AI-Based Cost Optimization Model for Cloud Resource Management

The AI-based cost optimization model for cloud resource management provides an efficient and intelligent approach to managing cloud infrastructure. The proposed system integrates machine learning models and optimization techniques to analyse cloud resource usage patterns, predict future workload demands, and make intelligent resource allocation decisions. By utilizing LSTM, ARIMA, and Random Forest algorithms, the system accurately forecasts both workload requirements and cloud cost trends, enabling proactive and cost-effective resource management. The integration of reinforcement learning further improves the system by enabling dynamic and automated decision-making. Unlike traditional static resource allocation methods, the proposed model continuously learns from real-time cloud data and automatically adjusts resource provisioning according to changing workloads. This helps reduce both over-provisioning and under-utilization, resulting in significant cost savings while maintaining system performance, availability, and reliability. Experimental results demonstrate that the proposed model achieves higher prediction accuracy, better resource utilization, and lower operational costs compared to conventional cloud management approaches. Furthermore, the system supports efficient cloud resource allocation while maintaining Service Level Agreement (SLA) compliance, making it suitable for real-world cloud environments. Although challenges such as data dependency and computational complexity remain, the proposed AI-based framework provides a scalable and practical solution. Overall, it effectively combines predictive analytics, intelligent automation, and optimization techniques to improve cloud resource management and minimize operational costs.

[1] S. R. Swain, A. K. Singh, and C. N. Lee, “Efficient Resource Management in Cloud Environment,” arXiv preprint arXiv:2207.12085, 2022. [2] Z. Qu, M. Dawande, and G. Janakiraman, “Cloud Cost Optimization: Model, Bounds, and Asymptotics,” Operations Research, vol. 72, no. 1, pp. 132–150, 2024. [3] P. Nawrocki and M. Smendowski, “Optimization of the Use of Cloud Computing Resources Using Exploratory Data Analysis and Machine Learning,” Journal of Artificial Intelligence and Soft Computing Research, vol. 14, no. 4, pp. 275–289, 2024. [4] K. Sathupadi, R. Avula, A. Velayutham, and S. Achar, “RAP-Optimizer: Resource-Aware Predictive Model for Cost Optimization of Cloud AIaaS Applications,” Electronics, vol. 13, no. 22, Art. no. 4462, 2024. [5] G. P. Rusum and S. Anasuri, “AI-Augmented Cloud Cost Optimization: Automating FinOps with Predictive Intelligence,” International Journal of Artificial Intelligence, Data Science and Machine Learning, vol. 5, no. 2, pp. 82–94, 2024. [6] K. Vhatkar, A. B. Kathole, A. P. Kshirsagar, and J. Katti, “Improved Optimization Algorithm for Resource Management in Cloud Applications with Performance Monitor of VM Provisioning, Placement and Recycling,” Journal of Healthcare Engineering, 2024. [7] Y. Zhang, X. Li, and H. Wang, “Artificial Intelligence for Cloud Resource Management and Cost Optimization: A Review,” IEEE Access, vol. 12, pp. 45678–45701, 2024. [8] Y. Zhang, B. Liu, Y. Gong, J. Huang, J. Xu, and W. Wan, “Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management,” arXiv preprint arXiv:2402.17216, 2024. [9] A. Jayanetti, S. Halgamuge, and R. Buyya, “A Deep Reinforcement Learning Approach for Cost Optimized Workflow Scheduling in Cloud Computing Environments,” arXiv preprint arXiv:2408.02926, 2024. [10] H. Wang, S. S. Gill, et al., “Ainet0: AI Forecasting Based Carbon Neutral Cloud Resource Management for Net Zero Targets,” Transactions on Emerging Telecommunications Technologies, 2025.