A Review of Cost Management Approaches in Cloud Computing
Article Sidebar
Main Article Content
Cloud computing has revolutionized the IT industry by offering scalable and on-demand resources under a pay-as-you-go model. However, the same flexibility that makes cloud computing attractive also introduces challenges in managing costs effectively. Cloud Cost Management (CCM) focuses on monitoring, analyzing and optimizing expenditure to achieve an ideal balance between performance and affordability. This paper reviews existing cost management strategies, techniques, and tools used across different cloud environments. It highlights traditional budgeting and monitoring methods, advanced optimization models using machine learning, and automation through FinOps practices. There view also identifies current challenges such as complex pricing structures, lack of cost visibility in multi-cloud environments, and unpredictable billing patterns. Finally, the paper discusses emerging trends and future research directions aimed at intelligent and sustainable cost optimization in cloud ecosystems.
Downloads
References
N. Ramesh,A.Banerjee,andV.K.Singh,“Auto-scalingApproachesforCloud-Native Applications: ASurvey and Taxonomy,” Sensors, vol. 24, no. 5, pp. 1–18, Mar. 2024.
T.M. Nguyenand Y. Lee, “Cost Modellingand Optimisation for Cloud:AGraph-Based Approach,”, Journal of Cloud Computing, vol. 13, no. 2, pp. 115 132, 2024
P. GhoshandL. Chen,“CostMinimizationinMulti-CloudSystemswithRuntime Microservice Re-orchestration,” arXiv preprint, arXiv:2403.09121, 2024. [Online].
K.Sharma,D.Raj,andJ.K.Verma,“ABACUS:A FinOps Service for Cloud Cost Optimization,”,arXiv preprint, arXiv:2501.06732, 2025.
M.Zhaoand, R.Gupta,“AdaptiveOrchestrationforPerformance–CostOptimizationin Multi-Cloud,”, SSRN Electronic Journal, 2020.
K. Bedi et al., “Unified Cost Visibility in Multi-Cloud,” IEEETrans. Cloud Comput., vol. 11, pp. 89–101, 2023.
Singh et al., “Blockchain for Cloud Billing Audit,” IEEEAccess, vol. 12, pp. 7823–7836, 2024.
M. Armbrust et al., “A View of Cloud Computing,” Commun.ACM, vol. 53, no. 4, pp. 50–58, 2010.
E. Walker, “The Real Cost of a CPU Hour,” Computer, vol. 42, no.4, pp. 35–41, 2011.
Khajeh-Hosseini et al., “The Cloud Adoption Toolkit,” Softw.Pract. Exp., vol. 42, no. 4, pp. 447–465, 2012.
R. Buyya et al., “Market-Oriented Cloud Resource Allocation,”Future Gener. Comput. Syst., vol. 26, pp. 1012–1023, 2013.
Beloglazov and R. Buyya, “Energy-Efficient Resource Allocation,” Future Gener. Comput. Syst., vol. 28, pp. 755–768, 2012.
S. Jain and R. Gupta, “Policy-Based Cloud Optimization,” IEEE Cloud Comput., vol. 6, pp. 40–48, 2019.
X. Li and Y. Chen, “ARIMA Forecasting in Cloud Environments,”Future Gener. Comput. Syst., vol. 107, pp. 509–519, 2020.
J. Zhao et al., “LSTM-Based Cost Forecasting,” IEEE Access, vol. 9, pp. 18123–18135, 2021.
M. Yadav, A. Mishra, Energy-efficient workflow scheduling using dynamic task clustering for sustainable cloud computing. Discov Computing, 28, article id 201, 2025. https://doi.org/10.1007/s10791-025-09712-0.
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.