Skip to main content
SpringerLink
Log in

Multi-objective workflow scheduling in cloud computing: trade-off between makespan and cost

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Recently, modern businesses have started to transform into cloud computing platforms to deploy their workflow applications. However, scheduling workflow under resource allocation is significantly challenging due to the computational intensity of the workflow, the dependency between tasks, and the heterogeneity of cloud resources. During resource allocation, the cloud computing environment may encounter considerable problems in terms of execution time and execution cost, which may lead to disruptions in service quality given to users. Therefore, there is a necessity to reduce the makespan and the cost at the same time. Often, this is modeled as a multi-objective optimization problem. In this respect, the fundamental research issue we address in this paper is the potential trade-off between the makespan and the cost of virtual machine usage. We propose a HEFT-ACO approach, which is based on the heterogeneous earliest end time (HEFT), and the ant colony algorithm (ACO) to minimize them. Experimental simulations are performed on three types of real-world science workflows and take into account the properties of the Amazon EC2 cloud platform. The experimental results show that the proposed algorithm performs better than basic ACO, PEFT-ACO, and FR-MOS.

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

Similar content being viewed by others

References

  1. Buyya, R., Broberg, J., Goscinski, A.M.: Cloud Computing: Principles and Paradigms, vol. 87. Wiley, Hoboken (2010)

    Google Scholar 

  2. Arregoces, M., Portolani, M.: Data Center Fundamentals. Cisco Press, Indianapolis (2003)

    Google Scholar 

  3. Smith, J.E., Nair, R.: The architecture of virtual machines. Computer 38(5), 32–38 (2005)

    Article  Google Scholar 

  4. Belgacem, A., Beghdad-Bey, K., Nacer, H., Bouznad, S.: Efficient dynamic resource allocation method for cloud computing environment. Clust. Comput. 23, 1–19 (2020)

    Article  Google Scholar 

  5. Belgacem, A., Beghdad-Bey, K., Nacer, H.: Dynamic resource allocation method based on symbiotic organism search algorithm in cloud computing. IEEE Trans Cloud. Comput. (2020). https://doi.org/10.1109/TCC.2020.3002205

    Article  Google Scholar 

  6. Noor, T.H., Zeadally, S., Alfazi, A., Sheng, Q.Z.: Mobile cloud computing: challenges and future research directions. J. Netw. Comput. Appl. 115, 70–85 (2018)

    Article  Google Scholar 

  7. Belgacem, A., Beghdad-Bey, K., Nacer, H.: Task scheduling in cloud computing environment: a comprehensive analysis. In: Proceedings of the International Conference on Computer Science and its Applications, pp. 14–26. Springer (2018)

  8. Sprinks, J., Wardlaw, J., Houghton, R., Bamford, S., Morley, J.: Task workflow design and its impact on performance and volunteers’ subjective preference in virtual citizen science. Int. J. Hum.-Comput. Stud. 104, 50–63 (2017)

    Article  Google Scholar 

  9. Momenzadeh, Z., Safi-Esfahani, F.: Workflow scheduling applying adaptable and dynamic fragmentation (WSADF) based on runtime conditions in cloud computing. Future Gen. Comput. Syst. 90, 327–346 (2019)

    Article  Google Scholar 

  10. Rodriguez, M.A., Buyya, R.: Deadline based resource provisioningand scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014)

    Article  Google Scholar 

  11. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)

    Article  Google Scholar 

  12. Gawali, M.B., Shinde, S.K.: Task scheduling and resource allocation in cloud computing using a heuristic approach. J. Cloud Comput. 7(1), 4 (2018)

    Article  Google Scholar 

  13. Jiang, H., Song, M., et al.: Dynamic scheduling of workflow for makespan and robustness improvement in the iaas cloud. IEICE Trans. Inf. Syst. 100(4), 813–821 (2017)

    Article  Google Scholar 

  14. Arabnejad, V., Bubendorfer, K., Ng, B.: Budget and deadline aware e-science workflow scheduling in clouds. IEEE Trans. Parallel Distrib. Syst. 30(1), 29–44 (2018)

    Article  Google Scholar 

  15. Na, W., Zuo, D., Zhang, Z.: Dynamic fault-tolerant workflow scheduling with hybrid spatial-temporal re-execution in clouds. Information 10(5), 169 (2019)

