AI Enabled Operational Intelligence Platforms for Large Scale Distributed Computing Environments
Abstract
Artificial Intelligence (AI) enabled operational intelligence platforms are transforming the management and monitoring of large-scale distributed computing environments. These platforms integrate machine learning, big data analytics, automation, and real-time monitoring to improve operational efficiency, reliability, and scalability across cloud infrastructures, data centers, edge computing systems, and enterprise networks. Traditional monitoring systems often struggle to process the enormous volume, velocity, and variety of operational data generated in distributed environments. AI-enabled platforms overcome these limitations by using predictive analytics, anomaly detection, intelligent automation, and self-healing mechanisms to optimize system performance and minimize downtime.
This study explores the architecture, functionalities, and significance of AI-enabled operational intelligence platforms in modern distributed computing systems. The research highlights the role of AI in predictive maintenance, resource allocation, workload balancing, cybersecurity, and fault management. Furthermore, the study examines current industry practices, emerging technologies, and research trends associated with operational intelligence solutions. The paper also discusses the advantages and limitations of implementing AI-driven operational platforms, including issues related to data privacy, complexity, integration, and computational overhead. The findings indicate that AI-enabled operational intelligence platforms significantly enhance decision-making, operational visibility, and system resilience, making them essential for future large-scale distributed computing environments and smart digital infrastructures.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 8 No. 5 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
17285-17292 |
Published |
September 16, 2025 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Kishore Nayak (2025). AI Enabled Operational Intelligence Platforms for Large Scale Distributed Computing Environments. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 8 No. 5 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 17285-17292. https://doi.org/10.15662/IJAESIT.2025.0805011 |
References
2. Panyala, V. R. (2024). Designing self-healing cloud architectures for mission-critical distributed systems. International Journal of Science, Research and Technology, 7(2), 11717–11721.
3. Shewale, V. (2025). Demystifying the MITRE ATT&CK Framework: A Practical Guide to Threat Modeling. Journal of Computer Science and Technology Studies, 7(3), 182-186.
4. Rongali, L. P. (2025). Compliance and Governance: Address the Role of Devops in Maintaining Compliance and Ensuring Governance throughout the Development Lifecycle. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5229546
5. Bheemisetty, N. (2024). AI-Powered Recommendation Systems Best Practices and Real-World Applications. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13926.
6. Kassetty, N., Alang, K., Paruchuru, V., Sharma, S., Goel, P., & Kumar, S. (2025, May). Cloud Security Management: Advanced AI Techniques for Anomaly Detection and Response Automation. In 2025 International Conference on Networks and Cryptology (NETCRYPT) (pp. 1620-1624). IEEE.
7. Pasumarthi, H. (2023). A Deep Dive into Enterprise B2B Integrations: Designing High-Availability File and API Workflows with IBM Datapower and Autosys. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(2), 8363-8370.
8. Mulla, F. A. (2024). Modern Mobile Testing Tools: A Comprehensive Guide to Quality Assurance and Automation. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 10-32628.
9. Macha, Y., & Pulichikkunnu, S. K. (2023). An Explainable AI System for Fraud Identification in Insurance Claims via Machine-Learning Methods. Int. J. Adv. Res. Sci. Commun. Technol, 3(3), 1391-1400.
10. Bellundagi, M. (2023). Design of an Intelligent Clinical Decision Support System Using Machine Learning Techniques. International Journal of Research and Applied Innovations, 6(6), 10075-10081.
11. Adepu, G. (2024). AI-driven healthcare payment systems using intelligent claims validation and fraud detection mechanisms. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 259–277.
12. Adepu, R. (2021). Modernizing legacy data centers through virtualization and software-defined infrastructure. International Journal of Research and Applied Innovations (IJRAI), 4(4), 17–36.
13. Mallireddy, S. (2024). Transforming financial services business through servicenow. International Journal of Computer Technology and Electronics Communication, 7(3), 1-6.
14. Ambalakannu, M. (2025). Accelerating Claims Processing with Observability and Automated Dashboards. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(3), 12179-12186.
15. Sarabu, V. B. (2022). Hybrid on-premise to cloud data migration: A controlled one-way synchronization framework for enterprise-scale modernization. International Journal of Science, Research and Technology (IJSRAT), 5(5), 19–33.
16. Hossain, M. S., Hossain, M. S., Ali, M., & Rahman, M. W. (2025). Data-Driven Strategies for Predicting and Enhancing Rural Business Growth in the United States. Data-Driven Strategies for Predicting and Enhancing Rural Business Growth in the United States, 1(7), 121-146.
