Hybrid Elephant Herding Optimization and Flamingo Search Algorithm for Effective load Balancing in Cloud Computing

Authors

  • Syed Muqthadar Ali Research Scholar, Department of Computer Science and Engineering, Annamalai University. Annamalainagar, Chidambaram 608002, Tamil Nadu, India
  • N. Kumaran Assistant Professor, Department of Computer Science and Engineering, Annamalai University. Annamalainagar, Chidambaram 608002, Tamil Nadu, India
  • G. N. Balaji Associate Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

Keywords:

Cloud environment, Elephant herd optimization, Flamingo Search Algorithm, Load balancing, Mean response time

Abstract

Cloud computing has many challenges, such as server failures, loss of confidentiality, improper workloads still limit the performance of cloud systems in real-world scenarios. Due to this, numerous research works are being carried out to improve the limitation of existing systems. Among them, load balancing seems to be a major issue that degrades the performance of the cloud industry, so optimal load balancing with optimal task scheduling is required. With the aim of attaining optimal load balancing by efficacious task deployment, in this manuscript Hybrid Elephant Herding Optimization and Flamingo Search Algorithm is proposed for effectual load balancing in cloud environment (LBS-CE-Hyb-EHO-FSA). The aim of proposed LBS-CE-Hyb-EHO-FSA is to enhance the population initialization and search space exploitation for activating the predominant load balance among the virtual machines (VMs) in the clouds. It includes the weighted task scheduling procedure depending on the optimization issue formulated utilizing the parameters of makespan, energy consumption and data center cost. Here, LBS-CE-Hyb-EHO-FSA is proposed for exploiting the merits of Elephant Herding Optimization (EHO) algorithm and Flamingo Search Algorithm (FSA) in order to achieve superior results in all dimensions of cloud computing. In this, LBS-CE-Hyb-EHO-FSA achieves the allocation of Virtual Machines (VMs) to incoming tasks of cloud, when the number of currently processing tasks of a specific VM is reduced than cumulative number of tasks presently processing by other VMs in the cloud.  It also attains potential load balancing process, then difference between the processing time of all individual virtual machine and the mean response time (MRT) incurred by the complete virtual machine. Finally, the simulation experiment of proposed LBS-CE-Hyb-EHO-FSA is conducted using Cloudsim platform. Here the proposed method provides 23.35%, 15.06%, 21.77%, 27.82%, 14.31%, 19.23% lower Mean Execution Time and 38.22%, 40.21%, 19.30%, 25.46%, 19.25%, 21.14% lower mean response time comparing to the existing models.

Downloads

Download data is not yet available.

References

A. Kaur, B. Kaur, and D. Singh, 2019. Meta-heuristic based framework for workflow load balancing in cloud environment. International Journal of Information Technology, vol. 11, no.1, pp. 119-125.

S.K. Mishra, B. Sahoo, and P.P. Parida, 2020. Load balancing in cloud computing: a big picture.Journal of King Saud University-Computer and Information Sciences, vol. 32, no. 2, pp.149-158.

V. Arulkumar, and N. Bhalaji, 2021. Performance analysis of nature inspired load balancing algorithm in cloud environment. Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 3, pp. 3735-3742.

S.M. Ali, N. Kumaran, and G.N. Balaji, 2022. A hybrid elephant herding optimization and harmony search algorithm for potential load balancing in cloud environments. International Journal of Modeling, Simulation, and Scientific Computing, vol. 13, no. 5, p. 2250042.

S. Govindaraju, W.V.R. Vinisha, F.H. Shajin, and D.A. Sivasakthi, 2022. Intrusion detection framework using auto‐metric graph neural network optimized with hybrid woodpecker mating and capuchin search optimization algorithm in IoT network. Concurrency and Computation: Practice and Experience, vol. 34, no. 24, p.e7197.

P. Arivubrakan, and K. Ramasubramanian, 2023. Multi-Objective Cluster Head based Energy Aware Routing Protocol using Hybrid Woodpecker and Flamingo Search Optimization Algorithm for Internet of Things Environment. International Journal of Information Technology & Decision Making.

