Predictive Cloud Workload Optimization Through Machine Learning and Automated Resource Allocation Strategies
DOI:
https://doi.org/10.66592/nmx7ec80Keywords:
Predictive Cloud Computing, Machine Learning, Workload Optimization, Automated Resource Allocation, Cloud Resource Management, Workload Forecasting, Dynamic Scaling, Resource Utilization, Long Short-Term Memory, Intelligent Cloud Infrastructure.Abstract
The rapid adoption of cloud computing has increased the demand for efficient workload management, dynamic resource allocation, and scalable infrastructure optimization. Traditional cloud resource management techniques often rely on static provisioning and reactive monitoring, which can lead to resource underutilization, performance degradation, increased operational costs, and inefficient workload distribution. This research proposes a predictive cloud workload optimization framework that integrates machine learning algorithms with automated resource allocation strategies to improve cloud infrastructure performance. The proposed framework analyzes historical workload patterns, real-time resource utilization, application performance metrics, and changing computational demands to predict future resource requirements. Machine learning techniques, including Random Forest, Long Short-Term Memory networks, and Gradient Boosting, are considered for workload forecasting and resource demand estimation. Based on predictive outputs, an automated allocation mechanism dynamically adjusts CPU, memory, storage, and network resources to maintain service performance while minimizing infrastructure costs. The framework also incorporates workload prioritization, threshold-based monitoring, and adaptive scaling to support changing enterprise application requirements. Its evaluation considers resource utilization, prediction accuracy, response time, infrastructure cost, and workload scheduling efficiency. The proposed approach provides a foundation for intelligent, scalable, and cost-effective cloud resource management, supporting improved operational efficiency across dynamic cloud environments.References
1. Bertolini, M., Mezzogori, D., Neroni, M., & Zammori, F. (2021). Machine Learning for industrial applications: A comprehensive literature review. Expert Systems with Applications, 175, Article 114820. https://doi.org/10.1016/j.eswa.2021.114820
2. Kopparthi, G. S. (2024). AI-driven predictive analytics for clinical decision support and improved patient outcomes. International Journal of Research Publications in Engineering, Technology and Management, 7(4), 10889–10899.
3. Gurram, S. (2024). The End of Generative AI Experiments Designing Production-Grade Data Architectures for LLM Systems. International Journal of Computer Technology and Electronics Communication, 7(1), 8233-8242.
4. Ramasamy, M. (2024). Secure and extensible platform architectures for enterprise network orchestration. International Journal of Humanities and Information Technology, 6(01), 86-98.
5. Aragani, V. M., Maroju, P. K., & Mudunuri, L. N. R. (2024, December). Implementing Non-functional Production Regression Testing with Kubernetes. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 231- 244). Singapore: Springer Nature Singapore.
6. Namdeo, A. (2023). Generative synthetic data pipelines for balanced BI training. International Journal of Emerging Research in Engineering and Technology, 4(1), 228–235. https://doi. org/10.63282/3050-922X.IJERET-V4I1P124
7. Tarigoppula, S. (2023). Cloud-native Oracle database modernization using intelligent migration frameworks and automated recovery architectures. International Journal of Research and Applied Innovations, 6(6), 10159–10171.
8. Bellundagi, M. (2024). An intelligent digital transformation framework for smart enterprises using AI and cloud computing. International Journal of Humanities and Information Technology, 6(3), 178–197.
9. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
10. Gadige, C. D. (2023). Secure CRM modernization: Architectural patterns for migrating enterprise customer data from legacy CRM platforms to Salesforce. International Journal of Computer Technology and Electronics Communication, 6(3), 7013–7023.
11. Kalakoti, M. K. R. (2024). Topology-aware generative AI for fault diagnosis and recovery planning in cloud data centers. International Journal of Research and Applied Innovations, 7(1), 10186–10196.
