Skip to main content
SpringerLink
Log in

An Optimized Load Balancing Strategy for an Enhancement of Cloud Computing Environment

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In cloud computing for effective communication, load balancing is one of the foremost challenges because cloud infrastructure has attained certain load conditions that cause machine failure, power consumption at a higher level, etc. Cloud computing is the model of on-demand data sharing via the Internet. Here, the load balancing strategy estimates the underloaded and overloaded system and balances the nodes as required. Thus, a load balancing strategy has been performed to equally load all connected Virtual machines (VM) in the cloud environment. However, conventional Load balancing methods in cloud computing confront non-deterministic polynomial-time hardness optimization issues. Therefore, this article has proposed a novel Load balancing methodology between VMs using the Hybrid Krill herd and Whale-based Deep Belief Neural model (HKHW-DBNM). This proposed method aims to improve the system's performance by balancing the Load between the VMs, optimizing the makespan, improving resource usage, reducing the degree of imbalance, and so on. Here, the developed load balancing algorithm distributes workload across multiple resources by reducing the demand on each resource and improving overall system performance. Furthermore, it helps to guarantee that resources are utilized efficiently, reducing execution time and optimizing costs. The implementation of this process has been carried out in the Python platform. The robustness of the proposed system is verified via the comparison with existing Load balancing approaches. The comparison results show that the proposed load-balancing method performs better than existing 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
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Data Availability

Enquiries about data availability should be directed to the authors.

References

  1. Rawat, P. S., Dimri, P., Kanrar, S., & Saroha, G. P. (2020). Optimize task allocation in cloud environment based on big-bang big-crunch. Wireless Personal Communications, 115, 1711–1754. https://doi.org/10.1007/s11277-020-07651-1

    Article  Google Scholar 

  2. Velpula, P., Pamula, R., Jain, P. K., & Shaik, A. (2022). Heterogeneous load balancing using predictive load summarization. Wireless Personal Communications, 125, 1075–1093. https://doi.org/10.1007/s11277-022-09589-y

    Article  Google Scholar 

  3. Abrol, P., Gupta, S., & Singh, S. (2020). A QoS aware resource placement approach inspired on the behavior of the social spider mating strategy in the cloud environment. Wireless Personal Communications, 113, 2027–2065. https://doi.org/10.1007/s11277-020-07306-1

    Article  Google Scholar 

  4. Li, S., & Pan, X. (2020). Adaptive management and multi-objective optimization of virtual machine in cloud computing based on particle swarm optimization. EURASIP Journal on Wireless Communications and Networking, 2020, 1–12. https://doi.org/10.1186/s13638-020-01722-4

    Article  Google Scholar 

  5. Hassan, H., El-Desouky, A. I., Ibrahim, A., El-Kenawy, E. M., & Arnous, R. (2020). Enhanced QoS-based model for trust assessment in cloud computing environment. IEEE Access, 8, 43752–43763. https://doi.org/10.1109/ACCESS.2020.2978452

    Article  Google Scholar 

  6. Namasudra, S., Devi, D., Kadry, S., Sundarasekar, R., & Shanthini, A. (2020). Towards DNA based data security in the cloud computing environment. Computer Communications, 151, 539–547. https://doi.org/10.1016/j.comcom.2019.12.041

    Article  Google Scholar 

  7. Tong, Z., Chen, H., Deng, X., Li, K., & Li, K. (2020). A scheduling scheme in the cloud computing environment using deep Q-learning. Information Sciences, 512, 1170–1191. https://doi.org/10.1016/j.ins.2019.10.035

    Article  Google Scholar 

  8. Shakarami, A., Ghobaei-Arani, M., Masdari, M., & Hosseinzadeh, M. (2020). A survey on the computation offloading approaches in mobile edge/cloud computing environment: A stochastic-based perspective. Journal of Grid Computing, 18(4), 639–671. https://doi.org/10.1007/s10723-020-09530-2

    Article  Google Scholar 

  9. Ebadifard, F., Babamir, S. M., & Barani, S. (2020). A dynamic task scheduling algorithm improved by Load balancing in cloud computing. In 2020 6th International Conference on Web Research (ICWR), IEEE. DOI: https://doi.org/10.1109/ICWR49608.2020.9122287

  10. Semmoud, A., Hakem, M., Benmammar, B., & Charr, J. C. (2020). Load balancing in cloud computing environments based on adaptive starvation threshold. Concurrency and Computation: Practice and Experience, 32(11), e5652. https://doi.org/10.1002/cpe.5652

    Article  Google Scholar 

  11. Li, C., Song, M., Zhang, M., & Luo, Y. (2020). Effective replica management for improving reliability and availability in edge-cloud computing environment. Journal of Parallel and Distributed Computing, 143, 107–128. https://doi.org/10.1016/j.jpdc.2020.04.012

    Article  Google Scholar 

  12. Al-Qerem, A., Alauthman, M., Almomani, A., & Gupta, B. B. (2020). IoT transaction processing through cooperative concurrency control on fog–cloud computing environment. Soft Computing, 24(8), 5695–5711. https://doi.org/10.1007/s00500-019-04220-y

    Article  Google Scholar 

  13. Agarwal, R., Baghel, N., & Khan, M. A. (2020). Load balancing in cloud computing using mutation based particle swarm optimization. In 2020 International Conference on Contemporary Computing and Applications (IC3A), IEEE. DOI: https://doi.org/10.1109/IC3A48958.2020.233295