17. Nijaguna, G.S.; Manjunath, D.R.; Abouhawwash, M.; Askar, S.S.; Basha, D.K.; Sengupta, J. Deep Learning-Based Improved WCM Technique for Soil Moisture Retrieval with Satellite Images. Remote Sens. 2023, 15, 2005.
18. Vayyasi, N. K. (2023). Designing a multi-domain predictive framework using Java and generative AI for financial, retail, and industrial use cases. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(6), 8060–8069.
19. Anbazhagan, K. (2025). AI Driven Zero Trust Security Model for Enterprise Data Protection and Intelligent Infrastructure Management. International Journal of Technology, Management and Humanities, 11(03), 101-107.
20. Appani, C. (2024). Explainable AI for fraud detection in financial transactions. Journal of Information Systems Engineering and Management, 9(3). https://jisem-journal.com/download/32_Explainable_AI_for_Fraud_Detection.pdf
21. Archana, R., & Anand, L. (2025). Residual u-net with Self-Attention based deep convolutional adaptive capsule network for liver cancer segmentation and classification. Biomedical Signal Processing and Control, 105, 107665.
22. Soundappan, S. J. (2024). AI-Driven Customer Intelligence in Enterprise Lakehouse Systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(5), 14905.
23. Gopinathan, V. R. (2024). Real-Time Financial Risk Intelligence Using Secure-by-Design AI in SAP-Enabled Cloud Digital Banking. International Journal of Computer Technology and Electronics Communication, 7(6), 9837-9845.
24. Praveena, M., Saravanan, M., & Yerra, R. (2025, June). PSO MPPT based Control Framework for Photovoltaic Systems to enhance Power Quality. In 2025 5th International Conference on Intelligent Technologies (CONIT) (pp. 1-5). IEEE.
25. Murugeshwari, B., Sabatini, S. A., Jose, L., & Padmapriya, S. (2023). Effective data aggregation in WSN for enhanced security and data privacy. arXiv preprint arXiv:2304.14654.
26. Anbazhagan, K. (2024). Trustworthy and Adaptive AI Systems for Enterprise Analytics Cybersecurity and Decision Optimization Using API-First and Cloud-Native Architectures. International Journal of Technology, Management and Humanities, 10(03), 65-74.
27. Vimal, V. R., Jayalakshmi, D., Narayanan, L. K., Hemavathi, R., & Loganayagi, S. (2024, November). 5G-Enabled Remote Healthcare Monitoring for Improved Patient Care. In 2024 International Conference on Recent Advances in Science and Engineering Technology (ICRASET) (pp. 1-5). IEEE.
28. Udayakumar, S. Y. P. D. (2023). User Activity Analysis Via Network Traffic Using DNN and Optimized Federated Learning based Privacy Preserving Method in Mobile Wireless Networks.
29. Raja, G. V. (2023). Modernizing Enterprise Systems using AI with Machine Learning and Cloud Computing for Intelligent Systems. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11713.
30. Mathew, A. (2024). Cloud data sovereignty governance and risk implications of cross-border cloud storage. Information Systems Audit and Control Association.
31. Mulajkar, R. M., & Gohokar, V. V. (2017, February). Development of Semi-Automatic Methodology for Extraction of Depth for 2D-to-3D Conversion. In Proceedings of the 9th International Conference on Machine Learning and Computing (pp. 373-378).
32. Reddy, B. V. S., & Sugumar, R. (2025, April). Improving dice-coefficient during COVID 19 lesion extraction in lung CT slice with watershed segmentation compared to active contour. In AIP Conference Proceedings (Vol. 3270, No. 1, p. 020094). AIP Publishing LLC.
33. Prasad, P. K. (2024). Establishing AI governance frameworks within CloudOps to accelerate safe, compliant AI adoption at scale. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 14026–14030.
34. Rao, G. R. (2023). Hidden Trade-Offs in Modern Frontend Architecture. International Journal of Computer Technology and Electronics Communication, 6(5), 7615-7625.
35. 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.
36. Ganesan M. (2025). Artificial intelligence AI driven proactive customer service excellence platform in e commerce industry. International Journal of Computer Technology and Electronics Communication 8(1) 10089–10099.
37. Lanka, S. (2025). AI driven healthcare at scale: Personalization and predictive tools in the CVS Health mobile app. International Journal of Research and Applied Innovations, 8(3), 12280-12297.
38. Parupalli, A. (2022). KPI-Driven Business Intelligence: A Review of Frameworks and Visualization Tools. Asian Journal of Computer Science Engineering, 7(4), 4.