Skip to main content
SpringerLink
Log in

CP-PGWO: multi-objective workflow scheduling for cloud computing using critical path

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

When each task of the longest path in a task-dependent scientific workflow must meet a deadline, the path is called critical. Tasks in a critical path have priority over tasks in non-critical paths. Considering this fact that less methods have already dealt with the critical path problem for workflow scheduling in cloud, this study aims to present a critical-path based method to consider the problem based on our previous optimal workflow scheduling method, GWO-based (Grey Wolf Optimization). We applied our study to balance and imbalance scientific workflows. Our results show that considering the critical path improves the completion time of workflows while maintaining a proper level of resource cost and resource utilization. Moreover, to show the effectiveness of the current study, we compared the performance of the proposed method with non-critical-path aware algorithms, using three different indicators. The simulation demonstrates that compared to PGWO as the base method, the proposed approach achieves (1) approximately 68% improvement for makespan, (2) more accuracy in population sampling for about 70% of workflows, and (3) avoidance of the cost increases in more than 50% of workflows. Moreover, the proposed method decreases makespan approximately 3 times compared to the constrained-based approaches.

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

Similar content being viewed by others

References

  1. Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: issues and challenges. J. Grid Comput. 14(2), 217–264 (2016). https://doi.org/10.1007/s10723-015-9359-2

    Article  Google Scholar 

  2. Mershad, K., Artail, H., Saghir, M.A.R., Hajj, H., Awad, M.: A study of the performance of a cloud datacenter server. IEEE Trans. Cloud Comput. 5(4), 590–603 (2017). https://doi.org/10.1109/TCC.2015.2415803

    Article  Google Scholar 

  3. I. Sadooghi, J. H. Martin, T. Li, K. Brandstatter, K. Maheshwari, T. P. P. de Lacerda Ruivo, G. Garzoglio, S. Timm, Y. Zhao, I. Raicu, Understanding the performance and potential of cloud computing for scientific applications, IEEE Trans. Cloud Comput. 5 (2) (2017) 358–371. https://doi.org/10.1109/TCC.2015.2404821

  4. Juve, G., Chervenak, A.L., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future Gener. Comp. Syst. 29(3), 682–692 (2013). https://doi.org/10.1016/j.future.2012.08.015

    Article  Google Scholar 

  5. 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). https://doi.org/10.1109/71.993206

    Article  Google Scholar 

  6. Son, J.H., Kim, J., Kim, M.: Extracting the workflow critical path from the extended well-formed workflow schema. J. Comput. Syst. Sci. 70(1), 86–106 (2005). https://doi.org/10.1016/j.jcss.2004.07.001

    Article  MathSciNet  MATH  Google Scholar 

  7. Meena, J., Kumar, M., Vardhan, M.: Cost effective genetic algorithm for workflow scheduling in cloud under deadline constraint. IEEE Access 4, 5065–5082 (2016). https://doi.org/10.1109/ACCESS.2016.2593903

    Article  Google Scholar 

  8. Liu, L.,  Zhang, M.,  Buyya, R.., Fan, Q.: Deadline-constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing, Concurrency and Computation: Pract. Exp. 29(5). https://doi.org/10.1002/cpe.3942

  9. Khalili, A., Babamir, S.M.: Optimal scheduling workflows in cloud computing environment using pareto-based grey wolf optimizer, Concurrency and Computation. Pract. Exp. 29(11). https://doi.org/10.1002/cpe.4044

  10. Ebadifard, F., Babamir, S.M.: Optimizing multi objective based workflow scheduling in cloud computing using black hole algorithm. In: 2017 3th International Conference on Web Research (ICWR), pp. 102–108. (2017)https://doi.org/10.1109/ICWR.2017.7959313

  11. Chen, Z., Zhan, Z., Lin, Y., Gong, Y., Gu, T., Zhao, F., Yuan, H., Chen, X., Li, Q., Zhang, J.: Multiobjective cloud workflow scheduling: A multiple populations ant colony system approach. IEEE Trans. Cybernet. 1–15 (2018). https://doi.org/10.1109/TCYB.2018.2832640

  12. Ebadifard, F., Doostali, S., Babamir, S.M.: A firefly-based task scheduling algorithm for the cloud computing environment: Formal verification and simulation analyses. In: 9th International Symposium on Telecommunications, IST 2018, pp. 664–669. Tehran, Iran (2018) https://doi.org/10.1109/ISTEL.2018.8661088

  13. Chen, W., Deelman, E.: Workflowsim: a toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th International Conference on E-Science, pp. 1–8. (2012). https://doi.org/10.1109/eScience.2012.6404430

