Skip to main content

Advertisement

Log in

Efficient dynamic resource allocation method for cloud computing environment

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The dynamic resource allocation is a good feature of the cloud computing environment. However, it faces serious problems in terms of service quality, fault tolerance, and energy consumption. It was necessary, then, to find an effective method that can effectively address these important issues and increase cloud performance. This paper presents a dynamic resource allocation model that can meet customer demand for resources with improved and faster responsiveness. It also proposes a multi-objective search algorithm called Spacing Multi-Objective Antlion algorithm (S-MOAL) to minimize both the makespan and the cost of using virtual machines. In addition, its impact on fault tolerance and energy consumption was studied. The simulation revealed that our method performed better than the PBACO, DCLCA, DSOS and MOGA algorithms, especially in terms of makespan.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

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

Similar content being viewed by others

Notes

  1. Service level agreement.

References

  1. Gupta, B., Agrawal, D.P., Yamaguchi, S.: Handbook of Research on Modern Cryptographic Solutions for Computer and Cyber Security. IGI Global, Hershey (2016)

    Book  Google Scholar 

  2. Gupta, B.B.: Computer and Cyber Security: Principles, Algorithm, Applications, and Perspectives. CRC Press, Boca Raton (2018)

    Google Scholar 

  3. Hamdaqa, M., Tahvildari, L.: Cloud computing uncovered: a research landscape. In: Advances in Computers, vol. 86, pp. 41–85. Elsevier (2012)

  4. Kumar, M., Sharma, S.C.: Deadline constrained based dynamic load balancing algorithm with elasticity in cloud environment. Comput. Electr. Eng. 69, 395–411 (2018)

    Article  Google Scholar 

  5. Mell, P., Grance, T., et al.: The Nist Definition of Cloud Computing. NIST, Gaithersburg (2011)

    Book  Google Scholar 

  6. Madni, S.H.H., Latiff, M.S.A., Coulibaly, Y., et al.: Resource scheduling for infrastructure as a service (iaas) in cloud computing: challenges and opportunities. J. Netw. Comput. Appl. 68, 173–200 (2016)

    Article  Google Scholar 

  7. Zhan, Z.-H., Liu, X.-F., Gong, Y.-J., Zhang, J., Chung, Henry Shu-Hung, Li, Yun: Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput. Surv. (CSUR) 47(4), 63 (2015)

    Article  Google Scholar 

  8. Chowhan, S.S., Shirwaikar, S., Kumar, A.: Predictive modeling of service level agreement parameters for cloud services. Int. J. Next-Gener. Comput. 7(2), 115–129 (2016)

    Google Scholar 

  9. Sadashiv, N., Dilip Kumar, S.M.: Broker-based resource management in dynamic multi-cloud environment. Int. J. High Perform. Comput. Netw. 12(1), 94–109 (2018)

    Article  Google Scholar 

  10. Latiff, M.S.A., Madni, S.H.H., Abdullahi, M., et al.: Fault tolerance aware scheduling technique for cloud computing environment using dynamic clustering algorithm. Neural Comput. Appl. 29(1), 279–293 (2018)

    Article  Google Scholar 

  11. Yan, H., Zhu, X., Chen, H., Guo, H., Zhou, W., Bao, W.: Deft: dynamic fault-tolerant elastic scheduling for tasks with uncertain runtime in cloud. Inf. Sci. 477, 30–46 (2019)

    Article  MATH  Google Scholar 

  12. Chou, L.-D., Chen, H.-F., Tseng, F.-H., Chao, H.-C., Chang, Yao-Jen: Dpra: dynamic power-saving resource allocation for cloud data center using particle swarm optimization. IEEE Syst. J. 12(2), 1554–1565 (2018)

    Article  Google Scholar 

  13. Juarez, F., Ejarque, J., Badia, R.M.: Dynamic energy-aware scheduling for parallel task-based application in cloud computing. Future Gener. Comput. Syst. 78, 257–271 (2018)

    Article  Google Scholar 

  14. Yong, L., Sun, N.: An effective task scheduling algorithm based on dynamic energy management and efficient resource utilization in green cloud computing environment. Clust. Comput. 22(1), 513–520 (2019)

