Citation :

Hari Shankar Punna, Arif Mohammad Abdul, "Deriving a Multi-Objective Function for Load Balancing in Cloud Environment Via Adaptive Bayesian Belief Network with Ridge Regression-based Load Prediction," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 10, pp. 192-212, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I10P114

Abstract

Cloud infrastructures require intelligent workload distribution mechanisms to maintain optimal throughput, control power consumption, and ensure efficient resource utilization. Traditional load balancing approaches fail to address several persistent issues - including workload unpredictability, delayed response cycles, server overutilization, and suboptimal task scheduling. This study proposes an Adaptive Bayesian Belief Network-Ridge Regression framework, termed MRA-SAO-ABBNRR, which employs a Modified Random Attribute Snow Ablation Optimizer. The proposed model integrates Bayesian Belief Networks with Ridge Regression to forecast future workload patterns from historical cloud telemetry. Overfitting is mitigated through correlation analysis across input attributes. Additionally, a modified random attribute snow ablation optimization scheme is used to fine-tune workload prediction and to support a multi-objective task allocation strategy spanning resource utilization, response time, makespan, scalability, and energy efficiency. All experiments were conducted in MATLAB 2020a, and benchmark comparisons were drawn against four existing models, namely Convolutional Neural Network-Long Short-Term Memory, Variable Model Matching Intelligent Self-Distillation, Ensemble Prediction and Risk Uncertainty Assessment, and Bayesian Belief Networks-Ridge Regression. The proposed framework delivered superior prediction accuracy alongside more effective load balancing. It recorded the lowest SMAPE of 0.2 when paired with the sigmoid activation function, outperforming every baseline. The model also demonstrated reduced utilization of compute resources, lower operational expenditure, and decreased energy demand.

Keywords

Bayesian belief network, Cloud load balancing, Multi-objective optimization, Ridge regression, Snow ablation optimizer.

