Articles Open Access
Intelligent Cloud Resource Orchestration Using Multi-Agent Systems and Reinforcement Learning for Enterprise Applications
School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
Abstract
Intelligent cloud resource orchestration has become an essential component of modern enterprise computing due to the increasing complexity of distributed applications, dynamic workloads, and growing demands for efficient resource utilization. Traditional cloud resource management approaches often depend on predefined policies and centralized decision-making mechanisms, which may struggle to respond effectively to rapidly changing application requirements. This study proposes an intelligent cloud resource orchestration framework integrating Multi-Agent Systems (MAS) and Reinforcement Learning (RL) to improve resource allocation, workload scheduling, scalability, and operational efficiency in enterprise applications. The proposed framework employs multiple autonomous agents responsible for monitoring cloud infrastructure, predicting workload variations, allocating computational resources, and optimizing service performance. Reinforcement learning enables these agents to learn optimal orchestration strategies through continuous interaction with the cloud environment, considering parameters such as CPU utilization, memory consumption, network latency, execution time, energy consumption, and operational cost. A cooperative multi-agent learning mechanism is incorporated to support coordination among distributed agents while maintaining service-level agreement requirements. The methodology emphasizes simulation-based evaluation and comparative analysis against conventional resource orchestration approaches. The expected outcomes include improved resource utilization, reduced operational expenses, enhanced application responsiveness, and increased infrastructure adaptability. The proposed approach provides an intelligent and scalable foundation for enterprise cloud environments requiring autonomous, cost-effective, and performance-oriented resource management
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 9 |
| Number | 5 |
| Pages | 17616-17624 |
| Published | 2025-10-18 |
| Issue | Vol. 9 No. 5 (2025): International Research Journal of Innovative Engineering (IRJIE) |
References
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26. Mudunuri, P. R. (2024). Operational transparency as a compliance mechanism in federal DevOps ecosystems. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(3), 8131-8142.
27. Vemireddy, S. (2023). Engineering regulatory-aware data processing systems for tax advisory platforms. International Journal of Science, Research and Technology, 6(1), 9243–9249.
28. Ponugoti, M. (2025). The full-stack engineer's role in reengineering enterprise systems for compliance and innovation. Journal of Multidisciplinary, 5(7), 948-956.
29. Jinna, K. R., & Bandari, U. (2025). Agentic pipeline operations: LLM agents for CI/CD failure triage and remediation under human-in-the-loop guardrails. International Journal of Research and Applied Innovations, 8(3), 13134-13144.
30. Randhi, K. S. (2021). Design and implementation of an API-driven hybrid integration framework for real-time utility inventory systems. International Journal of Emerging Trends in Engineering and Management Research, 6(2), 9281–9294.
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32. Kalakoti, M. K. R. (2024). Topology-aware generative AI for fault diagnosis and recovery planning in cloud data centers. International Journal of Science, Research and Technology, 7(1), 11381–11391.
2. Song, H., Triguero, I., & Özcan, E. (2019). A review on the self and dual interactions between machine learning and optimisation. Progress in Artificial Intelligence, 8, 143–165. https://doi.org/10.1007/s13748-019-00185-z
3. Sugumar, R. (2022). Explainable Deep Learning Framework for Financial Fraud Detection and Intelligent Risk Analysis. International Journal of Emerging Trends in Engineering and Management Research, 7(5), 12528.
4. Gadige, C. D. (2024). Event-driven CRM architecture for real-time order fulfillment and product provisioning. International Journal of Engineering & Extended Technologies Research, 6(2), 7968–7978.
5. Sayyaparaju, K. K. (2025, November). Q-Trans: Quantization-Aware Fine-Tuning of Transformer Models for Energy-Efficient IoT and Edge Deployment. In 2025 International Conference on Intelligent Computing, Information and Control Systems (ICOIICS) (pp. 494-499). IEEE.
6. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285-2287.
7. Kommineni, R. (2025). Defense in depth for executing model-generated code: Kernel-isolated sandboxes, layered network policy, and credential-free data access on multi-tenant Kubernetes. International Journal of Research and Applied Innovations, 8(2), 11295–11302.
