Skip to main content
SpringerLink
Log in

A Workflow Scheduling Method for Cloud Computing Platform

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Scheduling in computing environments such as homogeneous and heterogonous is very challenging and faces various difficulties computationally. This computing needs an optimal method that decides how to allocate and execute the tasks on a computing platform, so, it generates an efficient result. Here, the tasks are connected to each other and depicted using DAG  which is extensively used in Cloud Scheduling modeling. Generally, cloud work on principal pay per resources uses basis. This paper presents a new scheme for scheduling of the tasks  in a cloud platform. The proposed algorithm uses heuristic-guided Breadth-First Search (BFS) which works on two steps process as first it the priority computation of the tasks and second is to assign these tasks to  the available virtual machines with an entry task as duplicate to all virtual machines. This leads to reducing the scheduling length of the task scheduling and it is the prime with workflow scheduling algorithm. This paper also discussed performance analysis of the new method with the heuristic algorithms using various well known metrics. The proposed method gives better results than the state of the art.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Data availability

(1) K. Chitharanjan and R. SenthilKumar, “A study of resource allocation techniques in cloud computing,” Int. J. Bus. Inf. Syst., vol. 36, no. 2, pp. 254–269, 2021. (2) G. Demirci, I. Marincic, and H. Hoffmann, “A divide and conquer algorithm for dag scheduling under power constraints,” in SC18: International Conference for High Performance Computing, Networking, Storage and Analysis, 2018, pp. 466–477. (3) D. M. Batista and N. L. S. da Fonseca, “Scheduling grid tasks in face of uncertain communication demands,” IEEE Trans. Netw. Serv. Manag., vol. 8, no. 2, pp. 92–103, 2011. (4) M. S. Kumar, I. Gupta and P. K. Jana, "Delay-based workflow scheduling for cost optimization in heterogeneous cloud system," 2017 Tenth International Conference on Contemporary Computing (IC3), Noida,(2017) pp. 1–6. (5) Gupta, I.; Kumar, M.S.; Jana, P.K.: Efficient workflow scheduling algorithm for cloud computing system: a dynamic priority-based approach. Arabian Journal for Science and Engineering,Vol.43, No.12,(2018) pp 7945–7960. (6) Nidhi Rajak and Diwakar Shukla,” Performance Analysis of Workflow Scheduling Algorithm in Cloud Computing Environment using Priority Attribute” International Journal of Advanced Science and Technology, Australia,Vol. 28, No. 16, (2019), pp. 1810 – 1831.

Code availability

http://www.cloudbus.org/gridsim/

References

  1. Cooper, K., Dasgupta, A., Kennedy, K., Koelbel, C., Mandal, A., Marin, G., Mazina, M., Mellor-Crummey, J., Berman, F., Casanova, H., & Chien, A. (2004). New grid scheduling and rescheduling methods in the GrADS project, In 18th international parallel and distributed processing symposium, 2004. Proceedings, (p. 199).

  2. Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., & Vahi, K. (2013). Characterizing and profiling scientific workflows. Future Generation Computer Systems, 29(3), 682–692.

    Article  Google Scholar 

  3. Nasr, A. A., El-Bahnasawy, N. A., Attiya, G., & El-Sayed, A. (2019). Cost-effective algorithm for workflow scheduling in cloud computing under deadline constraint. Arabian Journal for Science and Engineering, 44(4), 3765–3780.

    Article  Google Scholar 

  4. Wieczorek, M., Prodan, R., & Fahringer, T. (2005). Scheduling of scientific workflows in the ASKALON grid environment. ACM SIGMOD Record, 34(3), 56–62.

    Article  Google Scholar 

  5. Kannan, R., & Karpinski, M. (2005). Approximation algorithms for NP-hard problems. Oberwolfach Reports, 1(3), 1461–1540.

    MathSciNet  MATH  Google Scholar 

  6. Woeginger, G. J. (2003). Exact algorithms for NP-hard problems: A survey, In Combinatorial optimization—eureka, you shrink!, (pp. 185–207) Springer.

  7. Hanen, C. (1994). Study of a NP-hard cyclic scheduling problem: The recurrent job-shop. European Journal of Operational Research, 72(1), 82–101.

