Skip to main content
SpringerLink
Log in

Improved chaotic binary grey wolf optimization algorithm for workflow scheduling in green cloud computing

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

The workflow scheduling in the cloud computing environment is a well-known NP-complete problem, and metaheuristic algorithms are successfully adapted to solve this problem more efficiently. Grey wolf optimization (GWO) is a recently proposed interesting metaheuristic algorithm to deal with continuous optimization problems. In this paper, we proposed IGWO, an improved version of the GWO algorithm which uses the hill-climbing method and chaos theory to achieve better results. The proposed algorithm can increase the convergence speed of the GWO and prevents falling into the local optimum. Afterward, a binary version of the proposed IGWO algorithm, using various S functions and V functions, is introduced to deal with the workflow scheduling problem in cloud computing data centers, aiming to minimize their executions’ cost, makespan, and the power consumption. The proposed workflow scheduling scheme is simulated using the CloudSim simulator and the results show that our scheme can outperform other scheduling approaches in terms of metrics such as power consumption, cost, and makespan.

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

Similar content being viewed by others

Notes

  1. Earliest Start Time.

References

  1. Rani D, Ranjan RK (2014) A comparative study of SaaS, PaaS and IaaS in cloud computing. Int J Adv Res Comput Sci Softw Eng 4(6):158–161

    Google Scholar 

  2. Masdari M et al (2016) Towards workflow scheduling in cloud computing: a comprehensive analysis. J Netw Comput Appl 66:64–82

    Article  Google Scholar 

  3. Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Berlin

    Book  Google Scholar 

  4. Aktel A et al (2017) The comparison of the metaheuristic algorithms performances on airport gate assignment problem. Transp Res Procedia 22:469–478

    Article  Google Scholar 

  5. Gharehchopogh FS, Gholizadeh H (2019) A comprehensive survey: Whale optimization algorithm and its applications. Swarm Evol Comput 48:1–24

    Article  Google Scholar 

  6. Shayanfar H, Gharehchopogh FS (2018) Farmland fertility: a new metaheuristic algorithm for solving continuous optimization problems. Appl Soft Comput 71:728–746

    Article  Google Scholar 

  7. Gharehchopogh FS, Shayanfar H, Gholizadeh H (2019) A comprehensive survey on symbiotic organisms search algorithms. Artif Intell Rev 53:2265–2312

    Article  Google Scholar 

  8. Mozaffari A, Emami M, Fathi A (2018) A comprehensive investigation into the performance, robustness, scalability and convergence of chaos-enhanced evolutionary algorithms with boundary constraints. Artif Intell Rev 52:2319–2380

    Article  Google Scholar 

  9. Kennedy J (2011) Particle swarm optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Berlin, pp 760–766

    Google Scholar 

  10. Masdari M et al (2017) A survey of PSO-based scheduling algorithms in cloud computing. J Netw Syst Manag 25(1):122–158

    Article  Google Scholar 

  11. Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845

    Article  MathSciNet  MATH  Google Scholar 

  12. Yang X-S, Hossein Gandomi A (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Abualigah L (2020) Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications. Neural Comput Appl 32:12381–12401

    Article  Google Scholar 

  15. Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249

    Article  Google Scholar 

  16. Luo J, Chen M-R (2014) Improved shuffled frog leaping algorithm and its multi-phase model for multi-depot vehicle routing problem. Expert Syst Appl 41(5):2535–2545

    Article  Google Scholar 

  17. Mirjalili SZ et al (2018) Grasshopper optimization algorithm for multi-objective optimization problems. Appl Intell 48(4):805–820

    Article  Google Scholar 

  18. Kamboj VK (2016) A novel hybrid PSO–GWO approach for unit commitment problem. Neural Comput Appl 27(6):1643–1655

    Article  Google Scholar 

  19. Kumar V, Kumar D (2017) An astrophysics-inspired Grey wolf algorithm for numerical optimization and its application to engineering design problems. Adv Eng Softw 112:231–254

    Article  Google Scholar 

  20. Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381

    Article  Google Scholar 

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

    Google Scholar 

  22. Abdullah S, Alzaqebah M (2013) A hybrid self-adaptive bees algorithm for examination timetabling problems. Appl Soft Comput 13(8):3608–3620

    Article  Google Scholar 

  23. Yousri D, Allam D, Eteiba M (2019) Chaotic whale optimizer variants for parameters estimation of the chaotic behavior in permanent magnet synchronous motor. Appl Soft Comput 74:479–503

