Skip to main content

Advertisement

Springer Nature Link
Log in

Task scheduling on computational Grids using Gravitational Search Algorithm

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Grid computing uses distributed interconnected computers and resources collectively to achieve higher performance computing and resource sharing. Task scheduling is one of the core steps to efficiently exploit the capabilities of Grid environment. Recently, heuristic algorithms have been successfully applied to solve task scheduling on computational Grids. In this paper, Gravitational Search Algorithm (GSA), as one of the latest population-based metaheuristic algorithms, is used for task scheduling on computational Grids. The proposed method employs GSA to find the best solution with the minimum makespan and flowtime. We evaluate this approach with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) method. The results demonstrate that the benefit of the GSA is its speed of convergence and the capability to obtain feasible schedules.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Abraham, A., Liu, H., Zhang, W., Chang, T.-G.: Scheduling jobs on computational grids using fuzzy particle swarm algorithm. In: Knowledge-Based Intelligent Information and Engineering Systems, pp. 500–507. Springer, Berlin (2006)

    Chapter  Google Scholar 

  2. Akbari Torkestani, J.: A new approach to the job scheduling problem in computational grids. Clust. Comput. 15(3), 201–210 (2012)

    Article  Google Scholar 

  3. Ali, S., Siegel, H.J., Maheswaran, M., Hensgen, D., Ali, S.: Representing task and machine heterogeneities for heterogeneous computing systems. Tamkang J. Sci. Eng. 3(3), 195–208 (2000)

    Google Scholar 

  4. Ali, S., Braun, T.D., Siegel, H.J., Maciejewski, A.A., Beck, N., Bölöni, L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., et al.: Characterizing resource allocation heuristics for heterogeneous computing systems. Adv. Comput. 63, 91–128 (2005)

    Article  Google Scholar 

  5. Bandieramonte, M., Di Stefano, A., Morana, G.: An ACO inspired strategy to improve jobs scheduling in a grid environment. In: Algorithms and Architectures for Parallel Processing, pp. 30–41. Springer, Berlin (2008)

    Chapter  Google Scholar 

  6. Braun, T.D., Siegel, H.J., Beck, N., Bölöni, L.L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Yao, B., Hensgen, D., et al.: 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 

  7. Carretero, J., Xhafa, F.: Use of genetic algorithms for scheduling jobs in large scale grid applications. Technol. Econ. Dev. Econ. 12(1), 11–17 (2006)

    Google Scholar 

  8. Carretero, J., Xhafa, F., Abraham, A.: Genetic algorithm based schedulers for grid computing systems. Int. J. Innov. Comput. Inf. Control 3(6), 1–19 (2007)

    Google Scholar 

  9. Chang, R.-S., Chang, J.-S., Lin, P.-S.: An ant algorithm for balanced job scheduling in grids. Future Gener. Comput. Syst. 25(1), 20–27 (2009)

    Article  Google Scholar 

  10. Chen, W.-N., Zhang, J.: An ant colony optimization approach to a grid workflow scheduling problem with various QoS requirements. IEEE Trans. Syst. Man Cybern., Part C, Appl. Rev. 39(1), 29–43 (2009)

    Article  Google Scholar 

  11. de Mello, R.F., Andrade Filho, J.A., Senger, L.J., Yang, L.T.: Grid job scheduling using route with genetic algorithm support. Telecommun. Syst. 38(3–4), 147–160 (2008)

    Article  Google Scholar 

  12. Di Martino, V., Mililotti, M.: Scheduling in a grid computing environment using genetic algorithms. In: IPDPS (2002)

    Google Scholar 

  13. Di Martino, V., Mililotti, M.: Sub optimal scheduling in a grid using genetic algorithms. Parallel Comput. 30(5), 553–565 (2004)

    Article  Google Scholar 

  14. Dong, F., Akl, S.G.: Scheduling algorithms for grid computing: state of the art and open problems. School of Computing, Queens University, Kingston, Ontario (2006)

  15. Dorigo, M.: Optimization, learning and natural algorithms. Ph.D. thesis, Politecnico di Milano, Italy (1992)

  16. Gao, Y., Rong, H., Huang, J.Z.: Adaptive grid job scheduling with genetic algorithms. Future Gener. Comput. Syst. 21(1), 151–161 (2005)

    Article  Google Scholar 

  17. Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13(5), 533–549 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  18. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning (1989)

    MATH  Google Scholar 

  19. Kant, A., Sharma, A., Agarwal, S., Chandra, S.: An ACO approach to job scheduling in grid environment. In: Swarm, Evolutionary, and Memetic Computing, pp. 286–295. Springer, Berlin (2010)

    Chapter  Google Scholar 

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

    Google Scholar 

  21. Kirkpatrick, S., Gelatt, D. Jr., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MATH  MathSciNet  Google Scholar 

  22. Liu, D., Cao, Y.: A chaotic genetic algorithm for fuzzy grid job scheduling. In: 2006 International Conference on Computational Intelligence and Security, vol. 1, pp. 320–323. IEEE Press, New York (2006)

    Chapter  Google Scholar 

  23. Liu, H., Abraham, A., Hassanien, A.E.: Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm. Future Gener. Comput. Syst. 26(8), 1336–1343 (2010)

