Skip to main content

Advertisement

SpringerLink
Log in

Energy and Cost-Aware Workflow Scheduling in Cloud Computing Data Centers Using a Multi-objective Optimization Algorithm

  • Published:
Journal of Network and Systems Management Aims and scope Submit manuscript

Abstract

A multi-objective optimization approach is suggested here for scientific workflow task-scheduling problems in cloud computing. More frequently, scientific workflow involves a large number of tasks. It requires more resources to perform all these tasks. Such a large amount of computing power can be supported only by cloud infrastructure. To implement complex science applications, more computing energy is expended, so the use of cloud virtual machines in an energy-saving way is essential. However, even today, it is a difficult challenge to conduct a scientific workflow in an energy-aware cloud platform. The hardness of this problem increases even more with several contradictory goals. Most of the existing research does not consider the essential characteristic of cloud and significant issues, such as energy variation and throughput besides makespan and cost. Therefore, a hybridization of the Antlion Optimization (ALO) algorithm with the Grasshopper Optimization Algorithm (GOA) was proposed and used multi-objectively to solve the scheduling problems. The novelty of the proposed algorithm was enhancing the search performance by making algorithms greedy and using random numbers according to Chaos Theory on the green cloud environment. The purpose was to minimize the makespan, cost of performing tasks, energy consumption, and increase throughput. WorkflowSim simulator was used for implementation, and the results were compared with the SPEA2 algorithm. Experimental results indicate that based on these metrics, a proposed multi-objective optimization algorithm is better than other similar methods.

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. Zhang, H., Cao, X., Ho, J.K., Chow, T.W.: Object-level video advertising: an optimization framework. IEEE Trans. Ind. Inf. 13(2), 520–531 (2016)

    Google Scholar 

  2. Nasr, A.A., El-Bahnasawy, N.A., El-Sayed, A.: Task scheduling optimization in heterogeneous distributed systems. Int. J. Comput. Appl 107(4), 5–12 (2014)

    Google Scholar 

  3. Masdari, M., Jalali, M.: A survey and taxonomy of DoS attacks in cloud computing. Secur. Commun. Netw. 9(16), 3724–3751 (2016)

    Google Scholar 

  4. Elsherbiny, S., Eldaydamony, E., Alrahmawy, M., Reyad, A.E.: An extended Intelligent Water Drops algorithm for workflow scheduling in cloud computing environment. Egyptian Inform. J. 19(1), 33–55 (2018)

    Google Scholar 

  5. Khalili, A., Babamir, S.M.: A Pareto-based optimizer for workflow scheduling in cloud computing environment. Int. J. Inf. Commun. Technol. Res. 8(1), 51–59 (2016)

    Google Scholar 

  6. Verma, A., Kaushal, S.: A hybrid multi-objective Particle Swarm Optimization for scientific workflow scheduling. Parallel Comput. 62, 1–19 (2017)

    MathSciNet  Google Scholar 

  7. Thaman, J., Singh, M.: Green cloud environment by using robust planning algorithm. Egyptian Inform. J. 18(3), 205–214 (2017)

    Google Scholar 

  8. Masdari, M., Nabavi, S.S., Ahmadi, V.: An overview of virtual machine placement schemes in cloud computing. J. Netw. Comput. Appl. 66, 106–127 (2016)

    Google Scholar 

  9. Liu, K., Jin, H., Chen, J., Liu, X., Yuan, D., Yang, Y.: A compromised-time-cost scheduling algorithm in swindew-c for instance-intensive cost-constrained workflows on a cloud computing platform. Int. J. High Perform. Comput. Appl. 24(4), 445–456 (2010)

    Google Scholar 

  10. Ritchie, G., Levine, J.: A fast, effective local search for scheduling independent jobs in heterogeneous computing environments (2003)

  11. Attiya, G., Hamam, Y.: Task allocation for maximizing reliability of distributed systems: a simulated annealing approach. J. Parallel Distrib. Comput. 66(10), 1259–1266 (2006)

    MATH  Google Scholar 

  12. Masdari, M., Salehi, F., Jalali, M., Bidaki, M.: A survey of PSO-based scheduling algorithms in cloud computing. J. Netw. Syst. Manag. 25(1), 122–158 (2017)