S.R. Deshmukh, S.K. Yadav, and D.N., Kyatanvar, 2021. Load balancing in cloud environs: Optimal task scheduling via hybrid algorithm. International Journal of Modeling, Simulation, and Scientific Computing, vol. 12, no. 02, p. 2150008.

C.T. Yang, S.T. Chen, J.C. Liu, Y.W. Chan, C.C. Chen, and V.K. Verma, 2019. An energy-efficient cloud system with novel dynamic resource allocation methods. The Journal of Supercomputing, vol. 75, pp. 4408-4429.

V.K. Verma, K. Ntalianis, C.M. Moreno, and C.T. Yang,2019Next-generation Internet of things and cloud security solutions. International Journal of Distributed Sensor Networks, vol. 15, no. 3, p. 1550147719835098.

C.T. Yang, C.K. Tsung, N.Y. Yen, and V.K. Verma, 2022. Special Issue on Innovative Applications of Big Data and Cloud Computing. Applied Sciences, vol. 12, no. 19, p. 9648.

W. Li, and G.G. Wang, 2022. Elephant herding optimization using dynamic topology and biogeography-based optimization based on learning for numerical optimization. Engineering with Computers, vol.38,(Suppl 2), pp.1585-1613.

W. Zhiheng, and L. Jianhua, 2021. Flamingo search algorithm: a new swarm intelligence optimization algorithm. IEEE Access, vol. 9, pp. 88564-88582.

A. Kaur, and B. Kaur, 2019. Load balancing optimization based on hybrid Heuristic-Metaheuristic techniques in cloud environment.Journal of King Saud University-Computer and Information Sciences.

K. Balaji, P.S. Kiran, and M.S. Kumar, 2021. An energy efficient load balancing on cloud computing using adaptive cat swarm optimization. Materials Today: Proceedings.

A.F.S. Devaraj, M. Elhoseny, S. Dhanasekaran, E.L. Lydia, and K. Shankar, 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, vol. 142, pp. 36-45.

J. Prassanna, and N. Venkataraman, 2021. Adaptive regressive holt–winters workload prediction and firefly optimized lottery scheduling for load balancing in cloud.Wireless Networks, vol. 27, no. 8, pp. 5597-5615.

S. Ziyath, and S. Senthilkumar, 2021. MHO: meta heuristic optimization applied task scheduling with load balancing technique for cloud infrastructure services.Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 6, pp.6629-6638.

N.K. Kamila, J. Frnda, S.K. Pani, R. Das, S.M. Islam, P.K. Bharti, and K. Muduli, 2022. Machine learning model design for high performance cloud computing & load balancing resiliency: An innovative approach. Journal of King Saud University-Computer and Information Sciences, 34(10), pp.9991-10009. Nov..

A. Pradhan, S.K. Bisoy, S. Kautish, M.B Jasser, and A.W. Mohamed, 2022. Intelligent Decision-Making of Load Balancing Using Deep Reinforcement Learning and Parallel PSO in Cloud Environment. IEEE Access, vol. 10, pp.76939-76952.

Perez-Siguas, R. ., Matta-Solis, H. ., Millones-Gomez, S. ., Matta-Perez, H. ., Cruzata-Martinez, A. ., & Meneses-Claudio, B. . (2023). Comparison of Social Skills of Nursing Students from Two Universities of Lima. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 14–19. https://doi.org/10.17762/ijritcc.v11i2.6105

Jones, D., Taylor, M., García, L., Rodriguez, A., & Fernández, C. Using Machine Learning to Improve Student Performance in Engineering Programs. Kuwait Journal of Machine Learning, 1(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/101

Aoudni, Y., Donald, C., Farouk, A., Sahay, K. B., Babu, D. V., Tripathi, V., & Dhabliya, D. (2022). Cloud security based attack detection using transductive learning integrated with hidden markov model. Pattern Recognition Letters, 157, 16-26. doi:10.1016/j.patrec.2022.02.012

Downloads

Published

16.07.2023

How to Cite

Ali, S. M. ., Kumaran, N. ., & Balaji, G. N. . (2023). Hybrid Elephant Herding Optimization and Flamingo Search Algorithm for Effective load Balancing in Cloud Computing. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 872–882. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3344

Issue

Vol. 11 No. 3 (2023)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.