  14. Arabnejad, H., Barbosa, J.G.: A budget constrained scheduling algorithm for workflow applications. J. Grid Comp. 12(4), 665–679 (2014). https://doi.org/10.1007/s10723-014-9294-7

    Article  Google Scholar 

  15. Yuan, Y., Li, X., Wang, Q., Zhu, X.: Deadline division-based heuristic for cost optimization in workflow scheduling. Inf. Sci. 179(15), 2562–2575 (2009). https://doi.org/10.1016/j.ins.2009.01.035

    Article  MATH  Google Scholar 

  16. Jailalita, S. Singh, M. Dutta, Critical path based scheduling algorithm for workflow applications in cloud computing, in: 2016 International Conference on Advances in Computing, Communication, Automation (ICACCA) (Spring), pp. 1–6. (2016). https://doi.org/10.1109/ICACCA.2016.7578905

  17. Abrishami, S., Naghibzadeh, M., Epema, D.H.J.: Deadline-constrained workflow scheduling algorithms for infrastructure as a service clouds. Future Gener. Comp. Syst. 29(1), 158–169 (2013). https://doi.org/10.1016/j.future.2012.05.004

    Article  Google Scholar 

  18. Cai, Z., Li, X., Gupta, J.N.D.: Critical path-based iterative heuristic for workflow scheduling in utility and cloud computing. In: Service-Oriented Computing - 11th International Conference, ICSOC 2013, Berlin, Germany, December 2-5, 2013, Proceedings, pp. 207–221. (2013). https://doi.org/10.1007/978-3-642-45005-1_15

  19. Wu, F., Wu, Q., Tan, Y., Li, R., Wang, W.: Pcp-b\({}^{\text{2 }}\): Partial critical path budget balanced scheduling algorithms for scientific workflow applications. Future Gener. Comp. Syst. 60, 22–34 (2016). https://doi.org/10.1016/j.future.2016.01.004

    Article  Google Scholar 

  20. Arabnejad, V., Bubendorfer, K., Ng, B., Chard, K.: A deadline constrained critical path heuristic for cost-effectively scheduling workflows. In: 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC), pp. 242–250. (2015). https://doi.org/10.1109/UCC.2015.41

  21. Arabnejad, V., Bubendorfer, K., Ng, B.: Scheduling deadline constrained scientific workflows on dynamically provisioned cloud resources. Future Gener.Comp. Syst. 75, 348–364 (2017). https://doi.org/10.1016/j.future.2017.01.002

    Article  Google Scholar 

  22. Zheng, W., Sakellariou, R.: Budget-deadline constrained workflow planning for admission control. J. Grid Comput. 11(4), 633–651 (2013). https://doi.org/10.1007/s10723-013-9257-4

    Article  Google Scholar 

  23. Verma, A., Kaushal, S.: Bi-criteria priority based particle swarm optimization workflow scheduling algorithm for cloud. In: 2014 Recent Advances in Engineering and Computational Sciences (RAECS), pp. 1–6., India (2014). https://doi.org/10.1109/RAECS.2014.6799614

  24. Verma, A., Kaushal, S.: Cost-time efficient scheduling plan for executing workflows in the cloud. J. Grid Comput. 13(4), 495–506 (2015). https://doi.org/10.1007/s10723-015-9344-9

    Article  MathSciNet  Google Scholar 

  25. Durillo, J.J., Prodan, R.: Multi-objective workflow scheduling in amazon EC2. Clust. Comput. 17(2), 169–189 (2014). https://doi.org/10.1007/s10586-013-0325-0

    Article  Google Scholar 

  26. Zhu, Z., Zhang, G., Li, M., Liu, X.: Evolutionary multi-objective workflow scheduling in cloud. IEEE Trans. Parallel Distrib. Syst. 27(5), 1344–1357 (2016). https://doi.org/10.1109/TPDS.2015.2446459

    Article  Google Scholar 

  27. Rodriguez, M.A., Buyya, R.: Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014). https://doi.org/10.1109/TCC.2014.2314655

    Article  Google Scholar 

  28. Oprescu, A., Filip, A.V., Kielmann, T.: Fast pareto front approximation for cloud instance pool optimization. In: Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain, July 11-15, 2015, Companion Material Proceedings, pp. 1443–1444. (2015). https://doi.org/10.1145/2739482.2764720