    Google Scholar 

  13. Falzon, G., Li, M.: Enhancing genetic algorithms for dependent job scheduling in grid computing environments. J. Supercomput. 62(1), 290–314 (2012)

    Google Scholar 

  14. Masdari, M., Zangakani, M.: Green cloud computing using proactive virtual machine placement: challenges and issues. J. Grid Comput. 1–33 (2019)

  15. 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)

    Google Scholar 

  16. Singh, V., Gupta, I., Jana, P.K.: An energy efficient algorithm for workflow scheduling in IaaS cloud. J. Grid Comput. 1–20 (2019)

  17. Abdelkader, D.M., Omara, F.: Dynamic task scheduling algorithm with load balancing for heterogeneous computing system. Egyptian Inform. J. 13(2), 135–145 (2012)

    Google Scholar 

  18. Camelo, M., Donoso, Y., Castro, H.: A multi-objective performance evaluation in grid task scheduling using evolutionary algorithms. Appl. Math. Inform. (2010)

  19. Durillo, J.J., Prodan, R.: Multi-objective workflow scheduling in Amazon EC2. Clust. Comput. 17(2), 169–189 (2014)

    Google Scholar 

  20. Durillo, J.J., Nae, V., Prodan, R.: Multi-objective energy-efficient workflow scheduling using list-based heuristics. Futur. Gener. Comput. Syst. 36, 221–236 (2014)

    Google Scholar 

  21. Chen, H., Zhu, X., Liu, G., Pedrycz, W.: Uncertainty-aware online scheduling for real-time workflows in cloud service environment. IEEE Trans. Serv. Comput. (2018)

  22. Fard, H.M., Prodan, R., Fahringer, T.: Multi-objective list scheduling of workflow applications in distributed computing infrastructures. J. Parallel Distrib. Comput. 74(3), 2152–2165 (2014)

    MATH  Google Scholar 

  23. Ye, X., Liu, S., Yin, Y., Jin, Y.: User-oriented many-objective cloud workflow scheduling based on an improved knee point driven evolutionary algorithm. Knowl.-Based Syst. 135, 113–124 (2017)

    Google Scholar 

  24. Liu, J., Ren, J., Dai, W., Zhang, D., Zhou, P., Zhang, Y., Min, G., Najjari, N.: Online multi-workflow scheduling under uncertain task execution time in IaaS clouds. IEEE Trans. Cloud Comput. (2019)

  25. Nosratabadi, S.M., Bornapour, M., Gharaei, M.A.: Grasshopper optimization algorithm for optimal load frequency control considering Predictive Functional Modified PID controller in restructured multi-resource multi-area power system with Redox Flow Battery units. Control. Eng. Pract. 89, 204–227 (2019)

    Google Scholar 

  26. Yu, J., Kirley, M., Buyya, R.: Multi-objective planning for workflow execution on grids. In: Proceedings of the 8th IEEE/ACM International Conference on Grid Computing 2007, pp. 10–17. IEEE Computer Society

  27. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. TIK-report 103 (2001).

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

    Google Scholar 

  29. Knowles, J., Corne, D.: The pareto archived evolution strategy: a new baseline algorithm for pareto multiobjective optimisation. In: Congress on Evolutionary Computation (CEC99), pp. 98–105 (1999)

  30. Mohammadzadeh, A., Masdari, M., Gharehchopogh, F.S., Jafarian, A.: Improved chaotic binary grey wolf optimization algorithm for workflow scheduling in green cloud computing. Evolut. Intell. 1–29 (2020)

  31. Emary, E., Zawbaa, H.M., Hassanien, A.E.: Binary grey wolf optimization approaches for feature selection. Neurocomputing 172, 371–381 (2016)

    Google Scholar 

  32. Khalili, A., Babamir, S.M.: Optimal scheduling workflows in cloud computing environment using Pareto-based Grey Wolf Optimizer. Concurr. Comput. 29(11), e4044 (2017)

