A scheduling algorithm using sub-deadline for workflow applications under budget and deadline constrained

Abstract

With the development of the cloud and grid computing, the cloud infrastructures and grids provide a platform for workflow applications. It is very essential to meet the requirements of users and to complete workflow scheduling efficiently. The scheduling of the workflow is limited by quality of service (QoS) parameters. Many scheduling algorithms have been proposed for the execution of workflow applications using QoS parameters. In this study, we improved a scheduling algorithm that considers workflow applications under budget and deadline constraints. This algorithm provided a simple way to deal with the deadline and budget constraints. The algorithm was named BDSD and used to find a scheduling that satisfies of deadline and budget constraints required by a user. The planning success rate (PSR) was utilized to show the effectiveness of the proposed algorithm. For the simulation experiment, random and real workflow applications were exploited. Experimental results showed that compared with other algorithms the algorithm had a higher PSR.

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References

  1. 1.

    Yu, J., Buyya, R., Ramamohanarao, K.: Workflow scheduling algorithms for grid computing. Metaheuristics Sched. Distrib. Comput. Environ. 146, 173–214 (2008)

    Article  Google Scholar 

  2. 2.

    Prodan, R., Wieczorek, M.: Bi-criteria scheduling of scientific grid workflows. IEEE Trans. Autom. Sci. Eng. 7(2), 364–376 (2010)

    Article  Google Scholar 

  3. 3.

    Coffman, E.G., Bruno, J.L.: Computer and Job-shop Scheduling Theory. Wiley, New York (1976)

    Google Scholar 

  4. 4.

    Ullman, J.D.: Np-complete scheduling problems. J. Comput. Syst. Sci. 10(3), 384–393 (1975)

    MathSciNet  Article  Google Scholar 

  5. 5.

    Wu, F., Wu, Q., Tan, Y.: Workflow scheduling in cloud: a survey. J. Supercomput. 71(9), 3373–3418 (2015)

    Article  Google Scholar 

  6. 6.

    Germain-Renaud, C., Rana, O.: The convergence of clouds, grids, and autonomics. IEEE Internet Comput. 13(6), 9–9 (2009)

    Article  Google Scholar 

  7. 7.

    Li, J., Qiu, M., Ming, Z., Quan, G., Qin, X., Gu, Z.: Online optimization for scheduling preemptable tasks on IaaS cloud systems. J. Parallel Distrib. Comput. 72(5), 666–677 (2012)

    Article  Google Scholar 

  8. 8.

    Qiu, M., Ming, Z., Li, J., Gai, K., Zong, Z.: Phase-change memory optimization for green cloud with genetic algorithm. IEEE Trans. Comput. 64(12), 3528–3540 (2015)

    MathSciNet  Article  Google Scholar 

  9. 9.

    Gai, K., Qiu, M.K., Zhao, H.: Cost-aware multimedia data allocation for heterogeneous memory using genetic algorithm in cloud computing. IEEE Trans. Cloud Comput. 99, 1–1 (2016)

    Article  Google Scholar 

  10. 10.

    Xu, X.J., Xiao, C.B., Tian, G.Z., Sun, T.: Expansion slot backfill scheduling for concurrent workflows with deadline on heterogeneous resources. Clust. Comput. 20(1), 471–483 (2017)

    Article  Google Scholar 

  11. 11.

    Arabnejad, H., Barbosa, J., Prodan, R.: Low-time complexity budget-deadline constrained workflow scheduling on heterogeneous resources. Future Gener. Comput. Syst. 55, 29–40 (2016)

    Article  Google Scholar 

  12. 12.

    Zhang, W., Sakellariou, R.: Budget-deadline constrained workflow planning for admission control. J. Grid Comput. 11(4), 633–651 (2013)

    Article  Google Scholar 

  13. 13.

    Arabnejad, H., Barbosa, J.G.: A budget constrained scheduling algorithm for workflow applications. J. Grid Comput. 12(4), 665–679 (2014)

    Article  Google Scholar 

  14. 14.

    Topcuoglu, H., Hariri, S., Wu, M.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)

    Article  Google Scholar 

  15. 15.

    Yu, J., Ramamohanarao, K., Buyya, R.: Deadline/budget-based scheduling of workflows on utility grids. Market-Oriented Grid Util. Comput. 200(9), 427–450 (2009)

    Article  Google Scholar 

  16. 16.

    Yu, J., Buyya, R., Tham, C.K.: Cost-based scheduling of scientific workflow applications on utility grids. In: First International Conference on e-Science and Grid Computing, IEEE, pp. 1–8 (2005)

  17. 17.

    Yu, J., Buyya, R., Tham, C.K.: QoS-based scheduling of workflow applications on service grids. In: Proceedings of 1st IEEE International Conference-Science and Grid Computing, pp. 5–8 (2005)

  18. 18.

    Casanova, H., Legrand A., Quinson, M.: Simgrid: A Generic Framework for Large-scale Distributed Experiments. In Proceedings of the 10th International Conference on Computer Modeling and Simulation, UKSIM 2008, IEEE, pp. 126–131 (2008)

  19. 19.

    Sakellariou, R., Zhao, H.: A hybrid heuristic for DAG scheduling on heterogeneous systems. Parallel and Distributed Processing Symposium, Proceedings. 18th International. IEEE, vol. 111 (2004)

  20. 20.

    Zheng, W., Sakellariou, R.: Budget-deadline constrained workflow planning for admission control. J. Grid Comput. 11(4), 633–651 (2013)

    Article  Google Scholar 

  21. 21.

    Yu, J., Buyya, R.: Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci. Program 14(3), 217–230 (2006)

    Google Scholar 

  22. 22.

    Wu, Z., Ni, Z., Gu, L., Liu, X.: A revised discrete particle swarm optimization for cloud workflow scheduling. In: Proceedings of 2010 international conference on computational intelligence and security (CIS), IEEE, pp. 184–188 (2010)

  23. 23.

    Rodriguez, M., Buyya, R.: Deadline based resource provisioning and scheduling algorithmfor scientific workflows on clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014)

    Article  Google Scholar 

  24. 24.

    Yuan, Y., Li, X., Wang, Q., Zhang, Y.: Bottom level based heuristic for workflow scheduling in grids. Chin. J. Comput. Chin. 31(2), 282 (2008)

    Article  Google Scholar 

  25. 25.

    Yuan, Y., Li, X., Wang, Q., Zhu, X.: Deadline division-based heuristic for cost optimization inworkflow scheduling. Inf. Sci. 179(15), 2562–2575 (2009)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Beijing Natural Science Foundation (4162007) and Natural Science Foundation of China (61501008).

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Correspondence to Ting Sun.

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This paper has been presented in the 4th IEEE International Conference on Cyber Security and Cloud Computing (IEEE cscloud 2017).

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Sun, T., Xiao, C. & Xu, X. A scheduling algorithm using sub-deadline for workflow applications under budget and deadline constrained. Cluster Comput 22, 5987–5996 (2019). https://doi.org/10.1007/s10586-018-1751-9

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Keywords

  • Scheduling
  • Sub-deadline
  • Quality of service
  • Planning success rate
  • Workflow application