  29. Tang, Z., Qi, L., Cheng, Z., Li, K., Khan, S.U., Li, K.: An energy-efficient task scheduling algorithm in dvfs-enabled cloud environment. J. Grid Comput. 14(1), 55–74 (2016). https://doi.org/10.1007/s10723-015-9334-y

    Article  Google Scholar 

  30. Thakur, S., Chaurasia, A.: Towards green cloud computing: Impact of carbon footprint on environment. In: 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence), pp. 209–213. (2016). https://doi.org/10.1109/CONFLUENCE.2016.7508115

  31. Jiang, J., Lin, Y., Xie, G., Fu, L., Yang, J.: Time and energy optimization algorithms for the static scheduling of multiple workflows in heterogeneous computing system. J. Grid Comput. 15(4), 435–456 (2017). https://doi.org/10.1007/s10723-017-9391-5

    Article  Google Scholar 

  32. Singh, V., Gupta, I., Jana, P.K.: An energy efficient algorithm for workflow scheduling in iaas cloud. J. Grid Comput. 1–20 (2019). https://doi.org/10.1007/s10723-019-09490-2

  33. Mirjalili, S., Saremi, S., Mirjalili, S.M., dos Santos Coelho, L.: Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst. Appl. 47, 106–119 (2016). https://doi.org/10.1016/j.eswa.2015.10.039

  34. Wang, B., Wang, C., Song, Y., Cao, J., Cui, X., Zhang, L.: A survey and taxonomy on workload scheduling and resource provisioning in hybrid clouds. Clust. Comput. 23(4), 2809–2834 (2020). https://doi.org/10.1007/s10586-020-03048-8

    Article  Google Scholar 

  35. Iranmanesh, A., Naji, H.R.: Dchg-ts: a deadline-constrained and cost-effective hybrid genetic algorithm for scientific workflow scheduling in cloud computing. Clust. Comput. 24, 667–681 (2021). https://doi.org/10.1007/s10586-020-03145-8

    Article  Google Scholar 

  36. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014). https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  37. Zitzler, E., Laumanns, M., Thiele, L.: Spea2: Improving the strength pareto evolutionary algorithm, Tech. rep. (2001)

  38. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002). https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  39. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000). https://doi.org/10.1162/106365600568202

    Article  Google Scholar 

  40. Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications. University of Zurich, Zürich, Switzerland (1999).. (Ph.D. thesis)

    Google Scholar 

  41. Schott, J.: Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics (1995)

    Google Scholar 

  42. Ostermann, S., Iosup, A., Yigitbasi, N., Prodan, R., Fahringer, T., Epema, D.H.J.: A performance analysis of EC2 cloud computing services for scientific computing. In: Cloud Computing - First International Conference, CloudComp 2009, pp. 115–131. Munich, Germany (2009) https://doi.org/10.1007/978-3-642-12636-9_9

  43. de Smith, M.: Statistical Analysis Handbook, Winchelsea Press (2018)

  44. Arcuri, A., Briand, L.C.: A practical guide for using statistical tests to assess randomized algorithms in software engineering. In: Proceedings of the 33rd International Conference on Software Engineering, ICSE 2011, pp. 1–10. Waikiki, Honolulu, HI, USA (2011). https://doi.org/10.1145/1985793.1985795

  45. Masadeh, R., Alsharman, N., Sharieh, A., Mahafzah, B.A.: Task scheduling on cloud computing based on sea lion optimization algorithm. Int. J. Web Inform. Syst. 17(2), 99–116 (2021). https://doi.org/10.1108/IJWIS-11-2020-0071

    Article  Google Scholar 

  46. Masadeh, R., Sharieh, A., Mahafzah, B.A.: Humpback whale optimization algorithm based on vocal behavior for task scheduling in cloud computing. Int. J. Adv. Sci. Technol. 13(3), 121–140 (2019)

    Google Scholar 

  47. Ziafat, H., Babamir, S.M.: A hierarchical structure for optimal resource allocation in geographically distributed clouds. Future Gener. Comp. Syst. 90, 539–568 (2019). https://doi.org/10.1016/j.future.2018.08.027

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Software Engineering, University of Kashan, Kashan, Iran

    Saeed Doostali, Seyed Morteza Babamir & Maryam Eini

Authors
  1. Saeed Doostali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seyed Morteza Babamir
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maryam Eini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seyed Morteza Babamir.

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

Doostali, S., Babamir, S.M. & Eini, M. CP-PGWO: multi-objective workflow scheduling for cloud computing using critical path. Cluster Comput 24, 3607–3627 (2021). https://doi.org/10.1007/s10586-021-03351-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-021-03351-y

Keywords

Search

Navigation