Virtual Machine Load Balancing Model Framework for Cloud Computing
Received: 13 February 2025 | Revised: 26 February 2025 | Accepted: 7 March 2025 | Online: 27 March 2025
Corresponding author: E. Suganthi
Abstract
Cloud Computing (CC) is a comprehensive paradigm that enables individuals and businesses to acquire necessary services on demand. CC provides numerous services, including archiving, distribution platforms, and easy access to online services. Implementing CC necessitates overcoming various difficulties, such as resource identification, protection, scheduling, and Load Balancing (LB). This study examines LB, which distributes workloads across cloud systems to ensure fair resource allocation and prevent Virtual Machines (VMs) from becoming over- or under-loaded. An effective LB solution is essential to maximize VM resource utilization while ensuring high user satisfaction. This study develops the VM LB model framework for CC, which includes a state and random model, a Weight Factor (WF) and priority-based model, and a two-stage optimal model. These models efficiently allocate the VM to the Physical Machine (PM) using Cloudsim. The PlanetLab workload evaluates the performance of the models in terms of Energy Consumption (EC) and Service Level Agreement Violation (SLAV). The experimental results indicate that the proposed model improves Service Level Agreement (SLA) compliance and energy efficiency.
Keywords:
cloud computing, load balancing, virtual machine, state, priority, optimizationDownloads
References
H. M. Shukur, S. R. M. Zeebaree, R. R. Zebari, D. Q. Zeebaree, O. M. Ahmed, and A. A. Salih, "Cloud Computing Virtualization of Resources Allocation for Distributed Systems," Journal of Applied Science and Technology Trends, vol. 1, no. 2, pp. 98–105, Jun. 2020.
D. A. Shafiq, N. Z. Jhanjhi, A. Abdullah, and M. A. Alzain, "A Load Balancing Algorithm for the Data Centres to Optimize Cloud Computing Applications," IEEE Access, vol. 9, pp. 41731–41744, 2021.
M. Jesi, A. Appathurai, M. Narayanaperumal, and A. Kumar, "Load Balancing in Cloud Computing via Mayfly Optimization Algorithm," Revue Roumaine des Sciences Techniques — Série Électrotechnique et Énergétique, vol. 69, no. 1, pp. 79–84, Apr. 2024.
S. Swarnakar, S. Bhattacharya, and C. Banerjee, "A Bio-Inspired and Heuristic-Based Hybrid Algorithm for Effective Performance With Load Balancing in Cloud Environment," International Journal of Cloud Applications and Computing, vol. 11, no. 4, pp. 59–79, Oct. 2021.
H. Singh, S. Tyagi, P. Kumar, S. S. Gill, and R. Buyya, "Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions," Simulation Modelling Practice and Theory, vol. 111, Sep. 2021, Art. no. 102353.
D. A. Shafiq, N. Z. Jhanjhi, and A. Abdullah, "Load balancing techniques in cloud computing environment: A review," Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 7, pp. 3910–3933, Jul. 2022.
J. P. B. Mapetu, L. Kong, and Z. Chen, "A dynamic VM consolidation approach based on load balancing using Pearson correlation in cloud computing," The Journal of Supercomputing, vol. 77, no. 6, pp. 5840–5881, Jun. 2021.
H. Li, T. Li, and Z. Shuhua, "Energy-performance optimisation for the dynamic consolidation of virtual machines in cloud computing," International Journal of Services Operations and Informatics, vol. 9, no. 1, pp. 62–82, Jan. 2018.
H. Wang and H. Tianfield, "Energy-Aware Dynamic Virtual Machine Consolidation for Cloud Datacenters," IEEE Access, vol. 6, pp. 15259–15273, 2018.
S. Azizi, M. Shojafar, J. Abawajy, and R. Buyya, "GRVMP: A Greedy Randomized Algorithm for Virtual Machine Placement in Cloud Data Centers," IEEE Systems Journal, vol. 15, no. 2, pp. 2571–2582, Jun. 2021.
S. Talwani et al., "Machine-Learning-Based Approach for Virtual Machine Allocation and Migration," Electronics, vol. 11, no. 19, Oct. 2022, Art. no. 3249.
R. Sharma and B. Sinha, "Load Balancing and Server Consolidation for Energy Management in Cloud Data Center," ITM Web of Conferences, vol. 54, 2023, Art. no. 01017.
D. Dabhi and D. Thakor, "Utilisation-aware VM placement policy for workload consolidation in cloud data centres," International Journal of Communication Networks and Distributed Systems, vol. 28, no. 6, pp. 704–726, Jan. 2022.
S. Vila, F. Guirado, and J. L. Lérida, "Cloud computing virtual machine consolidation based on stock trading forecast techniques," Future Generation Computer Systems, vol. 145, pp. 321–336, Aug. 2023.
M. Radi, A. A. Alwan, and Y. Gulzar, "Genetic-Based Virtual Machines Consolidation Strategy With Efficient Energy Consumption in Cloud Environment," IEEE Access, vol. 11, pp. 48022–48032, 2023.
D. Alsadie and M. Alsulami, "Efficient Resource Management in Cloud Environments: A Modified Feeding Birds Algorithm for VM Consolidation," Mathematics, vol. 12, no. 12, Jun. 2024, Art. no. 1845.
H. S. Madhusudhan, S. Kumar T., P. Gupta, and G. McArdle, "A Harris Hawk Optimisation system for energy and resource efficient virtual machine placement in cloud data centers," Plos One, vol. 18, no. 8, Aug. 2023, Art. no. e0289156.
S. Durairaj and R. Sridhar, "MOM-VMP: multi-objective mayfly optimization algorithm for VM placement supported by principal component analysis (PCA) in cloud data center," Cluster Computing, vol. 27, no. 2, pp. 1733–1751, Jun. 2023.
P. K. Mishra and A. K. Chaturvedi, "An Improved Laxity based Cost Efficient Task Scheduling Approach for Cloud-Fog Environment," Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 19037–19044, Feb. 2025.
E. Suganthi and F. Kurus Malai Selvi, "Cloud Computing by Implementing State and Random-Based Virtual Machine Load Balancing Model," International Journal of Intelligent Engineering and Systems, vol. 17, no. 3, pp. 92–101, Jun. 2024.
E. Suganthi and F. Kurus Malai Selvi, "Weight factor and priority-based virtual machine load balancing model for cloud computing," International Journal of Information Technology, vol. 16, no. 8, pp. 5271–5276, Dec. 2024.
Z. Á. Mann and M. Szabó, "Which is the best algorithm for virtual machine placement optimization?," Concurrency and Computation: Practice and Experience, vol. 29, no. 10, Apr. 2017, Art. no. e4083.
Z. Ma, D. Ma, M. Lv, and Y. Liu, "Virtual Machine Migration Techniques for Optimizing Energy Consumption in Cloud Data Centers," IEEE Access, vol. 11, pp. 86739–86753, 2023.
Downloads
How to Cite
License
Copyright (c) 2025 E. Suganthi, F. Kurus Malai Selvi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
