185 139
Full Length Article
Fusion: Practice and Applications
Volume 8 , Issue 1, PP: 27-38 , 2022 | Cite this article as | XML | Html |PDF

Title

Intelligent Red Deer Algorithm based Energy Aware Load Balancing Scheme for Data Fusion in Cloud Environment

Authors Names :   Abedallah Zaid Abualkishik   1 *     Rasha Almajed   2     William Thompson   3  

1  Affiliation :  American University in the Emirates, Dubai, UAE

    Email :  abedallah.abualkishik@aue.ae


2  Affiliation :  American University in the Emirates, Dubai, UAE

    Email :  rasha.almajed@aue.ae


3  Affiliation :  Towson University, Towson University, Maryland's University, USA

    Email :  wvthompson@towson.edu



Doi   :   https://doi.org/10.54216/FPA.080103

Received: April 17, 2022 Accepted: August 21, 2022

Abstract :

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.

Keywords :

Data Fusion; Internet of Things; Cloud computing; Load balancing; Energy efficiency; Red deer algorithm

References :

[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.


Cite this Article as :
Abedallah Zaid Abualkishik , Rasha Almajed , William Thompson, Intelligent Red Deer Algorithm based Energy Aware Load Balancing Scheme for Data Fusion in Cloud Environment, Fusion: Practice and Applications, Vol. 8 , No. 1 , (2022) : 27-38 (Doi   :  https://doi.org/10.54216/FPA.080103)