    Article  Google Scholar 

  24. Rizk-Allah RM, Hassanien AE, Bhattacharyya S (2018) Chaotic crow search algorithm for fractional optimization problems. Appl Soft Comput 71:1161–1175

    Article  Google Scholar 

  25. Kumar Y, Singh PK (2018) A chaotic teaching learning based optimization algorithm for clustering problems. Appl Intell 49:1036–1062

    Article  Google Scholar 

  26. Boushaki SI, Kamel N, Bendjeghaba O (2018) A new quantum chaotic cuckoo search algorithm for data clustering. Expert Syst Appl 96:358–372

    Article  MATH  Google Scholar 

  27. Arora S, Anand P (2018) Chaotic grasshopper optimization algorithm for global optimization. Neural Comput Appl 31:4385–4405

    Article  Google Scholar 

  28. Gandomi AH, Yang X-S (2014) Chaotic bat algorithm. J Comput Sci 5(2):224–232

    Article  MathSciNet  Google Scholar 

  29. Yu J, Buyya R (2005) A taxonomy of workflow management systems for grid computing. J Grid Comput 3(3–4):171–200

    Article  Google Scholar 

  30. Etminani K, Naghibzadeh M (2007) A min–min max–min selective algorihtm for grid task scheduling. In: 2007 3rd IEEE/IFIP international conference in central asia on internet. 2007. IEEE

  31. Gharehchopogh FS et al (2013) Analysis of scheduling algorithms in grid computing environment. Int J Innov Appl Stud 4(3):560–567

    Google Scholar 

  32. Topcuoglu H, Hariri S, Wu M-Y (1999) Task scheduling algorithms for heterogeneous processors. In: Proceedings. Eighth heterogeneous computing workshop (HCW’99). 1999. IEEE

  33. Wei W, GuoSun Z (2007) Trusted dynamic level scheduling based on Bayes trust model. Sci China Ser F Inf Sci 50(3):456–469

    Article  MATH  Google Scholar 

  34. Abdelkader DM, Omara F (2012) Dynamic task scheduling algorithm with load balancing for heterogeneous computing system. Egypt Inform J 13(2):135–145

    Article  Google Scholar 

  35. Chen W, Deelman E (2012) Workflowsim: a toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th international conference on E-science. 2012. IEEE

  36. Rahman M, Venugopal S, Buyya R (2007) A dynamic critical path algorithm for scheduling scientific workflow applications on global grids. In: Third IEEE international conference on e-science and grid computing (e-science 2007). IEEE

  37. Khajemohammadi H, Fanian A, Gulliver TA (2014) Efficient workflow scheduling for grid computing using a leveled multi-objective genetic algorithm. J Grid Comput 12(4):637–663

    Article  Google Scholar 

  38. Fard HM et al (2012) 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). IEEE

  39. Doğan A, Özgüner F (2005) Biobjective scheduling algorithms for execution time–reliability trade-off in heterogeneous computing systems. Comput J 48(3):300–314

    Article  Google Scholar 

  40. Camelo M, Donoso Y, Castro H (2010) A multi-objective performance evaluation in grid task scheduling using evolutionary algorithms. Appl Math Inform 28:100–105

    Google Scholar 

  41. Durillo JJ, Prodan R (2014) Multi-objective workflow scheduling in Amazon EC2. Cluster Comput 17(2):169–189

    Article  Google Scholar 

  42. Mateos C, Pacini E, Garino CG (2013) An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments. Adv Eng Softw 56:38–50

    Article  Google Scholar 

  43. Selvarani S, Sadhasivam GS (2010) Improved cost-based algorithm for task scheduling in cloud computing. In: 2010 IEEE international conference on computational intelligence and computing research. IEEE

  44. Mezmaz M et al (2011) A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. J Parallel Distrib Comput 71(11):1497–1508

    Article  Google Scholar 

  45. Li J et al (2011) Cost-conscious scheduling for large graph processing in the cloud. In: 2011 IEEE international conference on high performance computing and communications. IEEE

  46. Dongarra JJ et al (2007) Bi-objective scheduling algorithms for optimizing makespan and reliability on heterogeneous systems. In: Proceedings of the nineteenth annual ACM symposium on parallel algorithms and architectures. ACM

  47. Sih GC, Lee EA (1993) A compile-time scheduling heuristic for interconnection-constrained heterogeneous processor architectures. IEEE Trans Parallel Distrib Syst 4(2):175–187

    Article  Google Scholar 

  48. Yu J, Kirley M, Buyya R (2007) Multi-objective planning for workflow execution on grids. In: Proceedings of the 8th IEEE/ACM international conference on grid computing. IEEE Computer Society