    Article  Google Scholar 

  24. Lorpunmanee, S., Noor Sap, M., Hanan Abdullah, A., Chompoo-inwai, C.: An ant colony optimization for dynamic job scheduling in grid environment. Int. J. Comput. Inf. Sci. Eng. 1(4), 207–214 (2007)

    Google Scholar 

  25. Page, A.J., Naughton, T.J.: Framework for task scheduling in heterogeneous distributed computing using genetic algorithms. Artif. Intell. Rev. 24(3–4), 415–429 (2005)

    Article  Google Scholar 

  26. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)

    Article  MATH  Google Scholar 

  27. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: BGSA: binary gravitational search algorithm. Nat. Comput. 9(3), 727–745 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  28. Ritchie, G., Levine, J.: A Hybrid Ant Algorithm for Scheduling Independent Jobs in Heterogeneous Computing Environments (2004)

    Google Scholar 

  29. Siddiqui, M., Fahringer, T.: Grid Resource Management: On-demand Provisioning, Advance Reservation, and Capacity Planning of Grid Resources. Springer, Berlin (2010). LNCS sublibrary. SL 1. Theoretical computer science and general issues. ISBN 9783642115783

    Book  Google Scholar 

  30. Song, S., Hwang, K., Kwok, Y.-K.: Risk-resilient heuristics and genetic algorithms for security-assured grid job scheduling. IEEE Trans. Comput. 55(6), 703–719 (2006)

    Article  Google Scholar 

  31. Steuer, R.E.: Multiple Criteria Optimization: Theory, Computation, and Application. Wiley, New York (1986)

    MATH  Google Scholar 

  32. Sudha Sadasivam, G., Viji Rajendran, V.: An efficient approach to task scheduling in computational grids. Int. J. Comput. Sci. Appl. 6(1), 53–69 (2009)

    Google Scholar 

  33. Talbi, E.-G.: A taxonomy of hybrid metaheuristics. J. Heuristics 8(5), 541–564 (2002)

    Article  Google Scholar 

  34. Tao, Q., Chang, H.-y., Yi, Y., Gu, C.-q., Li, W.-j.: A rotary chaotic PSO algorithm for trustworthy scheduling of a grid workflow. Comput. Oper. Res. 38(5), 824–836 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  35. Wilkinson, B.: Grid Computing: Techniques and Applications. Chapman & Hall/CRC Press/Taylor & Francis, London/Boca Raton/London (2011). ISBN 9781420069549

    Google Scholar 

  36. Xhafa, F., Abraham, A.: Computational models and heuristic methods for grid scheduling problems. Future Gener. Comput. Syst. 26(4), 608–621 (2010)

    Article  Google Scholar 

  37. Xhafa, F., Barolli, L., Durresi, A.: Batch mode scheduling in grid systems. Int. J. Web Grid Serv. 3(1), 19–37 (2007)

    Article  Google Scholar 

  38. Xhafa, F., Carretero, J., Barolli, L., Durresi, A.: Immediate mode scheduling in grid systems. Int. J. Web Grid Serv. 3(2), 219–236 (2007)

    Article  Google Scholar 

  39. Xhafa, F., Duran, B., Abraham, A., Dahal, K.P.: Tuning struggle strategy in genetic algorithms for scheduling in computational grids. In: Computer Information Systems and Industrial Management Applications, pp. 275–280. IEEE Press, New York (2008)

    Google Scholar 

  40. Xhafa, F., Gonzalez, J.A., Dahal, K.P., Abraham, A.: A GA (TS) hybrid algorithm for scheduling in computational grids. In: Hybrid Artificial Intelligence Systems, pp. 285–292. Springer, Berlin (2009)

    Chapter  Google Scholar 

  41. Xhafa, F., Carretero, J., Dorronsoro, B., Alba, E.: A tabu search algorithm for scheduling independent jobs in computational grids. Comput. Inform. 28(2), 237–250 (2012)

    Google Scholar 

  42. Yan, H., Shen, X.-Q., Li, X., Wu, M.-H.: An improved ant algorithm for job scheduling in grid computing. In: Proceedings of 2005 International Conference on Machine Learning and Cybernetics, vol. 5, pp. 2957–2961. IEEE Press, New York (2005)

    Chapter  Google Scholar 

  43. YarKhan, A., Dongarra, J.J.: Experiments with scheduling using simulated annealing in a grid environment. In: Grid Computing—GRID 2002, pp. 232–242. Springer, Berlin (2002)

    Chapter  Google Scholar 

  44. Zarrabi, A., Samsudin, K., Wan Adnan, W.A.: Linux support for fast transparent general purpose checkpoint/restart of multithreaded processes in loadable kernel module. J. Grid Comput. 11(2), 187–210 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer and Communication Systems Engineering, Faculty of Engineering, University Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia

    Amirreza Zarrabi & Khairulmizam Samsudin

Authors
  1. Amirreza Zarrabi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Khairulmizam Samsudin
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Amirreza Zarrabi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zarrabi, A., Samsudin, K. Task scheduling on computational Grids using Gravitational Search Algorithm. Cluster Comput 17, 1001–1011 (2014). https://doi.org/10.1007/s10586-013-0338-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-013-0338-8

Keywords