Skip to main content

Multi-objective heuristics algorithm for dynamic resource scheduling in the cloud computing environment


Cloud infrastructure provides resources needed for tasks for resource scheduling. This work uses a genetic algorithm based on encoded chromosome (GEC-DRP) to manage dynamic resource scheduling. However, the existing scheduling algorithm estimates the number of required physical machines (PM) needed for the client in the future. This developed scheduling algorithm schedules the tasks on cloud by calculating the number of virtual machines needed in the near future along with their predicted CPU and memory requirements, which is the main contribution of the work. K-means algorithm clusters the tasks based on CPU and memory usage as parameters. The future arrival of tasks for every cluster is predicted and accordingly, the required number of VMs is created. The incoming requests known as tasks are scheduled on the appropriate VM using the genetic algorithm (GA). Based on the workload prediction results, a cost-optimized resource scheduling strategy in cloud computing environment is proposed aiming at minimizing the total cost of rental virtual machines from the central cloud. Finally, a genetic algorithm is used to solve the resource scheduling strategy. The developed algorithms are evaluated by the workload prediction accuracy, the total cost of the cluster and the algorithm’s consuming time for solving the resource scheduling problems through the experiments. Finally, the effective of workload prediction algorithm based on SES and cost-optimized resource scheduling strategy is verified by simulation.

This is a preview of subscription content, access via your institution.

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


  1. 1.

    Al-Maytami BA, Fan P, Hussain A, Baker T, Liatsis P (2019) A task scheduling algorithm with improved makespan based on prediction of tasks computation time algorithm for cloud computing. IEEE Access 7:160916–160926.

    Article  Google Scholar 

  2. 2.

    AKMMR Mazumder, KMA Uddin, N Arbe, L Jahan and M Whaiduzzaman, (2019) Dynamic task scheduling algorithms in cloud computing. In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, pp. 1280-1286.

  3. 3. center.htm

  4. 4.

    Tsai JT, Fang JC, Chou JH (2013) Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Comput Oper Res 40(12):3045–3055

    Article  Google Scholar 

  5. 5.

    Dabbagh M, Hamdaoui B, Guizani M, Rayes A (2015) Energy-efficient resource allocation and provisioning framework for cloud data centers. IEEE Trans Netw Serv Manage 12(3):377–391

    Article  Google Scholar 

  6. 6.

    Keshanchi B, Souri A, Navimipour NJ (2017) An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J Syst Softw 124:1–21

    Article  Google Scholar 

  7. 7.

    Mehdi NA, Mamat A, Ibrahim H, Subramaniam SK (2011) Impatient task mapping in elastic cloud using genetic algorithm. J Comput Sci 7(6):877

    Article  Google Scholar 

  8. 8.

    Arianyan E, Maleki D, Yari A, Arianyan I (2012) November. Efficient resource allocation in cloud data centers through genetic algorithm. IEEE Sixth Int Symp Telecommun (IST) 2012:566–570

    Google Scholar 

  9. 9.

    Tao F, Feng Y, Zhang L, Liao TW (2014) CLPS-GA: a case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling. Appl Soft Comput 19(2014):264–279

    Article  Google Scholar 

  10. 10.

    Iverson MA, Ozguner F, Potter L (1999) Statistical prediction of task execution times through analytic benchmarking for scheduling in a heterogeneous environment. IEEE Trans Comput 48(12):1374–1379.

    Article  Google Scholar 

  11. 11.

    Zhang Q, Zhani MF, Boutaba R, Hellerstein JL (2014) Dynamic heterogeneity-aware resource provisioning in the cloud. IEEE Trans Cloud Comput 2(1):14–28

    Article  Google Scholar 

  12. 12.

    Kumar M, Sharma SC, Goel A, Singh SP (2019) A comprehensive survey for scheduling techniques in cloud computing. J Netw Comput Appl 143:1–33

    Article  Google Scholar 

  13. 13.

    Li C, Bai J, Luo Y (2020) Efficient resource scaling based on load fluctuation in edge-cloud computing environment. J Supercomput.

    Article  Google Scholar 

  14. 14.

    Liang B, Dong X, Wang Y, Zhang X (2020) A low-power task scheduling algorithm for heterogeneous cloud computing. J Supercomput.

    Article  Google Scholar 

  15. 15.

    Nashaat H, Ashry N, Rizk R (2019) Smart elastic scheduling algorithm for virtual machine migration in cloud computing. J Supercomput 75(7):3842–3865.

    Article  Google Scholar 

  16. 16.

    Xiao Z, Song W, Chen Qi (2013) Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans Parallel Distrib Syst 24(6):1107–1117

    Article  Google Scholar 

  17. 17.

    Duan H, Chen C, Min G, Wu Y (2017) Energy-aware scheduling of virtual machines in heterogeneous cloud computing systems. Future Gener Comput Syst 74:142–150

    Article  Google Scholar 

  18. 18.

    Suresh Kumar D, Jagadeesh Kannan R (2020) Reinforcement learning-based controller for adaptive workflow scheduling in multi-tenant cloud computing. Int J Electr Eng Educ.

    Article  Google Scholar 

  19. 19.

