Load Balancing in Cloud Computing: A Proposed Novel Approach Based on Walrus Behavior

Asad Ali

Authors

Asad Ali University of Engineering and Technology, Lahore, Pakistan

Keywords:

Load Balancing, Cloud Computing, Algorithms, Metaheuristic, Walrus Behavior

Abstract

This research provides a comprehensive evaluation of load-balancing algorithms in cloud computing, classifying them into static, dynamic, and nature-inspired categories. Static algorithms, such as Round Robin and Min-Min, offer simplicity and efficiency in environments with stable workloads but struggle with adaptability to varying demands. Dynamic algorithms like Throttled Load Balancing and Least Connection are more flexible, adjusting to real-time server load changes and improving resource utilization, though they introduce higher overhead and computational costs. Nature-inspired algorithms, including Ant Colony Optimization and Particle Swarm Optimization, draw from biological processes to achieve high scalability, fault tolerance, and adaptability. A novel Walrus Optimization Algorithm (WaOA) is proposed, inspired by the social and migratory behaviors of walruses, to address challenges such as task bottlenecks and resource underutilization. MATLAB simulations reveal that WaOA outperforms traditional and nature-inspired methods in terms of scalability, response time, and resource optimization. The study concludes with suggestions for integrating machine learning, hybrid techniques, and real-world testing to further enhance WaOA’s effectiveness.

References

Rupinder Kaur, Dr.Kanwalvir Singh Dhindsa, “Efficient Task Scheduling using Load Balancing in Cloud Computing,” Int. J. Adv. Netw. Appl., vol. 10, no. 3, pp. 3888–3892, 2018, [Online]. Available: https://www.ijana.in/papers/V10I3-7.pdf

J. M. Shah, K. Kotecha, S. Pandya, D. B. Choksi, and N. Joshi, “Load balancing in cloud computing: Methodological survey on different types of algorithm,” Proc. - Int. Conf. Trends Electron. Informatics, ICEI 2017, vol. 2018-January, pp. 100–107, Jul. 2017, doi: 10.1109/ICOEI.2017.8300865.

A. Rashid and A. Chaturvedi, “Cloud Computing Characteristics and Services A Brief Review,” Int. J. Comput. Sci. Eng., vol. 7, no. 2, pp. 421–426, Feb. 2019, doi: 10.26438/IJCSE/V7I2.421426.

M. Kumar and B. Bhushan, “A Methodological Comparison of the Most Efficient Load Balancing Algorithms in Cloud Computing,” SSRN Electron. J., May 2020, doi: 10.2139/SSRN.3598908.

N. R. Tadapaneni, “A Survey of Various Load Balancing Algorithms in Cloud Computing,” Int. J. Sci. Adv. Res. Technol., vol. 6, 2020.

and V. P. A. Kumar, S. Pandey, “A survey: Load balancing algorithm in cloud computing,” Proc. 2nd Int. Conf. Adv. Comput. Softw. Eng., 2019.

Dalia Abdulkareem Shafiq and A. A. N.Z. Jhanjhi, “Load balancing techniques in cloud computing environment: A review,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 7, pp. 3910–3933, 2022, doi: https://doi.org/10.1016/j.jksuci.2021.02.007.

A. A. and A. H. A. Y. Lohumi, D. Gangodkar, P. Srivastava, M. Z. Khan, “Load Balancing in Cloud Environment: A State-of-the-Art Review,” IEEE Access, vol. 11, pp. 134517–134530, 2023, doi: 10.1109/ACCESS.2023.3337146.

V. R. U. D. Chitra Devi, “Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks,” Sci. World J, p. 14, 2016, doi: http://dx.doi.org/10.1155/2016/3896065.

K. Garala, N. Goswami, and P. D. Maheta, “A performance analysis of load Balancing algorithms in Cloud environment,” 2015 Int. Conf. Comput. Commun. Informatics, ICCCI 2015, Aug. 2015, doi: 10.1109/ICCCI.2015.7218063.

and T. N. A. S. Rathod, J. Nainani, “Load balancing in cloud computing – review,” Res. J. Eng. Technol, vol. 11, no. 2, pp. 57–61, 2020.

K. R. Venkata Ravindra Reddy YKaviarasan R, Balamurugan G, “Effective load balancing approach in cloud computing using Inspired Lion Optimization Algorithm,” e-Prime - Adv. Electr. Eng. Electron. Energy, vol. 6, p. 100326, 2023, doi: https://doi.org/10.1016/j.prime.2023.100326.

R. T. and J. Ramavat, “A Survey on Various Load Balancing Algorithms in Cloud Computing,” Int. J. Sci. Adv. Res. Technol., vol. 3, pp. 1034–1039, 2017.

R. R. and A. Murugaiyan, “Comparative Study of Load Balancing Algorithms in Cloud Computing Environment,” Indian J. Sci. Technol, vol. 9, p. 85866, 2016.

V. Arulkumar and N. Bhalaji, “Resource Scheduling Algorithms for Cloud Computing Environment: A Literature Survey,” Lect. Notes Networks Syst., vol. 89, pp. 1059–1069, 2020, doi: 10.1007/978-981-15-0146-3_102.

