Skip to main content
SpringerLink
Log in

A new approach to the job scheduling problem in computational grids

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Job scheduling is one of the most challenging issues in Grid resource management that strongly affects the performance of the whole Grid environment. The major drawback of the existing Grid scheduling algorithms is that they are unable to adapt with the dynamicity of the resources and the network conditions. Furthermore, the network model that is used for resource information aggregation in most scheduling methods is centralized or semi-centralized. Therefore, these methods do not scale well as Grid size grows and do not perform well as the environmental conditions change with time. This paper proposes a learning automata-based job scheduling algorithm for Grids. In this method, the workload that is placed on each Grid node is proportional to its computational capacity and varies with time according to the Grid constraints. The performance of the proposed algorithm is evaluated through conducting several simulation experiments under different Grid scenarios. The obtained results are compared with those of several existing methods. Numerical results confirm the superiority of the proposed algorithm over the others in terms of makespan, flowtime, and load balancing.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Tang, M., Lee, B.-S., Tang, X., Yeo, C.-K.: The impact of data replication on job scheduling performance in the Data Grid. Future Gener. Comput. Syst. 22, 254–268 (2006)

    Article  MATH  Google Scholar 

  2. Nakajima, Y., Sato, M., Aida, Y., Boku, T., Cappello, F.: Integrating computing resources on multiple Grid-enabled job scheduling systems through a Grid RPC system. J. Grid Comput. 6(2), 141–157 (2008)

    Article  Google Scholar 

  3. Tchernykh, A., Schwiegelshohn, U., Yahyapour, R., Kuzjurin, N.: On-line hierarchical job scheduling on Grids with admissible Allocation. J. Sched. 13(5), 545–552 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  4. Boyar, J., Favrholdt, L.M.: Scheduling jobs on Grid processors. Algorithmica 57(4), 819–847 (2010)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  6. Cheng, W., Congfeng, J., Xiaohu, L.: Fuzzy logic-based secure and fault tolerant job scheduling in Grid. Tsinghua Sci. Technol. 12(S1), 45–50 (2007)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  8. Liu, H., Abraham, A.: A hybrid fuzzy variable neighborhood particle swarm optimization algorithm for solving quadratic assignment problems. J. Univers. Comput. Sci. 13(7), 1032–1054 (2007)

    Google Scholar 

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

    Article  Google Scholar 

  10. Gao, Y., Rong, H., Zhexue Huang, J.: Adaptive Grid job scheduling with genetic algorithms. Future Gener. Comput. Syst. 21, 151–161 (2005)

    Article  Google Scholar 

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

    Google Scholar 

  12. 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 

  13. de Mello, R.F., Senger, L.J., Yang, L.T.: A routing load balancing policy for Grid computing environments. In: Proceedings of the 20th International Conference on Advanced Information Networking and Applications (AINA 2006), pp. 1–6 (2006)

    Google Scholar 

  14. Bandieramonte, M., Di Stefano, A., Morana, G.: An ACO inspired strategy to improve jobs scheduling in a Grid environment. In: Lecture Notes in Computer Science, vol. 5022, pp. 30–41 (2008)

    Google Scholar 

  15. Chang, R.-S., Changa, J.-S., Lina, P.-S.: An ant algorithm for balanced job scheduling in Grids. Future Gener. Comput. Syst. 25, 20–27 (2009)

    Article  Google Scholar 

  16. Kant, A., Sharma, A., Agarwal, S., Chandra, S.: An ACO approach to job scheduling in Grid environment. In: Lecture Notes in Computer Science, vol. 6466, pp. 286–295 (2010)

    Google Scholar 

  17. Xhafa, F., Carretero, J., Dorronsoro, B., Alba, E.: Tabu Search algorithm for scheduling independent jobs in computational Grids. Comput. Inform. J. 28(2), 237–249 (2009)

    Google Scholar 

  18. Xhafa, F., Gonzalez, J.A., Dahal, K.P., Abraham, A.: A GA(TS) hybrid algorithm for scheduling in computational grids. In: Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science, vol. 5572, pp. 285–292 (2009)

    Chapter  Google Scholar 

  19. Abraham, A., Buyya, R., Nath, B.: Nature’s heuristics for scheduling jobs on computational Grids. In: Proceedings of the 8th IEEE International Conference on Advanced Computing and Communications, India (2000)

    Google Scholar 

  20. YarKhan, A., Dongarra, J.: Experiments with scheduling using simulated annealing in a Grid environment. In: Proceedings of GRID2002, pp. 232–242 (2002)

    Google Scholar 

  21. Xhafa, F.: A hybrid evolutionary heuristic for job scheduling in computational Grids. In: Studies in Computational Intelligence, vol. 75. Springer, Berlin (2007) (Chap. 10)

    Google Scholar 

  22. 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 

  23. Wu, J., Xu, X., Zhang, P., Liu, C.: A novel multi-agent reinforcement learning approach for job scheduling in Grid computing. Future Gener. Comput. Syst. 27, 430–439 (2011)

    Article  Google Scholar 

  24. Ramírez-Alcaraz, J.M., Tchernykh, A., Yahyapour, R., Schwiegelshohn, U., Quezada-Pina, A., González-García, J.L., Hirales-Carbajal, A.: Job allocation strategies with user run time estimates for online scheduling in hierarchical Grids. J. Grid Comput. 9(1), 95–116 (2011). doi:10.1007/s10723-011-9179-y

    Article  Google Scholar 

  25. Ghosh, P., Das, S.K.: Mobility-aware cost-efficient job scheduling for single-class Grid jobs in a generic mobile Grid architecture. Future Gener. Comput. Syst. 26, 1356–1367 (2010)

    Article  Google Scholar 

  26. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-completeness. Freeman, New York (1979)

    MATH  Google Scholar 

  27. Narendra, K.S., Thathachar, K.S.: Learning Automata: An Introduction. Prentice-Hall, New York (1989)

    Google Scholar 

  28. Thathachar, M.A.L., Harita, B.R.: Learning automata with changing number of actions. IEEE Trans. Syst. Man Cybern. SMG17, 1095–1100 (1987)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Young Researchers Club, Arak Branch, Islamic Azad University, Arak, Iran

    Javad Akbari Torkestani

Authors
  1. Javad Akbari Torkestani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Javad Akbari Torkestani.

Appendix

Appendix

Table 2 List of acronyms
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Akbari Torkestani, J. A new approach to the job scheduling problem in computational grids. Cluster Comput 15, 201–210 (2012). https://doi.org/10.1007/s10586-011-0192-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-011-0192-5

Keywords

Search

Navigation