    Article  Google Scholar 

  8. Kwok, Y.-K., & Ahmad, I. (1999). Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Computing Surveys, 31(4), 406–471.

    Article  Google Scholar 

  9. Tsai, C.-W., Huang, W.-C., Chiang, M.-H., Chiang, M.-C., & Yang, C.-S. (2014). A hyper-heuristic scheduling algorithm for cloud. IEEE Transactions on Cloud Computing, 2(2), 236–250.

    Article  Google Scholar 

  10. Xu, M., Cui, L., Wang, H., & Bi, Y. (2009). A multiple QoS constrained scheduling strategy of multiple workflows for cloud computing, In 2009 IEEE international symposium on parallel and distributed processing with applications, (pp. 629–634).

  11. Rajak, R. (2018). Deterministic task scheduling method in multiprocessor environment, In International conference on advances in computing and data sciences, (pp. 331–341).

  12. Bansal, N. & Singh, A. K. (2020). Grey wolf optimized task scheduling algorithm in cloud computing, In Frontiers in intelligent computing: theory and applications, (pp. 137–145) Springer.

  13. Rajak, R., Shukla, D., & Alim, A. (2018) Modified critical path and top-level attributes (MCPTL)-based task scheduling algorithm in parallel computing, In Soft computing: theories and applications, (pp. 1–13) Springer.

  14. Xu, X.-J., Xiao, C.-B., Tian, G.-Z., Sun, T. (2016). Hybrid scheduling deadline-constrained multi-DAGs based on reverse HEFT, In 2016 international conference on information system and artificial intelligence (ISAI), (pp. 196–202)

  15. Zhou, J., Wang, T., Cong, P., Lu, P., Wei, T., & Chen, M. (2019). Cost and makespan-aware workflow scheduling in hybrid clouds. Journal of Systems Architecture, 100, 101631. https://doi.org/10.1016/j.sysarc.2019.08.004

    Article  Google Scholar 

  16. Durillo, J. J., Prodan, R., & Barbosa, J. G. (2015). Pareto tradeoff scheduling of workflows on federated commercial clouds. Simulation Modelling Practice and Theory, 58, 95–111.

    Article  Google Scholar 

  17. Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. MIT Press.

    MATH  Google Scholar 

  18. Topcuoglu, H., Hariri, S., & Wu, M.-Y. (2002). Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems, 13(3), 260–274.

    Article  Google Scholar 

  19. Chitharanjan, K., & SenthilKumar, R. (2021). A study of resource allocation techniques in cloud computing. International Journal of Business Information Systems, 36(2), 254–269.

    Article  Google Scholar 

  20. Tong, Z., Chen, H., Deng, X., Li, K., & Li, K. (2020). A scheduling scheme in the cloud computing environment using deep Q-learning. Infornation Sciences (Ny), 512, 1170–1191.

    Article  Google Scholar 

  21. Du, J., & Leung, J.Y.-T. (1989). Complexity of scheduling parallel task systems. SIAM Journal on Discrete Mathematics, 2(4), 473–487.

    Article  MathSciNet  Google Scholar 

  22. da Silva, E. C., & Gabriel, P. H. R. (2020). A Comprehensive review of evolutionary algorithms for multiprocessor DAG scheduling. Computation, 8(2), 26.

    Article  Google Scholar 

  23. Pop, F., Dobre, C., & Cristea, V. (2008) Performance analysis of grid DAG scheduling algorithms using MONARC simulation tool, In 2008 international symposium on parallel and distributed computing, (pp. 131–138)

  24. Bozdag, D., Ozguner, F., & Catalyurek, U. V. (2008). Compaction of schedules and a two-stage approach for duplication-based DAG scheduling. IEEE Transactions on Parallel and Distributed Systems, 20(6), 857–871.

    Article  Google Scholar 

  25. Hochba, D. S. (1997). Approximation algorithms for NP-hard problems. ACM SIGACT News, 28(2), 40–52.

    Article  Google Scholar 

  26. Demirci, G., Marincic, I., & Hoffmann, H. (2018). A divide and conquer algorithm for dag scheduling under power constraints, In SC18: international conference for high performance computing, networking, storage and analysis, (pp. 466–477).

