Skip to main content
SpringerLink
Log in

Hybrid Load Balancing Technique for Cloud Environment Using Swarm Optimization

  • Article
  • Published:
The Review of Socionetwork Strategies Aims and scope Submit manuscript

Abstract

One of the most challenging aspects of cloud computing is task scheduling. User needs are changing rapidly in a dynamic environment, and the resources can fluctuate depending on demand because they are virtual. This study presents a hybrid task scheduling model that combines Particle Swarm Optimization and Whale Optimization techniques to address the challenges of task scheduling and achieve the best performance. The method is analyzed and scored based on its “makespan,” “resource utilization,” and “convergence.” Test results indicate that the proposed method reduces the makespan in all cases. Additionally, it increases resource utilization compared to the existing state-of-the-art methods. Furthermore, resource utilization increases across the board with the number of tasks performed.

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
Algorithm–1
Algorithm–2
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Panda, S. K., Nanda, S. S., & Bhoi, S. K. (2022). A pair-based task scheduling algorithm for cloud computing environment. Journal of King Saud University-Computer and Information Sciences, 34(1), 1434–1445. https://doi.org/10.1016/j.jksuci.2018.10.001

    Article  Google Scholar 

  2. Rastogi, G, Sushil R (2015) Analytical literature survey on existing load balancing schemes in cloud computing. In: 2015 International Conference on Green Computing and Internet of Things (ICGCIoT). IEEE, pp 1506–1510. https://doi.org/10.1109/ICGCIoT.2015.7380705

  3. Xue, S., Shi, W., & Xu, X. (2016). A heuristic scheduling algorithm based on PSO in the cloud computing environment. International Journal of u-and e-Service Science and Technology, 9, 349–362. https://doi.org/10.14257/ijunesst.2016.9.1.36

    Article  Google Scholar 

  4. Rajakumari, K., Kumar, M. V., Verma, G., Balu, S., Sharma, D. K., & Sengan, S. (2022). Fuzzy based ant colony optimization scheduling in cloud computing. Computer Systems Science & Engineering. https://doi.org/10.32604/csse.2022.019175

    Article  Google Scholar 

  5. Liu, Z., & Wang, X. (2012). A PSO-based algorithm for load balancing in virtual machines of cloud computing environment. In: Advances in Swarm Intelligence: Third International Conference, ICSI 2012, Shenzhen, China, June 17–20, 2012 Proceedings, Part I 3, pp 142–147. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_17

  6. Verma, G. (2022). Hybrid optimization model for secure task scheduling cloud combining optimization. Cybernetics and Systems 1-23

  7. Kumar, M., & Sharma, S. C. (2020). Dynamic load balancing algorithm to minimize the makespan time and utilize the resources effectively in cloud environment. International Journal of Computers and Applications, 42(1), 108–117. https://doi.org/10.1080/1206212X.2017.1404823

    Article  Google Scholar 

  8. Rastogi, G. & Sushil, R. (2015). Cloud computing security and homomorphic encryption. IUP Journal of Computer Sciences, 9(3), 48–58. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2693438

  9. Jena, U. K., Das, P. K., & Kabat, M. R. (2022). Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment. Journal of King Saud University-Computer and Information Sciences, 34(6), 2332–2342. https://doi.org/10.1016/j.jksuci.2020.01.012

    Article  Google Scholar 

  10. Ebadifard, F., & Babamir, S. M. (2018). A PSO‐based task scheduling algorithm improved using a load‐balancing technique for the cloud computing environment. Concurrency and Computation: Practice and Experience, 30(12), e4368. https://onlinelibrary.wiley.com/doi/abs/https://doi.org/10.1002/cpe.4368

  11. Verma, G. (2022). Secure VM migration in cloud: Multi-criteria perspective with improved optimization model. Wireless Personal Communications. https://doi.org/10.1007/s11277-021-09319-w

    Article  Google Scholar 

  12. Kumar, M., & Sharma, S. C. (2018). Deadline constrained based dynamic load balancing algorithm with elasticity in cloud environment. Computers & Electrical Engineering, 69, 395–411. https://doi.org/10.1016/j.compeleceng.2017.11.018

