eISSN: 3087-4262 editor@aimjournals.com
All Journals
Submit your article

International Journal of Intelligent Data and Machine Learning

Open Access Peer Review International
Open Access

Enhancing Cloud Resource Management Through Machine Learning-Based Utilization Prediction

DOI https://doi.org/10.55640/ijidml-v03i11-01
PDF
Vivek Sharma 1 ,
4 Designation: System Analyst, MITS Deemed University, India

Abstract

Cloud resource management is important in the provision of efficiency in the allocation, scalability, and performance of virtualized computing environments. The ability to predict resource consumption is a very important and highly effective machine learning method that supports cloud operations in a perfect way by predicting future resource usage. An improved method for analyzing Microsoft Azure Trace data for forecasting is proposed in this research called BiLSTM-GRU, a deep learning architecture that combines Bidirectional Long Short-Term Memory with Gated Recurrent Units. The process for producing high-quality time-series inputs includes thoroughly cleaning the data, reducing noise, engineering features, and normalizing. BiLSTM captures temporal dependencies in both directions while GRU sequentially learns efficiently via gated operations, thus enabling both short- and long-term workload trend capture by the model. The LiR model outperforms the other linear resource trend models in terms of R² and RMSE (0.0169) according to the experimental data, while the BiLSTM-GRU model boasts the greatest prediction accuracy (0.9832%). The hybrid model's detection of the complex temporal variations in cloud workloads is further validated by a comparative analysis against the current NN, BP, and ARIMA models. The BiLSTM-GRU model is not only resourceful but also stable and offers correct CPU usage predictions, which enhance the efficiency of the cloud.

