Predictive Analytics for Cloud Resource Allocation using Artificial Intelligence
Main Article Content
Abstract
Cloud computing environments face significant challenges in efficient resource allocation due to dynamic workloads, fluctuating user demands, and Quality of Service (QoS) constraints. Traditional static and rule-based provisioning approaches often result in underutilization or overprovisioning of resources, leading to increased operational costs and degraded system performance. This research presents an Artificial Intelligence (AI)-driven predictive analytics framework for intelligent cloud resource allocation. The proposed approach leverages machine learning algorithms to analyze historical workload patterns, system utilization metrics, and user behavior data to forecast future resource requirements accurately.
The framework integrates time-series forecasting and supervised learning models to dynamically optimize the allocation of computing, storage, and network resources in real time. By predicting workload spikes and demand variability, the system enables proactive scaling of virtual machines and containers, thereby minimizing latency and improving resource utilization efficiency. Performance evaluation is conducted using standard cloud performance metrics, including accuracy, response time, resource utilization rate, and Quality of Service (QoS) compliance. Experimental results demonstrate that the AI-based predictive model significantly reduces resource wastage and operational costs while enhancing system reliability and scalability compared to conventional allocation techniques.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
H. Chen, F. Wang, N. Helian, and G. Akanmu, “User behavior aware task scheduling in cloud computing,” Computers & Electrical Engineering, vol. 48, pp. 447–460, 2015, doi:10.1016/j.compeleceng.2015.01.019.
B. Lu, L. Liu, J. Panneerselvam, B. Yuan, J. Gu, and N. Antonopoulos, “A GRU-based prediction framework for intelligent resource management at cloud data centres in the age of 5G,” IEEE Trans. Cognitive Commun. Networking, vol. 6, no. 2, pp. 486–496, Jun. 2019, doi:10.1109/TCCN.2019.2954388.
T. Khan, W. Tian, G. Zhou, S. Ilager, M. Gong, and R. Buyya, “Machine learning (ML)-centric resource management in cloud computing: A review and future directions,” J. Network and Computer Applications, vol. 204, p. 103405, 2022, doi:10.1016/j.jnca.2022.103405.
S. Xue et al., “A meta reinforcement learning approach for predictive autoscaling in the cloud,” arXiv, May 2022.
R. Sridhar, R. N. Kumar Dhenia, and I. J. Kanan, “A machine learning framework for predictive workload modeling and dynamic cloud resource allocation,” Int. J. AI, Data Sci., and ML, vol. 4, no. 1, 2022, doi:10.63282/3050-9262.IJAIDSML-V4I1P107.
S. Malik et al., “A resource utilization prediction model for cloud data centers using evolutionary algorithms and machine learning techniques,” Appl. Sci., vol. 12, no. 4, p. 2160, 2022, doi:10.3390/app12042160.
D. Bodra and S. Khairnar, “Machine learning-based cloud resource allocation algorithms: a comprehensive comparative review,” Front. Comput. Sci., vol. 7, Oct. 2025, doi:10.3389/fcomp.2025.1678976.
S. Kayalvili, R. Senthilkumar, S. Yasotha, and R. S. Kamalakannan, “An optimized resource allocation in cloud using prediction enabled reinforcement learning,” Sci. Reports, vol. 15, Art. no. 36088, Oct. 2025.
V. Reddy Nadagouda, “AI and automation in capacity planning: predicting and managing cloud resource demands,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 11, no. 2, pp. 709–719, Mar. 2025, doi:10.32628/CSEIT25112420.
S. Kumar Choudhary, “AI-powered predictive analytics for dynamic cloud resource optimization: a technical implementation framework,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 11, no. 1, pp. 1267–1275, Jan.–Feb. 2025, doi:10.32628/CSEIT251112122.
Y. Wang and X. Yang, “Intelligent resource allocation optimization for cloud computing via machine learning,” Adv. Comput. Signals Syst., vol. 9, no. 1, pp. 55–63, 2025, doi:10.23977/acss.2025.090109.
D. E. Ajeh, J. Ellman, and S. Keogh, “A cost modelling system for cloud computing,” in Proc. 14th Int. Conf. Computational Science and Its Applications (ICCSA), 2014, pp. 567–578.
X. Xiao, M. Zhao, and Y. Zhu, “Multi-stage resource-aware congestion control algorithm in edge computing environment,” Energy Reports, vol. 8, pp. 6321–6331, 2022.
S. S. Sefati et al., “A probabilistic workload forecasting and adaptive provisioning framework,” Electron., vol. 14, no. 16, 2025.
T. Le Duc, C. Nguyen, and P. O. Östberg, “Workload prediction for proactive resource allocation in large-scale cloud-edge applications,” Electronics, vol. 14, no. 16, 2025, doi:10.3390/electronics14163333.
A. Rossi et al., “Forecasting workload in cloud computing: towards uncertainty-aware predictions and transfer learning,” Cluster Comput., vol. 28, no. 4, 2025, doi:10.1007/s10586-024-04933-2.
G. Peng, H. Wang, J. Dong, and H. Zhang, “Knowledge-based resource allocation for collaborative simulation development in a multi-tenant cloud computing environment,” IEEE Trans. Serv. Comput., vol. 11, no. 2, pp. 306–317, Mar. 2018.
R. Srivastava, S. Gupta, P. K. Tiwari, and M. Kaur, “Resource management on cloud computing using machine learning,” in Proc. Int. Conf. Computational Intelligence and Networks, 2024.
P. Pradhan, P. K. Behera, and B. N. B. Ray, “Modified round robin algorithm for resource allocation in cloud computing,” Procedia Comput. Sci., vol. 85, pp. 878–890, 2016.
Z. Sharif, L. Tang Jung, M. Ayaz, M. Yahya, and S. Pitafi, “Priority-based task scheduling and resource allocation in edge computing for health monitoring systems,” J. King Saud Univ. – Comput. Inf. Sci., vol. 35, no. 2, pp. 544–559, 2023.
P. Wei, Y. Zeng, B. Yan, J. Zhou, and E. Nikougoftar, “VMP-A3C: Virtual machines placement in cloud computing based on asynchronous advantage actor-critic algorithm,” J. King Saud Univ. – Comput. Inf. Sci., vol. 35, no. 5, p. 101549, 2023.
R. Yang and J. Xu, “Computing at massive scale: scalability and dependability challenges,” in Proc. IEEE Symp. Service-Oriented System Engineering (SOSE), Oxford, UK, 2016.