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

    MathSciNet  Article  Google Scholar 

  20. 20.

    Singh, S. and Kalra, M., (2014) Scheduling of independent tasks in cloud computing using modified genetic algorithm. International conference on computational intelligence and communication networks (CICN) pp. 565–569

  21. 21.

    Hu H, Li Z, Hu H, Chen J, Ge J, Li C, Chang V (2018) Multi-objective scheduling for scientific workflow in multicloud environment. J Netw Comput Appl 114:108–122

    Article  Google Scholar 

  22. 22.

    Kar, I., Parida, R.R. and Das, H., (2016) Energy aware scheduling using genetic algorithm in cloud data centers. International conference on electrical, electronics, and optimization techniques (ICEEOT), pp. 3545–3550

  23. 23.

    Zhu Z, Zhang G, Li M, Liu X (2015) Evolutionary multi-objective workflow scheduling in cloud. IEEE Trans Parallel Distrib Syst 27(5):1344–1357

    Article  Google Scholar 

  24. 24.

    Zheng Z, Wang R, Zhong H and Zhang X, (2011) An approach for cloud resource scheduling based on Parallel Genetic Algorithm. IEEE. In 2011 3rd International Conference on Computer Research and Development (Vol. 2, pp. 444–447)

  25. 25.

    Ren, X., Lin, R. and Zou, H., (2011) A dynamic load balancing strategy for cloud computing platform based on exponential smoothing forecast. In IEEE international conference on cloud computing and intelligence systems (CCIS), pp. 220–224

  26. 26.

    Hartigan JA, Wong MA (1979) Algorithm AS 136: a k-means clustering algorithm. J R Stat Soc Ser C (Applied Statistics) 28(1):100–108

    MATH  Google Scholar 

  27. 27.

  28. 28.

    Yao Y, Wang Z (2020) Privacy information antistealing control method of medical system based on cloud computing. Int J Commun Syst.

    Article  Google Scholar 

  29. 29.

    Reiss, C., Tumanov, A., Ganger, G.R., Katz, R.H. and Kozuch, M.A., (2012) Heterogeneity and dynamicity of clouds at scale: Google trace analysis. In Proceedings of the third ACM symposium on cloud computing, p. 7

  30. 30.

    Calheiros R, Ranjan R, De Rose C, Rajkumar B (2009) CloudSim: a novel framework for modeling and simulation of cloud computing infrastructures and services

  31. 31.

    John Wilkes and Charles Reiss. The clusterdata-2011–2 trace.

  32. 32.

  33. 33.

    Madni SHH, Latiff MSA, Abdullahi M, Usman MJ (2017) Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment. PLoS ONE 12(5):e0176321

    Article  Google Scholar 

  34. 34.

    Pakhira MK, (2014) A linear time-complexity k-means algorithm using cluster shifting. IEEE, In 2014 international conference on computational intelligence and communication networks (pp. 1047–1051)

  35. 35.

    Kurdi H, Alfaries A, Al-Anazi A, Alkharji S, Addegaither M, Altoaimy L, Ahmed SH (2019) A lightweight trust management algorithm based on subjective logic for interconnected cloud computing environments. J Supercomput 75(7):3534–3554.

    Article  Google Scholar 

  36. 36.

    Liu, C.Y., Zou, C.M. and Wu, P., (2014) A Task scheduling algorithm based on genetic algorithm and ant colony optimization in cloud computing. Proceedings of the 13th international symposium on distributed computing and applications to business, engineering and science, pp. 68–72

  37. 37.

    Jiang Y, Perng CS, Li T, Chang RN (2013) Cloud analytics for capacity planning and instant vm provisioning. IEEE Trans Netw Serv Manage 10(3):312–325

    Article  Google Scholar 

  38. 38.

    Ghorbani, M., Wang, Y., Xue, Y., Pedram, M. and Bogdan, P., (2014). Prediction and control of bursty cloud workloads: a fractal framework. Proceedings of the 2014 ACM international conference on hardware/software codesign and system synthesis p. 12

  39. 39.

    Abdulhamid, Shafi'i Muhammad; Madni, Syed Hamid Hussain; Latiff, Muhammad ShafieAbd; Abdullahi, Mohammed; Usman, Mohammed Joda (2017) Cloud Workloads. figshare.

  40. 40.

    Cai W, Zhu J, Bai W, Lin W, Zhou N, Li K (2020) A cost saving and load balancing task scheduling model for computational biology in heterogeneous cloud datacenters. J Supercomput 76:6113–6139.

    Article  Google Scholar 

  41. 41.

    Zhang P, Zhou M (2017) Dynamic cloud task scheduling based on a two-stage strategy. IEEE Trans Autom Sci Eng 15(2):772–783

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to K. Lalitha Devi.

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

Verify currency and authenticity via CrossMark

Cite this article

Devi, K.L., Valli, S. Multi-objective heuristics algorithm for dynamic resource scheduling in the cloud computing environment. J Supercomput 77, 8252–8280 (2021).

Download citation


  • Cloud computing
  • Data clustering
  • Workload prediction
  • Task scheduling
  • Genetic algorithm