Keywords

Cloud Resource Management, Machine Learning, Utilization Prediction

References

Pezzuolo, V. Milani, D. Zhu, H. Guo, S. Guercini, and F. Marinello, “On-Barn Pig Weight Estimation Based on Body Measurements by Structure-from-Motion (SfM),” Sensors, vol. 18, no. 11, p. 3603, Oct. 2018, doi: 10.3390/s18113603.
Parupalli and H. Kali, “An In-Depth Review of Cost Optimization Tactics in Multi-Cloud Frameworks,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 3, no. 5, pp. 1043–1052, Jun. 2023, doi: 10.48175/IJARSCT-11937Q.
L. Wang and Y. Liao, “A short-term hybrid wind speed prediction model based on decomposition and improved optimization algorithm,” Front. Energy Res., vol. 11, pp. 1–17, Nov. 2023, doi: 10.3389/fenrg.2023.1298088.
M. R. R. Deva and N. Jain, “Utilizing Azure Automated Machine Learning and XGBoost for Predicting Cloud Resource Utilization in Enterprise Environments,” in 2025 International Conference on Networks and Cryptology (NETCRYPT), IEEE, May 2025, pp. 535–540. doi: 10.1109/NETCRYPT65877.2025.11102235.
Zhou and D. Fu, “Soft Measurement of Wind Tunnel Dynamic Flow Based on Attention_LSTM⁃ Kalman Modeling,” J. Data Acquis. Process., vol. 37, no. 2, pp. 463–470, 2022, doi: 10.16337/j.1004-9037.2022.02.019.
S. BuchiReddy Karri, C. M. Penugonda, S. Karanam, M. Tajammul, S. Rayankula, and P. Vankadara, “Enhancing Cloud-Native Applications: A Comparative Study of Java-To-Go Micro Services Migration,” Int. Trans. Electr. Eng. Comput. Sci., vol. 4, no. 1, pp. 1–12, Apr. 2025, doi: 10.62760/iteecs.4.1.2025.127.
G. Kaur, A. Bala, and I. Chana, “An intelligent regressive ensemble approach for predicting resource usage in cloud computing,” J. Parallel Distrib. Comput., vol. 123, pp. 1–12, Jan. 2019, doi: 10.1016/j.jpdc.2018.08.008.
R. Patel, “Advancements in Renewable Energy Utilization for Sustainable Cloud Data Centers: A Survey of Emerging Approaches,” Int. J. Curr. Eng. Technol., vol. 13, no. 05, pp. 447–454, Oct. 2023, doi: 10.14741/ijcet/v.13.5.7.
Muteeh, M. Sardaraz, and M. Tahir, “MrLBA: multi-resource load balancing algorithm for cloud computing using ant colony optimization,” Cluster Comput., vol. 24, no. 4, pp. 3135–3145, Dec. 2021, doi: 10.1007/s10586-021-03322-3.
Rahmanian, M. Ghobaei-Arani, and S. Tofighy, “A learning automata-based ensemble resource usage prediction algorithm for cloud computing environment,” Futur. Gener. Comput. Syst., vol. 79, pp. 54–71, Feb. 2018, doi: 10.1016/j.future.2017.09.049.
N. Malik, M. Sardaraz, M. Tahir, B. Shah, G. Ali, and F. Moreira, “Energy-Efficient Load Balancing Algorithm for Workflow Scheduling in Cloud Data Centers Using Queuing and Thresholds,” Appl. Sci., vol. 11, no. 13, p. 5849, Jun. 2021, doi: 10.3390/app11135849.
Mason, M. Duggan, E. Barrett, J. Duggan, and E. Howley, “Predicting host CPU utilization in the cloud using evolutionary neural networks,” Futur. Gener. Comput. Syst., vol. 86, pp. 162–173, Sep. 2018, doi: 10.1016/j.future.2018.03.040.
R. Duggasani, “Scalable and Optimized Load Balancing in Cloud Systems: Intelligent Nature-Inspired Evolutionary Approach,” Int. J. Innov. Sci. Res. Technol., vol. 10, no. 5, pp. 2153–2160, May 2025, doi: 10.38124/ijisrt/25may1290.
Sugawara and H. Nikaido, “Auditoría informática, para la evaluación de riesgos en la seguridad de la información en la Cooperativa de Ahorro y Crédito PRODVISIÓN, de la Provincia de Tungurahua, Canton Pelileo,” Trab. titulación, 2021.
P. Chandrashekar, “A Survey of Tools, Techniques, and Best Practices: CI/CD Integration in DevOps Workflows,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 3, no. 3, pp. 1366–1376, Jul. 2023, doi: 10.48175/IJARSCT-11978V.
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. Netw. Comput. Appl., vol. 204, p. 103405, Aug. 2022, doi: 10.1016/j.jnca.2022.103405.
S. R. Kurakula, “Cloud-native microservices in financial services: Architecting for scalability and flexibility,” World J. Adv. Res. Rev., vol. 26, no. 2, pp. 1435–1442, May 2025, doi: 10.30574/wjarr.2025.26.2.1690.
Kumaran and E. Sasikala, “Computational access point selection based on resource allocation optimization to reduce the edge computing latency,” Meas. Sensors, vol. 24, pp. 1–8, Dec. 2022, doi: 10.1016/j.measen.2022.100444.
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, doi: 10.1016/j.procs.2016.05.278.
Konda, “Performance Evaluation of Machine Learning Inference Workloads in Containerized Cloud Computing Environments,” Eastasouth J. Inf. Syst. Comput. Sci., vol. 3, no. 02, pp. 292–311, Dec. 2025, doi: 10.58812/esiscs.v3i02.949.
Y. Macha and S. K. Pulichikkunnu, “A Survey of DevOps Practices for Machine Learning and Artificial Intelligence Workflows in Modern Software Development,” ESP J. Eng. Technol. Adv., vol. 4, no. 3, pp. 200–208, 2024, doi: 10.56472/25832646/JETA-V4I3P121.