    Google Scholar 

  33. Xu, Y., Li, K., Hu, J., Li, K.: A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Inf. Sci. 270, 255–287 (2014)

    MathSciNet  MATH  Google Scholar 

  34. Schwiegelshohn, U.: Job Scheduling Strategies for Parallel Processing. Springer, New York (2010)

    Google Scholar 

  35. Mezmaz, M., Melab, N., Kessaci, Y., Lee, Y.C., Talbi, E.-G., Zomaya, A.Y., Tuyttens, D.: A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. J. Parallel Distrib. Comput. 71(11), 1497–1508 (2011)

    Google Scholar 

  36. Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)

    Google Scholar 

  37. Saremi, S., Mirjalili, S., Lewis, A.: Grasshopper optimisation algorithm: theory and application. Adv. Eng. Softw. 105, 30–47 (2017)

    Google Scholar 

  38. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016)

    Google Scholar 

  39. Tian, T., Liu, C., Guo, Q., Yuan, Y., Li, W., Yan, Q.: An improved ant lion optimization algorithm and its application in hydraulic turbine governing system parameter identification. Energies 11(1), 95 (2018)

    Google Scholar 

  40. Wang, M., Heidari, A.A., Chen, M., Chen, H., Zhao, X., Cai, X.: Exploratory differential Ant Lion-based optimization. Expert Syst. Appl. 113548 (2020)

  41. Wang, M., Wu, C., Wang, L., Xiang, D., Huang, X.: A feature selection approach for hyperspectral image based on modified ant lion optimizer. Knowl.-Based Syst. 168, 39–48 (2019)

    Google Scholar 

  42. Guo, W.-Y., Wang, Y., Dai, F., Xu, P.: Improved sine cosine algorithm combined with optimal neighborhood and quadratic interpolation strategy. Eng. Appl. Artif. Intell. 94, 103779 (2020)

    Google Scholar 

  43. Gupta, S., Deep, K., Engelbrecht, A.P.: A memory guided sine cosine algorithm for global optimization. Eng. Appl. Artif. Intell. 93, 103718 (2020)

    Google Scholar 

  44. Fan, Y., Wang, P., Heidari, A.A., Wang, M., Zhao, X., Chen, H., Li, C.: Rationalized Fruit Fly Optimization with Sine Cosine Algorithm: a comprehensive analysis. Expert Syst. Appl. 157, 113486 (2020)

    Google Scholar 

  45. Gupta, S., Deep, K., Mirjalili, S., Kim, J.H.: A modified Sine Cosine Algorithm with novel transition parameter and mutation operator for global optimization. Expert Syst. Appl. 154, 113395 (2020)

    Google Scholar 

  46. Masdari, M., ValiKardan, S., Shahi, Z., Azar, S.I.: Towards workflow scheduling in cloud computing: a comprehensive analysis. J. Netw. Comput. Appl. 66, 64–82 (2016)

    Google Scholar 

  47. Muhammad-Bello, B.L., Aritsugi, M.: A Robust Algorithm for deadline constrained scheduling in IaaS Cloud environment. IEICE Trans. Inf. Syst. 101(12), 2942–2957 (2018)

    Google Scholar 

  48. Marouf, I.: Task Scheduling Optimization in Cloud Computing Using Multi-Objective Evolutionary Algorithms With User-in-the-Loop. Birzeit University, Palestine (2019)

    Google Scholar 

  49. Fohler, G.: How different are offline and online scheduling? Gerhard Fohler, RTSOPS (2011)

  50. Purushothaman, R., Rajagopalan, S., Dhandapani, G.: Hybridizing Gray Wolf Optimization (GWO) with Grasshopper Optimization Algorithm (GOA) for text feature selection and clustering. Appl. Soft Comput. 96, 106651 (2020)

    Google Scholar 

  51. Cerrone, C., Cerulli, R., Golden, B.: Carousel greedy: a generalized greedy algorithm with applications in optimization. Comput. Oper. Res. 85, 97–112 (2017)

    MathSciNet  MATH  Google Scholar 

  52. Kohli, M., Arora, S.: Chaotic grey wolf optimization algorithm for constrained optimization problems. J. Comput. Des. Eng. 5(4), 458–472 (2018)