  27. Hosseinzadeh, M., Ghafour, M. Y., Hama, H. K., Vo, B., & Khoshnevis, A. (2020). Multi-objective task and workflow scheduling approaches in cloud computing: a comprehensive review. Journal of Grid Computing, 18, 1–30.

    Article  Google Scholar 

  28. Epstein, L., & Tassa, T. (2006). Optimal preemptive scheduling for general target functions. Journal of Computer and System Sciences, 72(1), 132–162.

    Article  MathSciNet  Google Scholar 

  29. Xu, Y., Li, K., Hu, J., & Li, K. (2014). A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Information Sciences (Ny), 270, 255–287.

    Article  MathSciNet  Google Scholar 

  30. Omara, F. A. & Arafa, M. M. (2009). Genetic algorithms for task scheduling problem, In Foundations of computational intelligence, (vol 3, pp. 479–507) Springer.

  31. Kalra, M., & Singh, S. (2015). A review of metaheuristic scheduling techniques in cloud computing. Egyptian Informatics Journal, 16(3), 275–295.

    Article  Google Scholar 

  32. Ben Alla, H., Ben Alla, S., Touhafi, A., & Ezzati, A. (2018). A novel task scheduling approach based on dynamic queues and hybrid meta-heuristic algorithms for cloud computing environment. Cluster Computing, 21(4), 1797–1820.

    Article  Google Scholar 

  33. Batista, D. M., & da Fonseca, N. L. S. (2011). Scheduling grid tasks in face of uncertain communication demands. IEEE Transactions on Network and Service Management, 8(2), 92–103.

    Article  Google Scholar 

  34. Kumar, M. S., Gupta, I., & Jana, P. K. (2017). Delay-based workflow scheduling for cost optimization in heterogeneous cloud system, In 2017 tenth international conference on contemporary computing (IC3), Noida, (pp. 1–6).

  35. Gupta, I., Kumar, M. S., & Jana, P. K. (2018). Efficient workflow scheduling algorithm for cloud computing system: A dynamic priority-based approach. Arabian Journal for Science and Engineering, 43(12), 7945–7960.

    Article  Google Scholar 

  36. Rajak, N., & Shukla, D. (2019). Performance analysis of workflow scheduling algorithm in cloud computing environment using priority attribute. International Journal of Advanced Science and Technology, Australia, 28(16), 1810–1831.

    Google Scholar 

  37. Yuan, H., Bi, J., Zhang, J., Zhou, M. (2021). Energy consumption and performance optimized taskscheduling in distributed data centers, In IEEE transactions on systems, man, and cybernetics: systems, (pp. 1–12).

  38. Yadav, A. M., Tripathi, K. N., & Sharma, S. C. (2021). An enhanced multi-objective fireworks algorithm for task scheduling in fog computing environment. Cluster Computing

  39. Kalra, M., & Singh, S. (2021). Multi-objective energy aware scheduling of deadline constrained workflows in clouds using hybrid approach. Wireless Personal Communications, 116, 1743–1764.

    Article  Google Scholar 

  40. Medara, R., & Singh, R. S. (2021). Energy efficient and reliability aware workflow task scheduling in cloud environment. Wireless Personal Communications, 119, 1301–1320.

    Article  Google Scholar 

  41. Arora, N. & Banyal, R.K. (2021) A particle grey wolf hybrid algorithm for workflow scheduling in cloud computing. Wireless Pers Communications

Download references

Funding

No funding was received for conducting this study.

Author information

Authors and Affiliations

  1. Department of Computer Science and Applications, Dr.Harisingh Gour Central University, Sagar, M.P, India

    Nidhi Rajak & Ranjit Rajak

  2. Department of Electronics and Communication, University of Allahabad, Prayagraj (U.P.), India

    Shiv Prakash

Authors
  1. Nidhi Rajak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ranjit Rajak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shiv Prakash
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ranjit Rajak.

Ethics declarations

Conflict of interest

He authors declare that they have no conflict of interest in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rajak, N., Rajak, R. & Prakash, S. A Workflow Scheduling Method for Cloud Computing Platform. Wireless Pers Commun 126, 3625–3647 (2022). https://doi.org/10.1007/s11277-022-09882-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09882-w

Keywords

Search

Navigation