S. Mayur and N. Chaudhary, “Enhanced Weighted Round Robin Load Balancing Algorithm in Cloud Computing,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 9S2, pp. 148–151, Aug. 2019, doi: 10.35940/ijitee.I1030.0789S219.
Alelyani, A. Datta, and G. M. Hassan, “Optimizing Cloud Performance: A Microservice Scheduling Strategy for Enhanced Fault-Tolerance, Reduced Network Traffic, and Lower Latency,” IEEE Access, vol. 12, pp. 35135–35153, 2024, doi: 10.1109/ACCESS.2024.3373316.
Patil, V. Kansal, B. R, T. Manohar, D. Vekariya, and S. Kannan, “Energy-Efficient Resource Allocation for Smart Grids using Deep Reinforcement Learning in IoT Cloud Systems,” in 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), IEEE, Oct. 2024, pp. 67–72. doi: 10.1109/I-SMAC61858.2024.10714834.
Basu, S. Kal, U. Ghosh, and R. Datta, “DRIVE: Dynamic Resource Introspection and VNF Embedding for 5G Using Machine Learning,” IEEE Internet Things J., vol. 10, no. 21, pp. 18971–18979, Nov. 2023, doi: 10.1109/JIOT.2023.3235382.
Mitropoulou, P. Kokkinos, P. Soumplis, and E. Varvarigos, “Anomaly Detection in Cloud Computing using Knowledge Graph Embedding and Machine Learning Mechanisms,” J. Grid Comput., vol. 22, no. 1, p. 6, Mar. 2024, doi: 10.1007/s10723-023-09727-1.
V. Varma, “Secure Cloud Computing with Machine Learning and Data Analytics for Business Optimization,” J. Eng. Technol. Adv., vol. 4, no. 3, pp. 181–188, 2024, doi: 10.56472/25832646/JETA-V4I3P119.
Tanwar and D. P. K. K V, “Review on Machine Learning for Resource Usage Cost Optimization in Cloud Computing,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 11, no. 5, pp. 468–472, May 2023, doi: 10.22214/ijraset.2023.51489.
S. S. Sahoo, C. Thomas, A. Sheth, C. Henson, and W. S. York, “GLYDE-an expressive XML standard for the representation of glycan structure.,” Carbohydr. Res., vol. 340, no. 18, pp. 2802–7, Dec. 2005, doi: 10.1016/j.carres.2005.09.019.
Mohammed Lateef et al., “Intelligent Cloud Resource Usage Potentially to Improve Task Scheduling by the use of Artificial Intelligence,” F1000Research, vol. 15, p. 366, Sep. 2026, doi: 10.12688/f1000research.177203.3.
R. Aleem, S. Malik, A. Kashif, and U. B. Tayab, “Cloud computing resources utilization forecasting using machine learning,” Eng. Res. Express, vol. 8, no. 7, p. 075217, Apr. 2026, doi: 10.1088/2631-8695/ae556f.
Javeed, A. Borg, H. Grahn, L. Lundberg, D. Patel, and S. Shirinbab, “A Two-Stage Forecasting System for CPU Workload Prediction in Private Clouds,” Sep. 2026. doi: 10.48550/arXiv.2609.03457.
Han, “Optimization of Cloud Computing Resource Scheduling using Firefly Swarm Optimization Intelligence Algorithm,” in 2025 3rd International Conference on Data Science and Information System (ICDSIS), IEEE, May 2025, pp. 1–6. doi: 10.1109/ICDSIS65355.2025.11070853.
R. Doukha and A. Ez-zahout, “Enhanced Virtual Machine Resource Optimization in Cloud Computing Using Real-Time Monitoring and Predictive Modeling,” Int. J. Adv. Comput. Sci. Appl., vol. 16, no. 2, pp. 658–664, 2025, doi: 10.14569/IJACSA.2025.0160267.
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.
Dixit, Himani, P. Bhardwaj, and R. Sachdeva, “Cloud-Driven AI: Enhancing Machine Learning Performance Through Distributed Computing,” in 2024 IEEE 4th International Conference on ICT in Business Industry & Government (ICTBIG), IEEE, Dec. 2024, pp. 1–5. doi: 10.1109/ICTBIG64922.2024.10911346.
P. Ramya, V. Ramya, and M. Babu Rao, “An Efficient E-Waste Management System through Energy-Aware Routing and Hybrid Optimization Deep Learning Routing on an IoT-Cloud Platform,” in 2023 International Conference on Inventive Computation Technologies (ICICT), IEEE, Apr. 2023, pp. 102–107. doi: 10.1109/ICICT57646.2023.10134402.
Kumar, R. Goomer, and A. K. Singh, “Long Short Term Memory Recurrent Neural Network (LSTM-RNN) Based Workload Forecasting Model For Cloud Datacenters,” Procedia Comput. Sci., vol. 125, pp. 676–682, 2018, doi: 10.1016/j.procs.2017.12.087.
V. Verma, “Improving Product Recommendations in Retail with Hybrid Collaborative Filtering and LSTM,” Int. J. Eng. Sci. Math., vol. 10, no. 8, August, pp. 113–128, 2021.
Ullah, “Ai-Driven Optimization Of Resource Allocation And Cost Efficiency In Cloud Computing Environments,” 2025.
H. Zhang, J. Li, and H. Yang, “Cloud computing load prediction method based on CNN-BiLSTM model under low-carbon background,” Sci. Rep., vol. 14, no. 1, p. 18004, Aug. 2024, doi: 10.1038/s41598-024-68339-1.
H. Gandhi and A. Jain, “Cloud Cost Optimization Strategies Using Machine Learning Algorithms,” Int. J. Innov. Sci. Res. Technol., vol. 9, no. 11, pp. 1004–1007, 2025, doi: 10.5281/zenodo.14831440.

Most read articles by the same author(s)

1 2 3 4 5 6 7 8 > >> 

Similar Articles

21-30 of 65

You may also start an advanced similarity search for this article.