    Google Scholar 

  15. Zhang, Y., Cheng, X., Chen, L., Shen, H.: Energy-efficient tasks scheduling heuristics with multi-constraints in virtualized clouds. J. Grid Comput. 16, 459–475 (2018)

    Article  Google Scholar 

  16. Belgacem, A., Beghdad-Bey, K., Nacer, H.: Task scheduling in cloud computing environment: a comprehensive analysis. In: International Conference on Computer Science and its Applications, pp. 14–26, 24–25 April, Springe in Algiers, Algeria (2018)

  17. Abdullahi, M., Ngadi, M.A., et al.: Symbiotic organism search optimization based task scheduling in cloud computing environment. Future Gener. Comput. Syst. 56, 640–650 (2016)

    Article  Google Scholar 

  18. Azad, P., Navimipour, N.J.: An energy-aware task scheduling in the cloud computing using a hybrid cultural and ant colony optimization algorithm. Int. J. Cloud Appl. Comput. (IJCAC) 7(4), 20–40 (2017)

    Google Scholar 

  19. Durillo, J.J., Prodan, R.: Multi-objective workflow scheduling in amazon ec2. Clust. comput. 17(2), 169–189 (2014)

    Article  Google Scholar 

  20. Keshanchi, B., Souri, A., Navimipour, N.J.: 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 (2017)

    Article  Google Scholar 

  21. Wei, J., Zeng, X.: Optimal computing resource allocation algorithm in cloud computing based on hybrid differential parallel scheduling. Clust. Comput. 22, 7577–7583 (2018)

    Article  Google Scholar 

  22. Zuo, L., Shu, L., Dong, S., Zhu, C., Hara, Takahiro: A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. IEEE Access. 3, 2687–2699 (2015)

    Article  Google Scholar 

  23. Belgacem, A., Beghdad-Bey, K., Nacer, H.: Enhancing cost performance using symbiotic organism search based algorithm in cloud. In: 2018 International Conference on Smart Communications in Network Technologies (SaCoNeT), pp.s 306–311, 27–31 Oct 2018, IEEE in El Oued, Algeria (2018)

  24. Belgacem, A., Beghdad-Bey, K., Nacer, H.: A new task scheduling approach based on spacing multi-objective genetic algorithm in cloud. In: International Conference on Computer Science and Information Systems, pp. 189–195, 9–12 September, in Pozna, Poland (2018)

  25. Belgacem, A., Beghdad-Bey, K., Nacer, H.: Task scheduling optimization in cloud based on electromagnetism metaheuristic algorithm. In: 2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS), pp. 1–7, 24–25 October 2018, IEEE in Tebessa, Algeria (2018)

  26. Zhang, F., Cao, J., Li, K., Khan, S.U., Hwang, Kai: Multi-objective scheduling of many tasks in cloud platforms. Future Gener. Comput. Syst. 37, 309–320 (2014)

    Article  Google Scholar 

  27. Barrett, E., Howley, E., Duggan, J.: A learning architecture for scheduling workflow applications in the cloud. In: Web Services (ECOWS), 2011 Ninth IEEE European Conference on, pages 83–90, 14–16 Sept 2011, IEEE in Lugano, Switzerland (2011)

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

    Article  Google Scholar 

  29. Kong, W., Lei, Y., Ma, J.: Virtual machine resource scheduling algorithm for cloud computing based on auction mechanism. Optik Int. J. Light Electron. Opt. 127(12), 5099–5104 (2016)

    Article  Google Scholar 

  30. Tsai, J.-T., Fang, J.-C., Chou, J.-H.: Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm. Comput. Oper. Res. 40(12), 3045–3055 (2013)

    Article  MATH  Google Scholar 

  31. Wang, W.-J., Chang, Y.-S., Lo, W.-T., Lee, Y.-K.: Adaptive scheduling for parallel tasks with qos satisfaction for hybrid cloud environments. J. Supercomput. 66(2), 783–811 (2013)

    Article  Google Scholar 

  32. Chou, L.-D., Chen, H.-F., Tseng, F.-H., Chao, H.-C., Chang, Y.-J.: Dpra: dynamic power-saving resource allocation for cloud data center using particle swarm optimization. IEEE Syst. J. 12(2), 1554–1565 (2016)