References

1. Arabinda Pradhan et al., “Intelligent Decision-Making of Load Balancing using Deep Reinforcement Learning and Parallel PSO in Cloud Environment,” IEEE Access, vol. 10, pp. 76939-76952, 2022.
   [CrossRef] [Google Scholar] [Publisher Link] 
2. Mana Saleh Al Reshan et al., “A Fast Converging and Globally Optimized Approach for Load Balancing in Cloud Computing,” IEEE Access, vol. 11, pp. 11390-11404, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]                                       
3. Madala Guru Brahmam, and Vijay Anand R, “VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics with Iterative Security Measures and Deep Learning Optimizations,” IEEE Access, vol. 12, pp. 39351-39374, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
4. Kaviarasan R et al., “Effective Load Balancing Approach in Cloud Computing using Inspired Lion Optimization Algorithm,” e-Prime- Advances in Electrical Engineering, Electronics and Energy, vol. 6, pp. 1-12, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
5. P. Udayasankaran, and S. John Justin Thangaraj, “Energy Efficient Resource Utilization and Load Balancing in Virtual Machines using Prediction Algorithms,” International Journal of Cognitive Computing in Engineering, vol. 4, pp. 127-134, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
6. B. Edward Gerald, P. Geetha, and E. Ramaraj, “A Fruitfly-based Optimal Resource Sharing and Load Balancing for the Better Cloud Services,” Soft Computing, vol. 27, no. 10, pp. 6507-6520, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
7. Sarita Simaiya et al., “A Hybrid Cloud Load Balancing and host Utilization Prediction Method using Deep Learning and Optimization Techniques,” Scientific Reports, vol. 14, no. 1, pp. 1-18, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
8. Amanpreet Kaur et al., “Load Balancing Optimization based on Deep Learning Approach in Cloud Environment,” International Journal of Information Technology and Computer Science(IJITCS), vol. 12, no. 3, pp. 8-18, 2020.
   [CrossRef] [Google Scholar] [Publisher Link]
9. G. Muneeswari et al., “GEP Optimization for Load Balancing of Virtual Machines (LBVM) in Cloud Computing,” Measurement: Sensors, vol. 33, pp. 1-6, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
10. Deepal Tennakoon, Morshed Chowdhury, and Tom H. Luan, “Cloud-based Load Balancing using Double Q-Learning for Improved Quality of Service,” Wireless Networks, vol. 29, no. 3, pp. 1043-1050, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]  
11. Jyoti Singh et al., “Load-Balancing Strategy: Employing a Capsule Algorithm for Cutting Down Energy Consumption in Cloud Data Centers for Next Generation Wireless Systems,” Computational Intelligence and Neuroscience, vol. 2023, no. 1, pp. 1-13, 2023.  
    [CrossRef] [Google Scholar] [Publisher Link]
12. Maria Jesi et al., “Load Balancing in Cloud Computing via Mayfly Optimization Algorithm,” Romanian Journal of Technical Sciences – Electrical Engineering and Energy, vol. 69, no. 1, pp. 79-84, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
13. Adeyinka Ajao Adewale et al., “Hybridization of the Q-Learning and Honey Bee Foraging Algorithms for Load Balancing in Cloud Environments,” International Journal of Electrical and Computer Engineering (IJECE), vol. 14, no. 4, pp. 4602-4615, 2024.
    [CrossRef] [Google Scholar] [Publisher Link] 
14. Kavish Chawla, “Reinforcement Learning-based Adaptive Load Balancing for Dynamic Cloud Environments,” arXiv preprint, pp. 1-6, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
15. Prathamesh Vijay Lahande et al., “Reinforcement Learning Approach for Optimizing Cloud Resource Utilization with Load Balancing,” IEEE Access, vol. 11, pp. 127567-127577, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
16. S. Sree Priya, and T. Rajendran, “Improved Round-Robin Rule Learning for Optimal Load Balancing in Distributed Cloud Systems,” International Journal of System of Systems Engineering, vol. 13, no. 1, pp. 83-99, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
17. Minal Shahakar, Surenda Mahajan, and Lalit Patil, “Load Balancing in Distributed Cloud Computing: A Reinforcement Learning Algorithms in Heterogeneous Environment,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 2, pp. 65-74, 2023.
    [CrossRef] [Google Scholar] [Publisher Link] 
18. Ankita Srivastava, and Narander Kumar, “An Efficient Firefly and Honeybee based Load Balancing Mechanism in Cloud Infrastructure,” Cluster Computing, vol. 27, no. 3, pp. 2805-2827, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]    
19. Thanwamas Kassanuk, and Khongdet Phasinam, “A Hybrid Binary Bird Swarm Optimization (BSO) and Dragonfly Algorithm (DA) for VM Allocation and Load Balancing in Cloud,” International Journal of Cloud Applications and Computing (IJCAC), vol. 13, no. 1, pp. 1-21, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
20. Raghavender Reddy Kothi Laxman et al., “PGWO-AVS-RDA: An Intelligent Optimization and Clustering based Load Balancing Model in Cloud,” Concurrency and Computation: Practice and Experience, vol. 34, no. 21, pp. 1-10, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
21. Garima Verma, “Load Balancing in Cloud Environment using Opposition based Spider Monkey Optimization,” Wireless Personal Communications, vol. 137, no. 2, pp. 977-996, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
22.  Shruthi G. et al., “Deep Learning-based Resource Prediction and Mutated Leader Algorithm Enabled Load Balancing in Fog Computing,” International Journal of Computer Network and Information Security (IJCNIS), vol. 15, no. 4, pp. 84-95, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
23. Peng Yang et al., “Reducing Idleness in Financial Cloud Services Via Multi-Objective Evolutionary Reinforcement Learning based Load Balancer,” Science China Information Sciences, vol. 67, no. 2, pp. 1-21, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
24. Tanzila Saba et al., “Cloud-Edge Load Balancing Distributed Protocol for IoE Services using Swarm Intelligence,” Cluster Computing, vol. 26, no. 5, pp. 2921-2931, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
25. Ibrahim Aqeel et al., “Load Balancing using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain,” Sensors, vol. 23, no. 11, pp. 1-15, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
26. Lingyun Deng, and Sanyang Liu, “Snow Ablation Optimizer: A Novel Metaheuristic Technique for Numerical Optimization and Engineering Design,” Expert Systems with Applications, vol. 225, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
27. Vivek Shrivastava, and R.B. Misra, “Development of Bayesian Belief Network Model for Electrical Load Demand,” International Journal of Systems Assurance Engineering and Management vol. 1, no. 2, pp. 170-177, 2010.
    [CrossRef] [Google Scholar] [Publisher Link]  
28. Jae Joon Ahn et al., “Using Ridge Regression with Genetic Algorithm to Enhance Real Estate Appraisal Forecasting,” Expert Systems with Applications, vol. 39, no. 9, pp. 8369-8379, 2012.
    [CrossRef] [Google Scholar] [Publisher Link]
29. Zhiyu Feng et al., “A Jaya Algorithm based on Self-Adaptive Method for Parameters Identification of Photovoltaic Cell and Module,” Cluster Computing, vol. 28, no. 2, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
30. Linfei Yin, and Wenyu Ding, “Dual Deep Neural Networks-Accelerated Non-Dominated Sorting Moth Flame Optimizer for Distributed Multi-Objective Economic Dispatch,” Expert Systems with Applications, vol. 259, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
31. Eva Trojovská, Mohammad Dehghani, and Víctor Leiva, “Drawer Algorithm: A New Metaheuristic Approach for Solving Optimization Problems in Engineering,” Biomimetics, vol. 8, no. 2, pp. 1-35, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]