Advertisement

Dynamic scheduling applying new population grouping of whales meta-heuristic in cloud computing

  • Farinaz Hemasian-Etefagh
  • Faramarz Safi-EsfahaniEmail author
Article
  • 20 Downloads

Abstract

Scheduling in cloud computing is the assignment of tasks to resources with maximum performance, which is a multi-purpose problem. The scheduling is of NP-Hard issues that is the reason why meta-heuristic algorithms are used in scheduling problems. The meta-heuristic scheduling algorithms are divided into two categories of biological and non-biological. Swarm-based meta-heuristics are of biological algorithms that are based on imitation, or based on sign. The whale optimization algorithm is a meta-heuristic biological swarm-based intelligence algorithm based on imitation. This algorithm suffers from the early convergence problem which means the population convergences early to an unfavorable optimum point. Usually, the early convergence occurs because of the weakness in exploration capability (global search). In this study, an optimized version of the Whale optimization algorithm is introduced that presents a new idea in grouping whales called GWOA. It is firstly proposed to overcome the early convergence problem and then make a balance between the local and the global search in finding the optimal solution. The proposed method divides the sorted population into δ groups and a member of each group is randomly selected which is used in encircling prey section of the whale optimization algorithm. Then, the average best fitness was enhanced to improve both exploitation and exploration as well as premature convergence. In the next step, GWOA is used in a cloud computing scheduler at high workload to reduce the average execution time, response time, and increase the throughput in the cloud computing environment. The proposed whale optimization algorithm is compared with the standard whale optimization algorithm (WOA), improved whale optimization algorithm (CWOA), particle swarm optimization (PSO), and bat algorithms applying CEC2017 functions to compare the average parameter of the best fit, and then they are implemented as a cloud computing scheduler. The results of the experiments show that the proposed method has a better performance in comparison with competent meta-heuristic algorithms and scheduling algorithms.

Keywords

Cloud computing Task scheduling Meta-heuristic algorithm Whale optimization algorithm 