    Article  Google Scholar 

  16. Zhou, X., Zhang, G., Sun, J., Zhou, J., Wei, T., Shiyan, H.: Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based heft. Future Gen. Comput. Syst. 93, 278–289 (2019)

    Article  Google Scholar 

  17. Rehman, A., Hussain, S.S., Zia ur Rehman, S.Z., Shamshirband, S.: Multi-objective approach of energy efficient workflow scheduling in cloud environments. Concurr. Comput. Pract. Exp. 31(8), e4949 (2019)

    Article  Google Scholar 

  18. Haidri, R.A., Katti, C.P., Saxena, P.C.: Cost-effective deadline-aware stochastic scheduling strategy for workflow applications on virtual machines in cloud computing. Concurr. Comput. Pract. Exp. 31(7), e5006 (2019)

    Article  Google Scholar 

  19. Gupta, S., Agarwal, I., Singh, R.S.: Workflow scheduling using Jaya algorithm in cloud. Concurr. Comput. Pract. Exp. 31(17), e5251 (2019)

    Article  Google Scholar 

  20. Xue, S., Peng, Y., Xiaolong, X., Zhang, J., Shen, C., Ruan, F.: Dsm: a dynamic scheduling method for concurrent workflows in cloud environment. Clust. Comput. 22(1), 693–706 (2019)

    Article  Google Scholar 

  21. Zhang, H., Zheng, X., Xia, Y., Li, M.: Workflow scheduling in the cloud with weighted upward-rank priority scheme using random walk and uniform spare budget splitting. IEEE Access 7, 60359–60375 (2019)

    Article  Google Scholar 

  22. Gao, Y., Zhang, S., Zhou, J.: A hybrid algorithm for multi-objective scientific workflow scheduling in iaas cloud. IEEE Access 7, 125783–125795 (2019)

    Article  Google Scholar 

  23. Sinha, N., Srivastav, V., Ahmad, W.: Deadline constrained workflow scheduling optimization by initial seeding with ant colony optimization. Int. J. Comput. Appl. 155(14), 24–29 (2016)

    Google Scholar 

  24. Jethava, A.N., Desai, M.R.: Optimizing multi objective based dynamic workflow using aco and black hole algorithm in cloud computing. In: Proceedings of the 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 1144–1147. IEEE (2019)

  25. Farid, M., Latip, R., Hussin, M., Hamid, N.A.W.A.: Scheduling scientific workflow using multi-objective algorithm with fuzzy resource utilization in multi-cloud environment. IEEE Access 8, 24309–24322 (2020)

    Article  Google Scholar 

  26. Han, P., Chenglie, D., Chen, J., Xiaoyan, D.: Minimizing monetary costs for deadline constrained workflows in cloud environments. IEEE Access 8, 25060–25074 (2020)

    Article  Google Scholar 

  27. Adhikari, M., Amgoth, T., Srirama, S.N.: Multi-objective scheduling strategy for scientific workflows in cloud environment: a firefly-based approach. Appl. Soft Comput. 93, 106411 (2020)

    Article  Google Scholar 

  28. Al-Janabi, S., Mohammad, M., Al-Sultan, A.: A new method for prediction of air pollution based on intelligent computation. Soft Comput. 24(1), 661–680 (2020)

    Article  Google Scholar 

  29. Al-Janabi, S., Alwan, E.: Soft mathematical system to solve black box problem through development the farb based on hyperbolic and polynomial functions. In: Proceedings of the 2017 10th International conference on developments in eSystems engineering (DeSE), pp. 37–42. IEEE (2017)

  30. Ali, S.H.: Novel approach for generating the key of stream cipher system using random forest data mining algorithm. In: Proceedings of the 2013 sixth international conference on developments in esystems engineering, pp. 259–269. IEEE (2013)

  31. Al-Janabi, S., Salman, A.H.: Sensitive integration of multilevel optimization model in human activity recognition for smartphone and smartwatch applications. Big Data Mining Anal. 4(2), 124–138 (2021)

    Article  Google Scholar 

  32. Al-Janabi, S., Alkaim, A.F.: A nifty collaborative analysis to predicting a novel tool (drflls) for missing values estimation. Soft Comput. 24(1), 555–569 (2020)