  14. Shafiq, D. A., Jhanjhi, N. Z., Abdullah, A., & Alzain, M. A. (2021). A load balancing algorithm for the data centres to optimize cloud computing applications. IEEE Access, 9, 41731–41744. https://doi.org/10.1109/ACCESS.2021.3065308

    Article  Google Scholar 

  15. Junaid, M., Sohail, A., Rais, R. N. B., Ahmed, A., Khalid, O., Khan, I. A., Hussain, S. S., & Ejaz, N. (2020). Modeling an optimized approach for load balancing in cloud. IEEE Access, 8, 173208–173226. https://doi.org/10.1109/ACCESS.2020.3024113

    Article  Google Scholar 

  16. Zhang, W. Z., Elgendy, I. A., Hammad, M., Iliyasu, A. M., Du, X., Guizani, M., & El-Latif, A. A. A. (2020). Secure and optimized load balancing for multitier IoT and edge-cloud computing systems. IEEE Internet of Things Journal, 8(10), 8119–8132. https://doi.org/10.1109/JIOT.2020.3042433

    Article  Google Scholar 

  17. Pradhan, A., & Bisoy, S. K. (2020). A novel load balancing technique for cloud computing platform based on PSO. Journal of King Saud University-Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2020.10.016

    Article  Google Scholar 

  18. Kong, L., Mapetu, J. P. B., & Chen, Z. (2020). Heuristic load balancing based zero imbalance mechanism in cloud computing. Journal of Grid Computing, 18(1), 123–148. https://doi.org/10.1007/s10723-019-09486-y

    Article  Google Scholar 

  19. Devaraj, A. F. S., Elhoseny, M., Dhanasekaran, S., Lydia, E. L., & Shankar, K. (2020). Hybridization of firefly and improved multi-objective particle swarm optimization algorithm for energy efficient load balancing in cloud computing environments. Journal of Parallel and Distributed Computing, 142, 36–45. https://doi.org/10.1016/j.jpdc.2020.03.022

    Article  Google Scholar 

  20. Jena, U. K., Das, P. K., & Kabat, M. R. (2020). Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment. Journal of King Saud University-Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2020.01.012

    Article  Google Scholar 

  21. Balaji, K., Kiran, P. S., & Kumar, M. S. (2021). An energy efficient load balancing on cloud computing using adaptive cat swarm optimization. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2020.11.106

    Article  Google Scholar 

  22. Neelima, P., & Reddy, A. R. M. (2020). An efficient load balancing system using adaptive dragonfly algorithm in cloud computing. Cluster Computing, 23(4), 2891–2899. https://doi.org/10.1007/s10586-020-03054-w

    Article  Google Scholar 

  23. Mishra, S. K., Sahoo, B., & Parida, P. P. (2020). Load balancing in cloud computing: A big picture. Journal of King Saud University-Computer and Information Sciences, 32(2), 149–158. https://doi.org/10.1016/j.jksuci.2018.01.003

    Article  Google Scholar 

  24. Thakur, A., & Goraya, M. S. (2022). RAFL: A hybrid metaheuristic based resource allocation framework for load balancing in cloud computing environment. Simulation Modelling Practice and Theory, 116, 102485.

    Article  Google Scholar 

  25. Manikandan, N., Gobalakrishnan, N., & Pradeep, K. (2022). Bee optimization based random double adaptive whale optimization model for task scheduling in cloud computing environment. Computer Communications, 187, 35–44.

    Article  Google Scholar 

  26. Rani, P., Singh, P. N., Verma, S., Ali, N., Shukla, P. K., & Alhassan, M. (2022). An implementation of modified blowfish technique with honey bee behavior optimization for load balancing in cloud system environment. Wireless Communications and Mobile Computing, 2022.

  27. Belgacem, A., Beghdad-Bey, K., Nacer, H., & Bouznad, S. (2020). Efficient dynamic resource allocation method for cloud computing environment. Cluster Computing, 23(4), 2871–2889. https://doi.org/10.1007/s10586-020-03053-x

    Article  Google Scholar 

  28. Thennarasu, S. R., Selvam, M., & Srihari, K. (2020). A new whale optimizer for workflow scheduling in cloud computing environment. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-01678-9

    Article  Google Scholar 

  29. Mapetu, J. P. B., Chen, Z., & Kong, L. (2019). Low-time complexity and low-cost binary particle swarm optimization algorithm for task scheduling and load balancing in cloud computing. Applied Intelligence, 49(9), 3308–3330. https://doi.org/10.1007/s10489-019-01448-x

    Article  Google Scholar 

Download references

Acknowledgements

None

Funding

The authors have not disclosed any funding.

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, 500090, India

    P. Neelakantan

  2. Department of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, 500090, India

    N. Sudhakar Yadav

Authors
  1. P. Neelakantan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. Sudhakar Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Neelakantan.

Ethics declarations

Conflict of interest

The authors declare that they have no potential conflict of interest.

Ethical Approval

All applicable institutional and national guidelines for the care and use of animals were followed.

Informed Consent

For this type of study, formal consent is not required.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Neelakantan, P., Yadav, N.S. An Optimized Load Balancing Strategy for an Enhancement of Cloud Computing Environment. Wireless Pers Commun 131, 1745–1765 (2023). https://doi.org/10.1007/s11277-023-10520-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-023-10520-2

Keywords

Search

Navigation