Skip to main content
SpringerLink
Log in

Multi-objective load balancing based on adaptive osprey optimization algorithm

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

With the increasing complexity of distributed systems, achieving an optimal distribution of tasks across resources is paramount for enhancing system performance. Therefore, in this study, a novel multi-objective load balancing approach based on adaptive osprey optimization algorithm (AO2) within computing environments is proposed. Our proposed method aims to simultaneously optimize multiple objectives, such as minimizing energy consumption, cost and time, through the application of advanced optimization algorithms. The proposed system initially predicts the load of each virtual machine (VMs). After the prediction process, the tasks are assigned to the VMs. The research involves a comprehensive evaluation comparing the proposed approach with existing load balancing techniques, showcasing its effectiveness in achieving superior results. The findings demonstrate the potential of the multi-objective optimization algorithm to enhance load balancing efficiency in diverse computing scenarios, providing a valuable contribution to the field of distributed systems and resource management.

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

Similar content being viewed by others

Data availability

Data sharing is not applicable to this article because of proprietary nature.

Code availability

Code sharing is not applicable to this article because of proprietary nature.

References

  1. Aceto G, Persico V, Pescapé A (2020) Industry 4.0 and health: internet of things, big data, and cloud computing for healthcare 4.0. J Indus Inf Integr 18:100129

    Google Scholar 

  2. Sunyaev A, Sunyaev A (2020) Cloud computing. In: Internet computing: principles of distributed systems and emerging internet-based technologies, pp 195–236

  3. Mishra SK, Sahoo B, Parida PP (2020) Load balancing in cloud computing: a big picture. J King Saud Univ-Comput Inf Sci 32(2):149–158

    Google Scholar 

  4. Fuzes P (2020) Response to disruptive innovation with hybrid products: transition of Oracle’s business applications to cloud computing. Int J Technol Learn Innov Dev 12(1):45–70

    Google Scholar 

  5. Surianarayanan C, Chelliah PR (2019) Essentials of Cloud Computing. Springer

    Book  Google Scholar 

  6. Kumar A (2023) Detection and prevention of DDoS attacks on edge computing of IoT devices through reinforcement learning. Int J Inf Technol 2023:1–12

    Google Scholar 

  7. Zubair S, Ahmed HM (2023) A hybrid algorithm-based optimization protocol to ensure data security in the cloud. Int J Inf Technol 7:1–8

    Google Scholar 

  8. Kumar A, Dutta S, Pranav P (2023) FQBDDA: fuzzy Q-learning based DDoS attack detection algorithm for cloud computing environment. Int J Inf Technol 16:1–10

    Google Scholar 

  9. Jamal F, Siddiqui T (2023) An optimized algorithm for resource utilization in cloud computing based on the hybridization of meta-heuristic algorithms. Int J Inf Technol 4:1–10

    Google Scholar 

  10. Keshri R, Vidyarthi DP (2023) Communication-aware, energy-efficient VM placement in cloud data center using ant colony optimization. Int J Inf Technol 15(8):4529–4535

    Google Scholar 

  11. Li R, Gong W, Lu C (2022) Self-adaptive multi-objective evolutionary algorithm for flexible job shop scheduling with fuzzy processing time. Comput Ind Eng 168:108099

    Article  Google Scholar 

  12. Schneider S, Khalili R, Manzoor A, Qarawlus H, Schellenberg R, Karl H, Hecker A (2021) Self-learning multi-objective service coordination using deep reinforcement learning. IEEE Trans Netw Serv Manag 18(3):3829–3842

    Article  Google Scholar 

  13. Ding S, Chen C, Xin B, Pardalos PM (2018) A bi-objective load balancing model in a distributed simulation system using NSGA-II and MOPSO approaches. Appl Soft Comput 63:249–267

    Article  Google Scholar 

  14. Zhou B, Li X, Liu W (2021) Hybrid multi-objective opposite-learning evolutionary algorithm for integrated production and maintenance scheduling with energy consideration. Neural Comput Appl 33:1587–1605

    Article  Google Scholar 

  15. Saxena D, Singh AK, Buyya R (2021) OP-MLB: an online VM prediction-based multi-objective load balancing framework for resource management at cloud data center. IEEE Trans Cloud Comput 10(4):2804–2816

  16. Kruekaew B, Kimpan W (2022) Multi-objective task scheduling optimization for load balancing in cloud computing environment using hybrid artificial bee colony algorithm with reinforcement learning. IEEE Access 10:17803–17818

    Article  Google Scholar 

  17. Haris M, Zubair S (2022) Mantaray modified multi-objective Harris hawk optimization algorithm expedites optimal load balancing in cloud computing. J King Saud Univ-Comput Inf Sci 34(10):9696–9709

    Google Scholar 

  18. Mishra SK, Manjula R (2020) A meta-heuristic based multi objective optimization for load distribution in cloud data center under varying workloads. Clust Comput 23:3079–3093

    Article  Google Scholar 

Download references

Funding

The authors declare that they have competing interests and funding.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India

    Karthick Panneerselvam

  2. Department of Computer Science and Engineering, Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal, Andhra Pradesh, India

    P. Prathap Nayudu

  3. Department of Information Technology, R.M.K. Engineering College, Kavaraipettai, Tamil Nadu, India

    M. Sheerin Banu

  4. Department of Information Science and Engineering, JSS Academy of Technical Education, Bangalore, Karnataka, India

    Rekha P. M

Authors
  1. Karthick Panneerselvam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Prathap Nayudu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Sheerin Banu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rekha P. M
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors read and approved the final manuscript.

Corresponding author

Correspondence to Karthick Panneerselvam.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

as per springer style author role and degree not allowed.

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

Panneerselvam, K., Nayudu, P.P., Banu, M.S. et al. Multi-objective load balancing based on adaptive osprey optimization algorithm. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-01823-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41870-024-01823-z

Keywords

Search

Navigation