    Google Scholar 

  53. Mukherjee, A., Mukherjee, V.: Chaotic krill herd algorithm for optimal reactive power dispatch considering FACTS devices. Appl. Soft Comput. 44, 163–190 (2016)

    Google Scholar 

  54. Saremi, S., Mirjalili, S., Lewis, A.: Biogeography-based optimisation with chaos. Neural Comput. Appl. 25(5), 1077–1097 (2014)

    Google Scholar 

  55. Mirjalili, S., Lewis, A.: S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evol. Comput. 9, 1–14 (2013)

    Google Scholar 

  56. Mohammadzadeh, A., Masdari, M., Gharehchopogh, F.S., Jafarian, A.: A hybrid multi-objective metaheuristic optimization algorithm for scientific workflow scheduling. Cluster Comput. (2020). https://doi.org/10.1007/s10586-020-03205-z

    Article  Google Scholar 

  57. 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. IEEE (2012)

  58. Zhou, J., Wang, T., Cong, P., Lu, P., Wei, T., Chen, M.: Cost and makespan-aware workflow scheduling in hybrid clouds. J. Syst. Architect. 100, 101631 (2019)

    Google Scholar 

  59. Deelman, E., Vahi, K., Juve, G., Rynge, M., Callaghan, S., Maechling, P.J., Mayani, R., Chen, W., Da Silva, R.F., Livny, M.: Pegasus, a workflow management system for science automation. Futur. Gener. Comput. Syst. 46, 17–35 (2015)

    Google Scholar 

  60. Liu, J., Pacitti, E., Valduriez, P., Mattoso, M.: A survey of data-intensive scientific workflow management. J. Grid Comput. 13(4), 457–493 (2015)

    Google Scholar 

  61. Saeedi, S., Khorsand, R., Bidgoli, S.G., Ramezanpour, M.: Improved many-objective particle swarm optimization algorithm for scientific workflow scheduling in cloud computing. Comput. Ind. Eng. 147, 106649 (2020)

    Google Scholar 

  62. 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)

    Google Scholar 

  63. Pasdar, A., Lee, Y.C., Almi’ani, K.: Hybrid scheduling for scientific workflows on hybrid clouds. Comput. Netw. 181, 107438 (2020)

    Google Scholar 

  64. Fard, H.M., Prodan, R., Barrionuevo, J.J.D., Fahringer, T.: A multi-objective approach for workflow scheduling in heterogeneous environments. In: 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), pp. 300–309. IEEE (2012)

  65. Durillo, J.J., Nebro, A.J.: jMetal: a Java framework for multi-objective optimization. Adv. Eng. Softw. 42(10), 760–771 (2011)

    Google Scholar 

  66. Okabe, T., Jin, Y., Sendhoff, B.: A critical survey of performance indices for multi-objective optimisation. In: The 2003 Congress on Evolutionary Computation. CEC'03. pp. 878–885. IEEE (2003)

  67. Mirjalili, S., Jangir, P., Mirjalili, S.Z., Saremi, S., Trivedi, I.N.: Optimization of problems with multiple objectives using the multi-verse optimization algorithm. Knowl.-Based Syst. 134, 50–71 (2017)

    Google Scholar 

  68. Anwar, N., Deng, H.: A hybrid metaheuristic for multi-objective scientific workflow scheduling in a cloud environment. Appl. Sci. 8(4), 538 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Shahindezh Branch, Islamic Azad University, Shahindezh, Iran

    Ali Mohammadzadeh

  2. Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

    Mohammad Masdari & Farhad Soleimanian Gharehchopogh

Authors
  1. Ali Mohammadzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Masdari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Farhad Soleimanian Gharehchopogh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Masdari.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohammadzadeh, A., Masdari, M. & Gharehchopogh, F.S. Energy and Cost-Aware Workflow Scheduling in Cloud Computing Data Centers Using a Multi-objective Optimization Algorithm. J Netw Syst Manage 29, 31 (2021). https://doi.org/10.1007/s10922-021-09599-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10922-021-09599-4

Keywords

Search

Navigation