2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 8 , Issue 1 , PP: 27-38, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Abedallah Zaid Abualkishik 1 * , Rasha Almajed 2 , William Thompson 3
Doi: https://doi.org/10.54216/FPA.080103
A cloud computing (CC) method was effectual if its sources were used in optimal way and an effectual consumption is attained by using and preserving proper management of cloud sources. Resource management can be attained through adoption of powerful source scheduling, allotment, and robust source scalability methods. The balancing of load in cloud is performed at VM level or physical machine level. A task use sources of VM and whenever a bunch of tasks reaches VM, the sources will be exhausted means no source is now existing for handling the extra task requests. This article develops an Intelligent Red Deer Algorithm based Energy Aware Load Balancing Scheme for data fusion in Cloud Environment, called IRDA-EALBS model. The presented IRDA-EALBS model majorly concentrates on the balancing of load among the virtual machines (VMs) in the cloud environment. The IRDA-EALBS model is mainly stimulated from the nature of red deers during a breading period. In addition, the IRDA-EALBS model derived an objective function to minimize energy consumption and maximize makespan. To demonstrate the enhanced performance of the IRDA-EALBS model, a wide range of experimental analyses is carried out. The simulation results highlighted the enhanced outcomes of the IRDA-EALBS model over other load balancers in the cloud environment.
Data Fusion , Internet of Things , Cloud computing , Load balancing , Energy efficiency , Red deer algorithm
[1] Kaur, A. and Luthra, M.P., 2018. A review on load balancing in cloud environment. International journal, 17(1).
[2] Mishra, S.K., Sahoo, B. and Parida, P.P., 2020. Load balancing in cloud computing: a big picture. Journal of King Saud University-Computer and Information Sciences, 32(2), pp.149-158.
[3] Liaqat, M., Naveed, A., Ali, R.L., Shuja, J. and Ko, K.M., 2019. Characterizing dynamic load balancing in cloud environments using virtual machine deployment models. IEEE Access, 7, pp.145767-145776.
[4] Milan, S.T., Rajabion, L., Ranjbar, H. and Navimipour, N.J., 2019. Nature inspired meta-heuristic algorithms for solving the load-balancing problem in cloud environments. Computers & Operations Research, 110, pp.159-187.
[5] Kaur, A. and Kaur, B., 2019. Load balancing optimization based on hybrid Heuristic-Metaheuristic techniques in cloud environment. Journal of King Saud University-Computer and Information Sciences.
[6] Nanjappan, M. and Albert, P., 2022. Hybrid based novel approach for resource scheduling using MCFCM and PSO in cloud computing environment. Concurrency and Computation: Practice and Experience, 34(7), p.e5517.
[7] Swarnakar, S., Bhattacharya, S. and Banerjee, C., 2021. A bio-inspired and heuristic-based hybrid algorithm for effective performance with load balancing in cloud environment. International Journal of Cloud Applications and Computing (IJCAC), 11(4), pp.59-79.
[8] Joshi, A. and Munisamy, S.D., 2022. Evaluating the performance of load balancing algorithm for heterogeneous cloudlets using HDDB algorithm. International Journal of System Assurance Engineering and Management, pp.1-9.
[9] Asghari, A. and Sohrabi, M.K., 2021. Combined use of coral reefs optimization and reinforcement learning for improving resource utilization and load balancing in cloud environments. Computing, 103(7), pp.1545-1567.
[10] Lin, W., Peng, G., Bian, X., Xu, S., Chang, V. and Li, Y., 2019. Scheduling algorithms for heterogeneous cloud environment: main resource load balancing algorithm and time balancing algorithm. Journal of Grid Computing, 17(4), pp.699-726.
[11] Priya, V., Kumar, C.S. and Kannan, R., 2019. Resource scheduling algorithm with load balancing for cloud service provisioning. Applied Soft Computing, 76, pp.416-424.
[12] Golchi, M.M., Saraeian, S. and Heydari, M., 2019. A hybrid of firefly and improved particle swarm optimization algorithms for load balancing in cloud environments: Performance evaluation. Computer Networks, 162, p.106860
[13] Jena, U.K., Das, P.K. and 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.
[14] Kumar, M. and Sharma, S.C., 2018. Deadline constrained based dynamic load balancing algorithm with elasticity in cloud environment. Computers & Electrical Engineering, 69, pp.395-411
[15] Mohammed, M.A., Hasan, R.A., Ahmed, M.A., Tapus, N., Shanan, M.A., Khaleel, M.K. and Ali, A.H., 2018, June. A Focal load balancer based algorithm for task assignment in cloud environment. In 2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (pp. 1-4). IEEE
[16] Thakur, A. and Goraya, M.S., 2022. RAFL: A hybrid metaheuristic based resource allocation framework for load balancing in cloud computing environment. Simulation Modelling Practice and Theory, p.102485
[17] Fathollahi-Fard, A.M., Hajiaghaei-Keshteli, M. and Tavakkoli-Moghaddam, R., 2020. Red deer algorithm (RDA): a new nature-inspired meta-heuristic. Soft Computing, 24(19), pp.14637-14665.
[18] Zitar, R.A., Abualigah, L. and Al-Dmour, N.A., 2021. Review and analysis for the Red Deer Algorithm. Journal of Ambient Intelligence and Humanized Computing, pp.1-11.
