FinOps-Driven Strategies for Large-Scale Cloud Cost Optimization
Keywords:
Cloud, FinOps, Cost Optimization, StrategiesAbstract
The high migrations into cloud computing among the big businesses impose some challenges in terms of cost forecasting, visibility, and control. In this paper, the author presents the potential impacts of FinOps as an innovative model of bridging financial responsibility and engineering nimbleness. We will see the three fundamental stages namely Inform, Optimize and Operate and point out the clays in action namely the tagging, chargeback models, rightsizing and rate optimization in AWS, Azure and GCP. We produce cost savings of 20 to 40 percent through real life case studies. The research findings define FinOps as a tool of cost containment but also a strategic tool to maximize ROI, work on collaboration and instil a culture of financial responsibility to cloud-native environments.
Downloads
References
Insight, "Cloud-Native Software Company Captures Competitive Edge With FinOps," insight.com, Jul. 12, 2023
Li, J., & Wang, X. (2022). Cost-minimized microservice migration strategies with machine learning. IEEE Transactions on Cloud Computing, 10(3), 487-499.
Xiao, Y., & Liu, J. (2021). Cost-efficient load balancing for cloud computing applications. Journal of Cloud Computing Research, 14(6), 178-189.
R. Patel and Y. Zhang, "The future of FinOps: Integrating AI in cloud cost management," IEEE Transactions on Cloud Computing, vol. 8, no. 3, pp. 67-79, 2020
Manvi, S. S., & Shyam, G. K. (2021). Green computing-based cost optimization in cloud systems. Journal of Environmental Computing, 13(3), 191-203.
Wang, L., & Lu, X. (2022). Dynamic cost-aware resource allocation in multi-cloud environments. IEEE Transactions on Parallel and Distributed Systems, 33(12), 3370-3385.
Ahmad, S. G., Iqbal, T., Munir, E. U., & Ramzan, N. (2023). Cost optimization in cloud environment based on task deadline. Journal of Cloud Computing Advances Systems and Applications, 12(1). https://doi.org/10.1186/s13677-022-00370-x
Yadav, N., & Singh, A. (2022). Reducing operational costs through efficient cloud migration strategies. Proceedings of the IEEE International Conference on Cloud Computing, 412-419.
Dixit, A., & Kumar, R. (2020). Predictive resource scaling strategies for cost optimization in cloud services. IEEE Access, 8, 120345-120356.
Sharma, P., & Agrawal, D. (2023). Automated cost optimization in cloud services using reinforcement learning. ACM Transactions on Cloud Computing, 10(4), 275-284
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