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

  50. Deb K et al (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  51. Knowles J, Corne D (1999) The pareto archived evolution strategy: a new baseline algorithm for pareto multiobjective optimisation. In: Congress on evolutionary computation (CEC99)

  52. Filatovas E, Kurasova O, Sindhya K (2015) Synchronous R-NSGA-II: an extended preference-based evolutionary algorithm for multi-objective optimization. Informatica 26(1):33–50

    Article  Google Scholar 

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

    Article  Google Scholar 

  54. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  55. Burke EK, Bykov Y (2017) The late acceptance Hill–Climbing heuristic. Eur J Oper Res 258(1):70–78

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  58. Liang J-J, Suganthan PN, Deb K (2005) Novel composition test functions for numerical global optimization. In: Swarm intelligence symposium, 2005. SIS 2005. Proceedings 2005 IEEE

  59. Yang X-S (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspired Comput 2(2):78–84

    Article  Google Scholar 

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

    Article  Google Scholar 

  61. Mahmoudi M, Gharehchopogh FS (2018) An improvement of shuffled frog leaping algorithm with a decision tree for feature selection in text document classification. 16(1):60–72

  62. Masdari M, Zangakani M (2019) Efficient task and workflow scheduling in inter-cloud environments: challenges and opportunities. J Supercomput 76:499–535

    Article  Google Scholar 

  63. Masdari M, Khoshnevis A (2019) A survey and classification of the workload forecasting methods in cloud computing. Cluster Comput 22:1–26

    Google Scholar 

  64. Xu Y et al (2014) A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Inf Sci 270:255–287

    Article  MathSciNet  MATH  Google Scholar 

  65. Schwiegelshohn U (2010) Job scheduling strategies for parallel processing. Springer, Berlin

    Google Scholar 

  66. Elsherbiny S et al (2018) An extended intelligent water drops algorithm for workflow scheduling in cloud computing environment. Egypt Inform J 19(1):33–55

    Article  Google Scholar 

  67. Casas I et al (2018) GA-ETI: an enhanced genetic algorithm for the scheduling of scientific workflows in cloud environments. J Comput Sci 26:318–331

    Article  Google Scholar 

  68. Abazari F et al (2018) MOWS: multi-objective workflow scheduling in cloud computing based on heuristic algorithm. Simul Model Pract Theory 93:119–132

    Article  Google Scholar 

  69. Alkhanak EN, Lee SP (2018) A hyper-heuristic cost optimisation approach for scientific workflow scheduling in cloud computing. Fut Gener Comput Syst 86:480–506

    Article  Google Scholar 

  70. Choudhary A et al (2018) A GSA based hybrid algorithm for bi-objective workflow scheduling in cloud computing. Fut Gener Comput Syst 83:14–26

    Article  Google Scholar 

  71. Hu H et al (2018) Multi-objective scheduling for scientific workflow in multicloud environment. J Netw Comput Appl 114:108–122

    Article  Google Scholar 

  72. Ebadifard F, Babamir SM (2018) Optimal workflow scheduling in cloud computing using AHP Based multi objective black hole algorithm. 2145:36–42

  73. Yao G-S, Ding Y-S, Hao K-R (2017) Multi-objective workflow scheduling in cloud system based on cooperative multi-swarm optimization algorithm. J Cent South Univ 24(5):1050–1062

    Article  Google Scholar 

  74. Fard HM et al (2012) A multi-objective approach for workflow scheduling in heterogeneous environments. In 2012 12th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGrid). IEEE

  75. Naghibzadeh M (2016) Modeling and scheduling hybrid workflows of tasks and task interaction graphs on the cloud. Fut Gener Comput Syst 65:33–45

    Article  Google Scholar 

  76. Masdari M, Zangakani M (2019) Green cloud computing using proactive virtual machine placement: challenges and issues. J Grid Comp. https://doi.org/10.1007/s10723-019-09489-9

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Ali Mohammadzadeh, Mohammad Masdari & Farhad Soleimanian Gharehchopogh

  2. Department of Mathematics, Urmia Branch, Islamic Azad University, Urmia, Iran

    Ahmad Jafarian

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

  4. Ahmad Jafarian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Masdari.

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

Mohammadzadeh, A., Masdari, M., Gharehchopogh, F.S. et al. Improved chaotic binary grey wolf optimization algorithm for workflow scheduling in green cloud computing. Evol. Intel. 14, 1997–2025 (2021). https://doi.org/10.1007/s12065-020-00479-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-020-00479-5

Keywords

Search

Navigation