    Article  Google Scholar 

  33. Ding, Y., Qin, X., Liu, L., Wang, T.: Energy efficient scheduling of virtual machines in cloud with deadline constraint. Future Gener. Comput. Syst. 50, 62–74 (2015)

    Article  Google Scholar 

  34. Dong, Z., Liu, N., Rojas-Cessa, R.: Greedy scheduling of tasks with time constraints for energy-efficient cloud-computing data centers. J. Cloud Comput. 4(1), 5 (2015)

    Article  Google Scholar 

  35. Jiang, H.-P., Chen, W.-M.: Self-adaptive resource allocation for energy-aware virtual machine placement in dynamic computing cloud. J. Netw. Comput. Appl. 120, 119–129 (2018)

    Article  Google Scholar 

  36. Wolke, A., Tsend-Ayush, B., Pfeiffer, C., Bichler, M.: More than bin packing: dynamic resource allocation strategies in cloud data centers. Inf. Syst. 52, 83–95 (2015)

    Article  Google Scholar 

  37. Cong, X., Yang, J., Weng, J., Wang, Y., Hui, Yu.: Optimising the deployment of virtual machine image replicas in cloud storage clusters. Int. J. High Perform. Comput. Netw. 10(4–5), 423–435 (2017)

    Google Scholar 

  38. Alsadie, D., Tari, Z., Alzahrani, E.J., Zomaya, A.Y.: Dynamic resource allocation for an energy efficient vm architecture for cloud computing. In: Proceedings of the Australasian Computer Science Week Multiconference, p. 16, January 29–February 02, 2018, ACM in Brisband, Queensland, Australia (2018)

  39. Kong, Y., Zhang, M., Ye, D.: A belief propagation-based method for task allocation in open and dynamic cloud environments. Knowl. Based Syst. 115, 123–132 (2017)

    Article  Google Scholar 

  40. Mousavi, S., Mosavi, A., Varkonyi-Koczy, A.R., Fazekas, G.: Dynamic resource allocation in cloud computing. Acta Polytech. Hung. 14(4), 83–104 (2017)

    Google Scholar 

  41. Tseng, F.-H., Wang, X., Chou, L.-D., Chao, H.-C., Leung, Victor C.M.: Dynamic resource prediction and allocation for cloud data center using the multiobjective genetic algorithm. IEEE Syst. J. 12(2), 1688–1699 (2018)

    Article  Google Scholar 

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

    Article  Google Scholar 

  43. Onat Yazir, Y., Matthews, C., Farahbod, R., Neville, S., Guitouni, A., Ganti, S., Coady, Y.: Dynamic resource allocation in computing clouds using distributed multiple criteria decision analysis. In: 2010 IEEE 3rd International Conference on Cloud Computing, pp. 91–98, 5–10 July 2010, in Miami, FL, USA (2010)

  44. Zhang, Q., Zhu, Q., Boutaba, R.: Dynamic resource allocation for spot markets in cloud computing environments. In: 2011 Fourth IEEE International Conference on Utility and Cloud Computing, pp. 178–185, 5–8 December 2011, in Victoria, NSW, Australia (2011)

  45. Doshi, P., Goodwin, R., Akkiraju, R., Verma, K.: Dynamic workflow composition: using markov decision processes. Int. J. Web Serv. Res. (IJWSR) 2(1), 1–17 (2005)

    Article  Google Scholar 

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

    Article  Google Scholar 

  47. Mirjalili, S., Jangir, P., Saremi, S.: Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl. Intell. 46(1), 79–95 (2017)

    Article  Google Scholar 

  48. Das, I., Dennis, J.E.: Normal-boundary intersection: a new method for generating the pareto surface in nonlinear multicriteria optimization problems. SIAM J. Optim. 8(3), 631–657 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  49. On line: Tarifs de google compute engine. https://cloud.google.com/compute/pricing. Accessed 17 Jan 2019

Download references

Author information

Authors and Affiliations

Authors

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., Nacer, H. et al. Efficient dynamic resource allocation method for cloud computing environment. Cluster Comput 23, 2871–2889 (2020). https://doi.org/10.1007/s10586-020-03053-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-020-03053-x

Keywords

Navigation