Notes

References

  1. 1.
    Kalra M, Singh S (2015) Review A review of metaheuristic scheduling techniques in cloud computing, Egypt. Inf J 16(3):275–295Google Scholar
  2. 2.
    Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67Google Scholar
  3. 3.
    Kaur G, Arora S (2018) Chaotic whale optimization algorithm. J Comput Des Eng 5:275–284Google Scholar
  4. 4.
    Trivedi IN (2016) A novel adaptive whale optimization algorithm for global optimization. Indian J Sci Technol 9(38):319–326Google Scholar
  5. 5.
    Hu H, Bai Y, Xu T (2016) A whale optimization algorithm with inertia weight. WSEAS Trans Comput 15:319–326Google Scholar
  6. 6.
    Trivedi R, Indrajit N, Pradeep J, Kumar A, Jangir N, Totlani R (2018) A novel hybrid PSO-WOA algorithm for global numerical functions optimization. In: Advances in Computer and Computational Sciences, Springer, 2018, pp 53–60Google Scholar
  7. 7.
    Trivedi R, Indrajit N, Pradeep J, Kumar A, Jangir N, Totlani R (2016) A hybrid whale algorithm and pattern search technique for optimal power flow problem. In: Modelling, Identification and Control, IEEE, 2016, pp 1048–1053Google Scholar
  8. 8.
    Abdel-Basset M, Abdle-Fatah L, Saíngaiah AK (2018) An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing. Cluster Comput.  https://doi.org/10.1007/s10586-018-1769-z Google Scholar
  9. 9.
    Ling Q, Zhou Y, Luo Y (2017) Lévy flight trajectory-based whale optimization algorithm for global optimization. IEEE Access 5:6168–6186Google Scholar
  10. 10.
    Mafarja MM, Mirjalili S (2017) Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing 260:302–312Google Scholar
  11. 11.
    Tsai J, Fang J, Chou J (2013) Optimized task scheduling and resource allocation on cloud nt using improved differential evolution algorithm. Comput Oper Res 40(12):3045–3055zbMATHGoogle Scholar
  12. 12.
    Navimipour NJ, Milani FS (2015) Task scheduling in the cloud computing based on the cuckoo search algorithm. Int J Model Optim 5(1):44Google Scholar
  13. 13.
    Jafarzadeh-Shirazi O, Dastghaibyfard G, Raja MM (2014) Task scheduling with firefly algorithm in cloud computing. Sci Int (Lahore) 27:167–171Google Scholar
  14. 14.
    Zheng L, Wang X-L (2016) A pareto based fruit fly optimization algorithm for task scheduling and resource allocation in cloud computing environment. Evolut Comput IEEE 2013:3393–3400Google Scholar
  15. 15.
    Kumar VS (2014) Hybrid optimized list scheduling and trust based resource selection in cloud computing. J Theor Appl Inf Technol 69(3):434–442Google Scholar
  16. 16.
    Technique SO (2015) A novel approach of load balancing in cloud computing using cat swarm optimization technique. Int J Adv Res Comput Sci Softw Eng 5(12):466–471Google Scholar
  17. 17.
    Sreelatha KSM (2012) W-Scheduler : whale optimization for task scheduling in cloud computing. Cluster Comput.  https://doi.org/10.1007/s10586-017-1055-5 Google Scholar
  18. 18.
    Parallel Workloads Archive: LCG Grid (2005) www.cs.huji.ac.il/labs/parallel/workload/l_lcg/
  19. 19.
    Iosup A, Ostermann S, Yigitbasi MN, Prodan R, Fahringer T, Epema D (2011) Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Trans Parallel Distrib Syst 22(6):931–945.  https://doi.org/10.1109/TPDS.2011.66 Google Scholar
  20. 20.
    Iosup A, Epema D (2011) Grid computing workloads. IEEE Internet Comput 15(2):19–26Google Scholar
  21. 21.
    Shah SN, Mahmood AK, Oxley A (2011) Dynamic multilevel hybrid scheduling algorithms for grid computing. Procedia Comput Sci 4:402–411Google Scholar
  22. 22.
    Salimian L, Safi F (2013) Survey of energy efficient data centers in cloud computing. In: Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing, 2013, pp 369–374Google Scholar
  23. 23.
    Transactions I, Computing C (2017) An adaptive and fuzzy resource management approach in cloud computing. IEEE Trans Cloud Comput 7161(1):1–1Google Scholar
  24. 24.
    Donyadari E, Branch N, Esfahani FS, Branch N, Nourafza N, Branch N (2015) Scientific workflow scheduling based on deadline constraints in cloud environment. Int J Mechatron Electr Comput Technol 5(16):1–15Google Scholar
  25. 25.
    Alaei N, Safi-Esfahani F (2018) RePro-Active: a reactive–proactive scheduling method based on simulation in cloud computing. J Supercomput 74(2):801–829Google Scholar
  26. 26.
    Motavaselalhagh F, Esfahani FS, Arabnia HR (2015) Knowledge-based adaptable scheduler for SaaS providers in cloud computing. Human-Centric Comput Inf Sci 5(1):16Google Scholar
