Intelligent Multi-Agent AI Architecture for Scalable Cloud Infrastructure and Enterprise Optimization
Abstract
: The rapid expansion of enterprise digital services has increased the demand for scalable, intelligent, and self-adaptive cloud infrastructures capable of managing complex workloads and optimizing enterprise applications. Traditional centralized management approaches are often insufficient for handling dynamic cloud environments where workloads fluctuate, resources scale automatically, and application dependencies evolve continuously. Artificial Intelligence (AI), particularly multi-agent systems, has emerged as a promising solution for building autonomous and intelligent cloud infrastructures. Multi-agent AI architectures consist of distributed intelligent agents that collaborate, coordinate, and make decentralized decisions to optimize system performance and resource utilization.
This research proposes an Intelligent Multi-Agent Artificial Intelligence Architecture designed to enhance scalability, efficiency, and performance optimization in cloud-based enterprise systems. The proposed framework integrates autonomous AI agents responsible for workload management, resource allocation, system monitoring, and application optimization across distributed cloud environments. These agents use machine learning algorithms and collaborative decision-making techniques to dynamically adapt to changing system conditions. The study investigates how multi-agent systems can improve infrastructure management, reduce operational overhead, and enhance enterprise application performance. The research methodology involves architectural modeling, simulation experiments, and comparative analysis of traditional cloud management frameworks. The findings indicate that multi-agent AI architectures can significantly improve scalability, system reliability, and intelligent automation in cloud infrastructures while supporting optimized performance of enterprise applications.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 7 No. 5 (2024): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
12805-12815 |
Published |
September 6, 2024 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Aleksandr Sutyagin (%2024). Intelligent Multi-Agent AI Architecture for Scalable Cloud Infrastructure and Enterprise Optimization. International Journal of Science, Research and Technology , Vol. 7 No. 5 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 12805-12815. https://doi.org/10.15662/IJSRAT.2024.0705005 |
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2. Karnam, A. (2023). SAP Beyond Uptime: Engineering Intelligent AMS with High Availability & DR through Pacemaker Automation. International Journal of Research Publications in Engineering, Technology and Management, 6(5), 9351–9361. https://doi.org/10.15662/IJRPETM.2023.0605011
3. Kwankajornkeat, S., & Aswakul, C. (2021). Differential private motion sensor and wasted energy in building energy management system. IEEE Access, 10, 486-501.
4. Barve, P. S., Vigenesh, M., Deshpande, V., Wanjari, M. B., & Patil, S. (2023, December). A Non-Linear Dimensionality Reduction Approach for Unmixing Hyper Spectral Data. In 2023 International Conference on Power Energy, Environment & Intelligent Control (PEEIC) (pp. 1718-1724). IEEE.
5. Ambalakannu, M. (2024). Driving Operational Efficiency and Clinical Insights via Unified Care Management. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10693-10702.
6. Meka, S. (2023). Building Digital Banking Foundations: Delivering End-to-End FinTech Solutions with Enterprise-Grade Reliability. International Journal of Research and Applied Innovations, 6(2), 8582-8592.
7. Konda, S. K. (2024). Carbon-native DCIM architectures for AI data centers: Autonomous infrastructure control via smart grid intelligence. World Journal of Advanced Research and Reviews, 21(1), 3008–3318. https://doi.org/10.30574/wjarr.2024.21.1.0095
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10. Potel, R. (2023). Artificial Intelligence in Human Capital Management: A Comprehensive Framework for Intelligent Workforce Systems. International Journal of AI, BigData, Computational and Management Studies, 4(4), 147-174.
11. Poornima, G., & Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.
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14. Indurthy, V. S. K. (2024). Streamlining ROP Metrics and Reporting through Cloud Migration and Automation. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10703-10712.
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16. Inampudi, R. K., Pichaimani, T., & Surampudi, Y. (2022). AI-enhanced fraud detection in real-time payment systems: leveraging machine learning and anomaly detection to secure digital transactions. Australian Journal of Machine Learning Research & Applications, 2(1), 483-523.
17. Selvi, C. P., Muneeshwari, P., Selvasheela, K., & Prasanna, D. (2023). Twitter Media Sentiment Analysis to Convert Non-Informative to Informative Using QER. Intelligent Automation & Soft Computing, 35(3).
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22. 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.
23. Bheemisetty, N. (2024). From Fragmentation to Agility: Nautilus Architecture for Risk Management Modernization. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10673-10682.
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25. Dave, B. L. (2023). Enhancing Vendor Collaboration via an Online Automated Application Platform. International Journal of Humanities and Information Technology, 5(02), 44-52.
26. Gopinathan, V. R. (2024). Meta-Learning–Driven Intrusion Detection for Zero-Day Attack Adaptation in Cloud-Native Networks. International Journal of Humanities and Information Technology, 6(01), 19-35.
27. Dama, H. B. (2023). Designing Highly Available Multi-Cloud Database Architectures for Global Financial Services. International Journal of Research and Applied Innovations, 6(1), 8329-8336.
28. Karvannan, R. (2023). Real-Time Prescription Management System Intake & Billing System. International Journal of Humanities and Information Technology, 5(02), 34-43.
29. Vootla, A. (2023). Continuous Accessibility Assurance through DevSecOps-Integrated Testing Pipelines. International Journal of Research and Applied Innovations, 6(6), 9975-9984.
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