    Article  Google Scholar 

  13. Jacob Prem, T., & Pradeep, K. (2019). A multi-objective optimal task scheduling in cloud environment using cuckoo particle swarm optimization. Wireless Personal Communications, 109, 315–331. https://doi.org/10.1007/s11277-019-06566-w

    Article  Google Scholar 

  14. Agarwal, M., & Srivastava, G. M. S. (2019). A PSO algorithm based task scheduling in cloud computing. International Journal of Applied Metaheuristic Computing (IJAMC), 10(4), 1–17. https://doi.org/10.4018/IJAMC.2019100101

    Article  Google Scholar 

  15. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks. IEEE 4:1942–1948. https://doi.org/10.1109/ICNN.1995.488968

  16. Mangalampalli, S., Karri, G. R., & Kose, U. (2023). Multi Objective Trust aware task scheduling algorithm in cloud computing using Whale Optimization. Journal of King Saud University-Computer and Information Sciences, 35(2), 791–809. https://doi.org/10.1016/j.jksuci.2023.01.016

    Article  Google Scholar 

  17. Huang, X., Li, C., Chen, H., & An, D. (2020). Task scheduling in cloud computing using particle swarm optimization with time varying inertia weight strategies. Cluster Computing, 23, 1137–1147. https://doi.org/10.1007/s10586-019-02983-5

    Article  Google Scholar 

  18. Moganarangan, N., Babukarthik, R. G., Bhuvaneswari, S., Basha, M. S., & Dhavachelvan, P. (2016). A novel algorithm for reducing energy-consumption in cloud computing environment: Web service computing approach. Journal of King Saud University-Computer and Information Sciences, 28(1), 55–67. https://doi.org/10.1016/j.jksuci.2014.04.007

    Article  Google Scholar 

  19. Saleh, H., Nashaat, H., Saber, W., & Harb, H. M. (2018). IPSO task scheduling algorithm for large scale data in cloud computing environment. IEEE Access, 7, 5412–5420. https://doi.org/10.1109/ACCESS.2018.2890067

    Article  Google Scholar 

  20. Ramezani, F., Lu, J., & Hussain, F. K. (2014). Task-based system load balancing in cloud computing using particle swarm optimization. International journal of parallel programming, 42, 739–754. https://doi.org/10.1007/s10766-013-0275-4

    Article  Google Scholar 

  21. Pradhan, A., & Bisoy, S. K. (2022). A novel load balancing technique for cloud computing platform based on PSO. Journal of King Saud University-Computer and Information Sciences, 34(7), 3988–3995. https://doi.org/10.1016/j.jksuci.2020.10.016

    Article  Google Scholar 

  22. Amer, D. A., Attiya, G., Zeidan, I., & Nasr, A. A. (2022). Elite learning Harris hawks optimizer for multi-objective task scheduling in cloud computing. The Journal of Supercomputing. https://doi.org/10.1007/s11227-021-03977-0

    Article  Google Scholar 

  23. Mangalampalli, S., Swain, S. K., & Mangalampalli, V. K. (2022). Multi objective task scheduling in cloud computing using cat swarm optimization algorithm. Arabian Journal for Science and Engineering, 47(2), 1821–1830. https://doi.org/10.1007/s13369-021-06076-7

    Article  Google Scholar 

  24. Verma, G. (2022). Secure client-side deduplication scheme for cloud with dual trusted execution environment. IETE Journal of Research, 69, 7015–7025.

    Article  Google Scholar 

  25. Verma, G., & Adhikari, S. (2020). Qualitative perspective of live VM migration techniques in cloud computing. AIJR Proceedings. https://doi.org/10.21467/proceedings.100.6

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CSE Dept, School of Computing, DIT University, Dehradun, India

    Maanas Singal

  2. School of Computing, DIT University, Dehradun, India

    Garima Verma

Authors
  1. Maanas Singal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Garima Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Garima Verma.

Ethics declarations

Conflict of interest

We hereby declare that this manuscript is an original work and is not under consideration for publication in any other journal. All authors have approved the submission of the manuscript to the journal mentioned above.

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

Singal, M., Verma, G. Hybrid Load Balancing Technique for Cloud Environment Using Swarm Optimization. Rev Socionetwork Strat (2024). https://doi.org/10.1007/s12626-024-00160-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12626-024-00160-8

Keywords

Search

Navigation