12. Gazi, M. S., Tasnim, M., Enayet, M. A., Chowdhury, J. A., Ullah, S., Akter, A., Rahman, M. R., & Khan, M. A. N. (2024). A novel framework for memory-resident malware investigation and forensic evidence reconstruction. International Journal of Research and Applied Innovations, 7(3), 10910–10921.
13. Ponugoti, M. (2023). Bridging the digital divide: Architecture for equitable technological access. International Journal of Computer Technology and Electronics Communication, 6(3), 6991-7002.
14. Indurthy, V. S. K. (2025). ETL-Driven Data Integration for Enhanced Pharmaceutical Manufacturer Rebate Processing. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(1), 11606-11615.
15. Basavala, S. R. (2022). Securit y assessment framework for microservices-based applications using automated penetration testing. International Journal of Humanities and Information Technology, 4(1–3), 327–341.
16. Selvarajan, K. (2024). Observability-driven reliability engineering for distributed data platforms. International Journal of Computer Technology and Electronics Communication, 7(4), 9258-9268.
17. Karuturi, Y. (2025). Serverless computing framework for enterprise ERP background job processing in cloud SAP environments. International Journal of Humanities and Information Technology, 7(1), 100–110.
18. Bheemisetty, N. (2024). AI-powered recommendation systems best practices and real-world applications. International Journal of Research Publications in Engineering, Technology and Management, 7(6), 11777–11785.
19. Kommineni, R. (2022). Monitoring as code for self-hosted Elasticsearch: Independent, delta-based lifecycle management of alerting watchers and dashboards across multi-cloud environments. International Journal of Science, Research and Technology (IJSRAT), 5(2), 7437–7446.
20. Yadavalli, V. R. (2024). Hypercare signal fusion for early instability detection in SAP S/4HANA and EWM go-lives. International Journal of Research Publications in Engineering, Technology and Management, 7(5), 11384–11898.
21. Randhi, K. S. (2024). A scalable integration framework for automated utility revenue leakage detection and remediation. International Journal of Research and Applied Innovations, 7(2), 10536–10555.
22. Neela, S. (2022). Toward Intelligent Enterprise Integration: Cloud-Native Middleware Design Patterns and Adaptive Stream Orchestration Architectures for Autonomous Real-Time Decisioning. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(2), 154-164.
23. Ambati, K. C. (2024). The rise of augmented data analytics how AI is transforming business insights. International Journal of Computer Technology and Electronics Communication, 7(6), 10068–10076.
24. Reddy, K. V. (2021). UVM methodology for industry-specific applications using AI in hardware verification. International Journal of Engineering & Extended Technologies Research, 3(3), 3094–3107.
25. Shaik, N. (2024). AI-driven scheduling for multi-cloud Kubernetes: Lessons from enterprise deployment. International Journal of Research and Applied Innovations, 7(3), 10880–10891.
26. Kalabhavi, V. (2023). Automated quote generation using AI for product configuration, pricing and external order integration in SAP CPQ. International Journal of Humanities and Information Technology, 5(2), 110–124.
27. Abd-Rouf, M. S. K., Adigun, P. O., Alalade, E. O., Oyekanmi, T. T., Faniyi, A. J., Oladapo, B., Awopejo, T. E., Adegoke, O. S., Jamiu, A., Michael, O. B., Obisesan, A., Ajala, S., Adekanye, M. A., Yambali, P. M., & Abd-Rouf, A. B. (2024). From molecular profiling to predictive algorithms: A conceptual machine-learning framework for mechanism-informed therapy selection in multidrug-resistant cancer. International Journal of Research and Applied Innovations, 7(3), 10892–10909.
28. Bitragunta, S. L. V. (2024). AI-driven deep learning-based animal intrusion detection and early warning system for railroad tracks. International Journal of Humanities and Information Technology, 6(1), 156–167.
29. Maniah, Abdurachman, E., Gaol, F. L., & Soewito, B. (2019). Survey on threats and risks in the cloud computing environment. Procedia Computer Science,