    Article  Google Scholar 

  33. Al-Janabi, S., Alkaim, A.F., Adel, Z.: An innovative synthesis of deep learning techniques (dcapsnet & dcom) for generation electrical renewable energy from wind energy. Soft Comput. 24(14), 10943–10962 (2020)

    Article  Google Scholar 

  34. Alkaim, A.F., Al-Janabi, S.: Multi objectives optimization to gas flaring reduction from oil production. In: Proceedings of the International conference on big data and networks technologies, pp. 117–139. Springer (2019)

  35. Alkaim, A.F., Al-Janabi, S.: A comparative analysis of dna protein synthesis for solving optimization problems: a novel nature-inspired algorithm. Adv. Intell. Syst. Comput. 1372 (2020)

  36. Kliazovich, D., Pecero, J.E., Tchernykh, A., Bouvry, P., Khan, S.U., Zomaya, A.Y.: Ca-dag: modeling communication-aware applications for scheduling in cloud computing. J. Grid Comput, 14(1), 23–39 (2016)

    Article  Google Scholar 

  37. Lee, Y.C., Han, H., Zomaya, A.Y., Yousif, M.: Resource-efficient workflow scheduling in clouds. Knowl. Based Syst. 80, 153–162 (2015)

    Article  Google Scholar 

  38. Malawski, M., Figiela, K., Bubak, M., Deelman, E., Nabrzyski, J.: Scheduling multilevel deadline-constrained scientific workflows on clouds based on cost optimization. Sci. Program. 2015, 5 (2015)

    Google Scholar 

  39. Maciej, M.: Cost-and deadline-constrained provisioning for scientific work flow ensembles in iaas clouds. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis. IEEE Computer Society Press (2012)

  40. On line: Amazon ec2 instance store. Accessed (2 Jun 2021). (https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/)

  41. Zhao, H., Sakellariou, R.: An experimental investigation into the rank function of the het- erogeneous earliest finish time scheduling algorithm. In: Proceedings of the European Conference on Parallel Processing, pp. 189–194. Springer (2003)

  42. Dorigo, M., Di Caro, G.: Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406)

  43. Zhou, Y., Huang, X.: Scheduling work flow in cloud computing based on ant colony opti- mization algorithm. In: Proceedings of the 2013 Sixth International Conference On Business Intelligence And Financial Engineering, pp. 57–61. IEEE (2013)

  44. Tabucanon, M.T.: Multiple Criteria Decision Making in Industry, vol. 8. Elsevier Science Ltd, New York (1988)

    Google Scholar 

  45. Giagkiozis, I., Fleming, P.J.: Pareto front estimation for decision making. Evol. Comput. 22(4), 651–678 (2014)

    Article  Google Scholar 

  46. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.A.M.T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evol. Comput 6(2), 182–197 (2002)

    Article  Google Scholar 

  47. Chen, W., Deelman, E.: Workflowsim: a toolkit for simulating scientific work flows in distributed environments. In: Proceedings of the 2012 IEEE 8th International Conference on E-Science, pp. 1–8. IEEE (2012)

  48. Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future Gen. Comput. Syst. 29(3), 682–692 (2013)

    Article  Google Scholar 

  49. On line: Amazon ec2 on-demand pricing. Accessed (24 Apr 2021). (https://aws.amazon.com/ec2/pricing/on-demand/)

  50. Kaur, A., Kaur, B..: Load balancing optimization based on hybrid heuristic-metaheuristic techniques in cloud environment. J. King Saud Univ. Comput. Inf. Sci. (2019)

  51. Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3–18 (2011)

    Article  Google Scholar 

  52. Wei, J., Zhang, M.: A memetic particle swarm optimization for constrained multi-objective optimization problems. In Proceedings of the 2011 IEEE Congress of Evolutionary Computation (CEC), pp. 1636–1643. IEEE (2011)

  53. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. M’hamed Bougara University, Boumerdes, Algeria

    Ali Belgacem

  2. École Militaire Polytechnique, Algiers, Algeria

    Kadda Beghdad-Bey

Authors
  1. Ali Belgacem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kadda Beghdad-Bey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Belgacem.

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

Belgacem, A., Beghdad-Bey, K. Multi-objective workflow scheduling in cloud computing: trade-off between makespan and cost. Cluster Comput 25, 579–595 (2022). https://doi.org/10.1007/s10586-021-03432-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-021-03432-y

Keywords

Search

Navigation