8. Selvarajan, K. (2023). Optimizing large-scale data lakehouse architectures for performance and cost efficiency. International Journal of Research and Applied Innovations, 6(6), 10093–10101.
9. Kannan, K. S., Sunitha, G., Deepa, S. N., Babu, D. V., & Avanija, J. (2022). A multi-objective load balancing and power minimization in cloud using bio-inspired algorithms. Computers and Electrical Engineering, 102, 108225.
10. Rengarajan, A. (2022). Developing AI Powered Enterprise Integration Platforms Through Cloud Native Observability and Automated Governance. International Journal of Science, Research and Technology, 5(6), 8970-8977.
11. Sahu, S. K. (2022). Automating insurance benefit interpretation: A domain-intelligence pipeline for document extraction, benefit classification, and plan recommendation. International Journal of Research and Applied Innovations, 5(6), 8252–8259.
12. 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.
13. Basavala, S. R. (2022). Security-by-design: Threat modeling and requirements for enterprise applications. International Journal of Science, Research and Technology, 5(1), 7168–7178.
14. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
15. Natta, P. K. (2022). Computer-vision-assisted retail inventory automation: Integrating autonomous scan data with worklist completion systems. International Journal of Science, Research and Technology, 5(6), 8957–8969.
16. 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.
17. Chinthamani, R. (2025). Reliable Credential Rotation and Runtime Secret Convergence in Large-Scale Cloud-Native Systems. International Journal of Future Innovative Science and Technology (IJFIST), 8(2), 14617.
18. Gopinathan, V. R. (2022). Advanced Predictive Decision Intelligence Using Machine Learning for Strategic Enterprise Management Platforms. International Journal of Research and Applied Innovations, 5(6), 8162-8169.
19. Mysula, S. (2022). Architectural modernization of legacy enterprise integration systems for scalable and reliable business operations. International Journal of Research and Applied Innovations, 5(2), 6796–6801.
20. 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.
21. Jinna, K. R., & Bandari, U. (2023). Progressive delivery for machine-learning inference services: A comparative study of blue-green, canary and shadow strategies with model-aware gates on enterprise container platforms. International Journal of Research Publications in Engineering, Technology and Management, 6(4), 9092–9101.
22. Ravichandran, S., & Kandasamy, V. (2025). Optimized Attention Augmented Residual Convolutional Neural Network with Fa-Resnet for Fabric Defect Detection. Journal of Control Engineering and Applied Informatics, 27(4), 3-15.
23. Ramasamy, M. (2022). Architecting scalable intent-based networking platforms for enterprise automation. International Journal of Emerging Trends in Engineering and Management Research (IJETEMR), 7(3), 11812–11823.
24. Raja, G. V. (2022). AI-Driven Enterprise Architecture Integrating Zero-Trust Security for Secure Scalable and Compliant Cloud-Native Platforms. International Journal of Emerging Trends in Engineering and Management Research, 7(4), 12178.
25. Karrothu, A. (2024). Automated risk-based security controls for vulnerability detection and prevention across cloud-native software development lifecycles. International Research Journal of Innovative Engineering, 8(4), 14994–15009.
26. Mudunuri, P. R. (2024). Operational transparency as a compliance mechanism in federal DevOps ecosystems. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(3), 8131-8142.
27. Vemireddy, S. (2023). Engineering regulatory-aware data processing systems for tax advisory platforms. International Journal of Science, Research and Technology, 6(1), 9243–9249.
28. Ponugoti, M. (2025). The full-stack engineer's role in reengineering enterprise systems for compliance and innovation. Journal of Multidisciplinary, 5(7), 948-956.
29. Jinna, K. R., & Bandari, U. (2025). Agentic pipeline operations: LLM agents for CI/CD failure triage and remediation under human-in-the-loop guardrails. International Journal of Research and Applied Innovations, 8(3), 13134-13144.
30. Randhi, K. S. (2021). Design and implementation of an API-driven hybrid integration framework for real-time utility inventory systems. International Journal of Emerging Trends in Engineering and Management Research, 6(2), 9281–9294.
31. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533-5537.
32. Kalakoti, M. K. R. (2024). Topology-aware generative AI for fault diagnosis and recovery planning in cloud data centers. International Journal of Science, Research and Technology, 7(1), 11381–11391.
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