197–207 (1998)

    Article  Google Scholar 

  28. Xiong, J., Xing, L., Chen, Y.: Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns. International Journal of Production Economics (2012)

    Google Scholar 

  29. Yannibelli, V., Amandi, A.: Project scheduling: A multi-objective evolutionary algorithm that optimizes the effectiveness of human resources and the project makespan. Engineering Optimization, 1–21 (2012)

    Google Scholar 

  30. Ye, G., Rao, R., Li, M.: A multiobjective resources scheduling approach based on genetic algorithms in grid environment. In: International Conference on Grid and Cooperative Computing Workshops, pp. 504–509 (2006)

    Google Scholar 

  31. Yu, J., Kirley, M., Buyya, R.: Multi-objective planning for workflow execution on grids. In: GRID 2007: Proceedings of the 8th IEEE/ACM International Conference on Grid Computing, pp. 10–17. IEEE Computer Society, Washington, DC (2007)

    Chapter  Google Scholar 

  32. Zeng, B., Wei, J., Wang, W., Wang, P.: Cooperative grid jobs scheduling with multi-objective genetic algorithm. In: Stojmenovic, I., Thulasiram, R.K., Yang, L.T., Jia, W., Guo, M., de Mello, R.F. (eds.) ISPA 2007. LNCS, vol. 4742, pp. 545–555. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  33. Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms - a comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–304. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Beams Department, European Organization for Nuclear Research, CERN, CH-1211, Geneva 23, Switzerland

    María Arsuaga-Ríos

  2. ARCO Research Group, University of Extremadura, Badajoz, Spain

    Miguel A. Vega-Rodríguez

  3. Dept. Technologies of Computers and Communications, Escuela Politécnica, Cáceres, Spain

    Miguel A. Vega-Rodríguez

Authors
  1. María Arsuaga-Ríos
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  2. Miguel A. Vega-Rodríguez
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Correspondence to María Arsuaga-Ríos .

Editor information

Editors and Affiliations

  1. , Elektrik-Elektronik Fakultesi, Istanbul Teknik Universitesi, Istanbul, 34469, Turkey

    A. Sima Uyar

  2. , School of Computer Science, University of Nottingham, Wollaton Road, Nottingham, NG8 1BB, United Kingdom

    Ender Ozcan

  3. , Centre for Emergent Computing, Edinburgh Napier University, 10 Colinton Road, Edinburgh, EH10 5DT, United Kingdom

    Neil Urquhart

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Arsuaga-Ríos, M., Vega-Rodríguez, M.A. (2013). Multi-objective Grid Scheduling. In: Uyar, A., Ozcan, E., Urquhart, N. (eds) Automated Scheduling and Planning. Studies in Computational Intelligence, vol 505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39304-4_9

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  • DOI: https://doi.org/10.1007/978-3-642-39304-4_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39303-7

  • Online ISBN: 978-3-642-39304-4

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