An Efficient Dynamic Load Balancing and Resource Provisioning Scheme for Cloud Computing Environments
DOI:
https://doi.org/10.63682/jns.v14i16S.4299Keywords:
Cloud Computing, Resource Provisioning, Service Level Agreements, Machine Learning, Neural Network, Fault ToleranceAbstract
Effective resource management is essential for maximizing performance and guaranteeing adherence to service level agreements (SLAs) in the ever-changing world of cloud computing. While reactive fault tolerance techniques usually handle problems only after they arise, resulting in downtime and inefficiencies, traditional load balancing techniques frequently find it difficult to adjust to changing demands. This study suggests a novel hybrid approach that uses machine learning techniques to combine proactive fault tolerance mechanisms with dynamic load balancing. The system predicts changes in workload and possible errors by examining real-time metrics and previous data, which allows for more efficient resource allocation and a reduction in SLA breaches. According to preliminary findings, resource utilization has improved by more than 80%, and fault recovery times have significantly decreased . By providing a thorough foundation for further study and opening the door for more robust cloud computing systems that put efficiency and dependability first, this work fills in the gaps in literature
Downloads
Metrics
References
H. Shukur, S. Zeebaree, R. Zebari, D. Zeebaree, O. Ahmed, and A. Salih, ‘‘Cloud computing virtualization of resources allocation for distributed systems,’’ J. Appl. Sci. Technol. Trends, vol. 1, no. 3, pp. 98–105, Jun. 2020, doi: 10.38094/jastt1331.
M. Agarwal and G. M. Saran Srivastava, ‘‘Cloud computing: A paradigm shift in the way of computing,’’ Int. J. Mod. Educ. Comput. Sci., vol. 9, no. 12, pp. 38–48, Dec. 2017, doi: 10.5815/ijmecs.2017.12.05.
N. Zanoon, ‘‘Toward cloud computing: Security and performance,’’ Int. J. Cloud Comput.: Services Archit., vol. 5, no. vol. 5, nos. 5–6, pp. 17–26, Dec. 2015, doi: 10.5121/ijccsa.2015.5602.
C. T. S. Xue and F. T. W. Xin, ‘‘Benefits and challenges of the adoption of cloud computing in business,’’ Int. J. Cloud Comput.: Services Archit., vol. 6, no. 6, pp. 1–15, Dec. 2016, doi: 10.5121/ijccsa.2016.6601.
D. A. Shafiq, N. Jhanjhi, and A. Abdullah, ‘‘Proposing a load balancing algorithm for the optimization of cloud computing applications,’’ in Proc. 13th Int. Conf. Math., Actuarial Sci., Comput. Sci. Statist. (MACS), Dec. 2019, pp. 1–6, doi: 10.1109/MACS48846.2019.9024785.
S. K. Mishra, B. Sahoo, and P. P. Parida, ‘‘Load balancing in cloud computing: A big picture,’’ J. King Saud Univ.–Comput. Inf. Sci., vol. 32, no. 2, pp. 149–158, 2020, doi: 10.1016/j.jksuci.2018.01.003.
I. Odun-Ayo, M. Ananya, F. Agono, and R. Goddy-Worlu, ‘‘Cloud computing architecture: A critical analysis,’’ in Proc. 18th Int. Conf. Comput. Sci. Appl. (ICCSA), Jul. 2018, pp. 1–7, doi: 10.1109/ICCSA.2018.8439638.
A. Jyoti, M. Shrimali, and R. Mishra, ‘‘Cloud computing and load balancing in cloud computing -survey,’’ in Proc. 9th Int. Conf. Cloud Comput., Data Sci. Eng. (Confluence), Jan. 2019, pp. 51–55, doi: 10.1109/confluence.2019.8776948.
S. H. H. Madni, M. S. Abd Latiff, M. Abdullahi, S. M. Abdulhamid, and M. J. Usman, ‘‘Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment,’’ PLoS ONE, vol. 12, no. 5, May 2017, Art. no. e0176321, doi: 10.1371/journal.pone.0176321.
M. Adhikari and T. Amgoth, ‘‘Heuristic-based load-balancing algorithm for IaaS cloud,’’ Future Gener. Comput. Syst., vol. 81, pp. 156–165, Apr. 2018, doi: 10.1016/j.future.2017.10.035.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