S. T. Milan, L. Rajabion, H. Ranjbar, and N. J. Navimipour, “Nature inspired meta-heuristic algorithms for solving the load-balancing problem in cloud environments,” Comput. Oper. Res., vol. 110, pp. 159–187, Oct. 2019, doi: 10.1016/J.COR.2019.05.022.

S. J. and U. Kumari, “A Comprehensive Analysis of Load Balancing Algorithms in Cloud Computing,” 2017, doi: 10.13140/RG.2.2.15001.06247.

and A. D. C. C. C. Ijeoma, P. Inyiama, A. Samuel, O. M. Okechukwu, “Review of Hybrid Load Balancing Algorithms in Cloud Computing Environment,” arXiv Prepr. arXiv2202, p. 13181, 2022.

W. W. Mulat, S. K. Mohapatra, R. Sathpathy, and S. K. Dhal, “Improving Throttled Load Balancing Algorithm in Cloud Computing,” pp. 369–377, 2022, doi: 10.1007/978-981-19-0332-8_27.

S. Shukla, A. K. Singh, and V. Kumar Sharma, “Survey on Importance of Load Balancing for Cloud Computing,” Proc. - 2021 3rd Int. Conf. Adv. Comput. Commun. Control Networking, ICAC3N 2021, pp. 1479–1484, 2021, doi: 10.1109/ICAC3N53548.2021.9725442.

M. Rahman, S. Iqbal, and J. Gao, “Load balancer as a service in cloud computing,” Proc. - IEEE 8th Int. Symp. Serv. Oriented Syst. Eng. SOSE 2014, pp. 204–211, 2014, doi: 10.1109/SOSE.2014.31.

A. A. Jaiswal and S. Jain, “An approach towards the dynamic load management techniques in cloud computing environment,” 2014 Int. Conf. Power, Autom. Commun. INPAC 2014, pp. 112–122, Dec. 2014, doi: 10.1109/INPAC.2014.6981147.

M. Ala’anzy and M. Othman, “Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-Study,” IEEE Access, vol. 7, pp. 141868–141887, 2019, doi: 10.1109/ACCESS.2019.2944420.

H. M. and B. E. E. M. Gamal, R. Rizk, “Osmotic Bio-Inspired Load Balancing Algorithm in Cloud Computing,” IEEE Access, vol. 7, pp. 42735–42744, 2019, doi: 10.1109/ACCESS.2019.2907615.

M. Ashouraei, S. N. Khezr, R. Benlamri, and N. J. Navimipour, “A New SLA-Aware Load Balancing Method in the Cloud Using an Improved Parallel Task Scheduling Algorithm,” Proc. - 2018 IEEE 6th Int. Conf. Futur. Internet Things Cloud, FiCloud 2018, pp. 71–76, Sep. 2018, doi: 10.1109/FICLOUD.2018.00018.

A. H. R. Arif Ullah, Nazri Mohd Nawi, Jamal Uddin, Samad Baseer, “Artificial bee colony algorithm used for load balancing in cloud computing: review,” IAES Int. J. Artif. Intell, vol. 8, no. 2, 2019, doi: http://doi.org/10.11591/ijai.v8.i2.pp156-167.

G. Rastogi and R. Sushil, “Analytical literature survey on existing load balancing schemes in cloud computing,” Proc. 2015 Int. Conf. Green Comput. Internet Things, ICGCIoT 2015, pp. 1506–1510, Jan. 2016, doi: 10.1109/ICGCIOT.2015.7380705.

A. M. Yuganes A/P Parmesivan, Sazlinah Hasan, “Performance Evaluation of Load Balancing Algorithm for Virtual Machine in Data Centre in Cloud Computing,” Int. J. Eng. Technol, vol. 7, no. 4, pp. 386–390, 2018, doi: https://doi.org/10.14419/ijet.v7i4.31.23717.

A. Yadav, “Load balancing in cloud computing environment using hybrid approach (ESCEL and PSO) algorithms,” Adv. Comput. Sci. Inf. Technol, vol. 2, no. 8, pp. 10–13, 2015.

Y. Meraihi, A. B. Gabis, A. Ramdane-Cherif, and D. Acheli, “A comprehensive survey of Crow Search Algorithm and its applications,” Artif. Intell. Rev., vol. 54, no. 4, pp. 2669–2716, Apr. 2021, doi: 10.1007/S10462-020-09911-9.

N. M. N. Arif Ullah, “BAT algorithm used for load balancing purpose in cloud computing: an overview,” Int. J. High Perform. Comput. Netw., vol. 16, no. 1, 2020, [Online]. Available: http://www.inderscience.com/storage/f101241793621185.pdf

R. Kaviarasan, P. Harikrishna, and A. Arulmurugan, “Load balancing in cloud environment using enhanced migration and adjustment operator based monarch butterfly optimization,” Adv. Eng. Softw., vol. 169, p. 103128, Jul. 2022, doi: 10.1016/J.ADVENGSOFT.2022.103128.

and L. F. M. Christopher, D. Kumar, “Migration-based load balance of virtual machine servers in cloud computing by load prediction,” Int. J. Discov. Innov. Appl. Sci, vol. 2, no. 5, pp. 55–78, 2022.