  27. 27.
    Journal AI, Kaveh A, Ghazaan MI (2017) Enhanced whale optimization algorithm for sizing optimization of skeletal structures enhanced whale optimization algorithm for sizing optimization of skeletal structures. Mech Based Des Struct Mach 45(3):345–362Google Scholar
  28. 28.
    Tawfeek M, El-sisi A, Keshk A, Torkey F (2015) Cloud task scheduling based on ant colony optimization. Comput Eng 12(2):129–137Google Scholar
  29. 29.
    Li K, Xu G, Zhao G, Dong Y, Wang D (2011) Cloud task scheduling based on load balancing ant colony optimization. In: ChinaGrid Conference 2011, pp 3-9Google Scholar
  30. 30.
    Chen H, Xiong L, Wang C (2013) Cloud task scheduling simulation via improved Ant Colony optimization algorithm. J Converg Inf Technol 8(7):1139–1147Google Scholar
  31. 31.
    Navimipour NJ (2015) Task scheduling in the cloud environments based on an artificial Bee Colony algorithm. In: International Conference on Image Processing, pp 38–44Google Scholar
  32. 32.
    Pan J, Wang H, Zhao H, Tang L (2015) Interaction artificial bee colony based load balance method in cloud computing, genetic and evolutionary computing. Springer, Berlin, pp 49–57Google Scholar
  33. 33.
    Al-Olimat HS, Alam M, Green R, Lee JK (2015) Cloudlet scheduling with particle swarm optimization. In: Communication Systems and Network Technologies, IEEE, pp 991–995Google Scholar
  34. 34.
    Ramezani F, Lu J, Hussain F (2013) Task scheduling optimization in cloud computing applying multi-objective particle swarm optimization. In: International Conference on Service-Oriented Computing, Springer, pp 237–251Google Scholar
  35. 35.
    Zhan S, Huo H (2012) Improved PSO-based task scheduling algorithm in cloud computing. J Inf Comput Sci 9(13):3821–3829Google Scholar
  36. 36.
    Al-maamari A, Omara FA (2015) Task scheduling using hybrid algorithm in cloud computing environments. J Comput Eng 17(3):96–106Google Scholar
  37. 37.
    Jiang T, Li J (2016) Research on the task scheduling algorithm for cloud computing on the basis of particle swarm optimization. Int J Simul Syst Sci Technol 17(11):1–5Google Scholar
  38. 38.
    Kumar M, Aramudhan VS (2014) Trust based resource selection in cloud computing using hybrid algorithm. Int J Intell Syst Appl 4(3):59Google Scholar
  39. 39.
    Mandal T (2015) Optimal task scheduling in cloud computing environment : meta heuristic approaches. In: Electrical Information and Communication Technology (EICT), pp 24–28Google Scholar
  40. 40.
    Hu Y, Fu F (2015) Task scheduling model of cloud computing based on firefly algorithm. Int J Hybrid Inf Technol 8(8):35–46Google Scholar
  41. 41.
    Habibi M (2016) Multi-objective task scheduling in cloud computing using an imperialist competitive algorithm. Int J Adv Comput Sci Appl 7(5):289–293Google Scholar
  42. 42.
    Awad NH, Ali MZ, Liang JJ, Qu BY, Suganthan PN (2017) Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization. Technical Report, NTU, SingaporeGoogle Scholar
  43. 43.
    Levine DM, Berenson ML, Hrehbiel TC, Stephan DF (2011) Friedman Rank test: nonparametric analysis for the randomized block design. Stat Manag using MS Excel 6/E:1–5Google Scholar
  44. 44.
    Torabi S, Safi-Esfahani F (2018) A dynamic task scheduling framework based on chicken swarm and improved raven roosting optimization methods in cloud computing. J Supercomput 74(6):2581–2626Google Scholar
  45. 45.
    Nadimi-shahraki MH, Fard ES, Safi F (2015) Efficient load balancing using Ant Colony. J Theor Appl Inf Technol 77(2):253–258Google Scholar
  46. 46.
    Kamalinasab S, Safi-Esfahani F, Shahbazi M (2019) CRFF. GP: cloud runtime formulation framework based on genetic programming. J Supercomput.  https://doi.org/10.1007/s11227-019-02750-8 Google Scholar
  47. 47.
    Salimian F, Safi-Esfahani L (2018) Energy efficient placement of virtual machines in cloud data centres based on fuzzy decision making. Int J Grid Util Comput 9(4):367–384Google Scholar
  48. 48.
    Agarwal A, Jain S (2014) Efficient optimal algorithm of task scheduling in cloud computing environment. arXiv Prepr. arXiv1404.2076 9(7):344–349Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Computer EngineeringNajafabad Branch, Islamic Azad UniversityNajafabadIran
  2. 2.Big Data Research CenterNajafabad Branch, Islamic Azad UniversityNajafabadIran

Personalised recommendations