Cognitive Cloud Operations Management using AI-Driven Automation for Resilient Enterprise Infrastructure
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806047Keywords:
cognitive cloud operations, AI-driven automation, enterprise infrastructure, cloud computing, AIOps, predictive analytics, intelligent automation, infrastructure resilience, anomaly detection, automated remediation, cloud management, artificial intelligenceAbstract
Cognitive cloud operations management represents an emerging approach to enterprise infrastructure management in which artificial intelligence, machine learning, automation, and contextual analytics are integrated to improve system reliability, operational efficiency, and resilience. Modern enterprise infrastructures generate large volumes of telemetry from applications, virtual machines, containers, networks, databases, and cloud services, making manual monitoring and incident management increasingly difficult. AI-driven automation can transform this operational model by continuously analyzing infrastructure data, identifying anomalies, predicting potential failures, correlating incidents, and initiating appropriate remediation actions. This study examines the application of cognitive cloud operations management to resilient enterprise infrastructure, focusing on automated observability, predictive analytics, intelligent incident management, automated root-cause analysis, and adaptive resource optimization. The research also considers challenges associated with explainability, data quality, model drift, automation errors, security, interoperability, and human oversight. A conceptual research methodology is proposed using literature analysis, architectural modeling, scenario-based experimentation, and quantitative evaluation of operational performance. Key evaluation dimensions include incident detection time, mean time to resolution, service availability, prediction accuracy, resource utilization, false-alert rates, and automation effectiveness. The study conceptualizes cognitive cloud operations as a continuous feedback loop in which infrastructure telemetry is transformed into operational knowledge, intelligent decisions, automated actions, and subsequent learning. The proposed approach aims to provide a foundation for resilient, self-adaptive, and intelligent enterprise cloud operations
References
1. Devineni, A., Pochincharla, S., & Muppalla, L. K. (2022). A telemetry-fusion framework for proactive incident detection in multi-tenant financial cloud platforms. International Journal of Advances in Signal and Image Sciences, 13–18.
2. Wirtz, B. W., Weyerer, J. C., & Kehl, I. (2022). Governance of artificial intelligence: A risk and guideline-based integrative framework. Government Information Quarterly, 39(4), 101685. https://doi.org/10.1016/j.giq.2022.101685
3. Pothuri, M. K. (2025). AI-powered governance with modern data engineering for regulatory excellence in healthcare and fintech. International Journal for Multidisciplinary Research, 7(4). https://doi.org/10.36948/ijfmr.2025.v07i04.52676
4. Bitragunta, S. L. V. (2024). Intelligent electric vehicle charging hubs AI-based demand forecasting and smart energy management. International Journal of Emerging Trends in Engineering and Management Research, 9(2), 15383–15393.
5. Neela, S. (2023). Performance Benchmarking Methodologies for Real-Time Integration Middleware Platforms. American International Journal of Computer Science and Technology, 5(5), 39-46.
6. Bellundagi, M. (2022). Performance Optimization Techniques for Enterprise Java Applications Using Middleware and Messaging Systems. International Journal of Computer Technology and Electronics Communication, 5(3), 5158-5168.
7. Sugumar, R. (2022). Federated Learning and Distributed AI Architectures for Cloud-Based Cyber Healthcare Data Collaboration Systems. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9232.
8. Chinnam, N. B. (2023). Modernizing retail fulfillment operations through automated load execution and microservices. International Journal of Computer Technology and Electronics Communication, 6(4), 7367–7371.
9. Yadavalli, V. R. (2022). Continuous trust scoring for cross-layer access governance in S/4HANA, Fiori, and SAP BTP. International Journal of Engineering & Extended Technologies Research, 4(1), 4347–4357.
10. Ahuja, D. (2025). DevOps and Ethical AI: Ensuring Responsible Deployment. Journal Of Multidisciplinary, 5(6), 1-14.
11. Srikanth, V., Walia, R., Augustine, P. J., Simla, J., & Jegajothi, B. (2022, March). Chaotic Whale Optimization based Node Localization Protocol for Wireless Sensor Networks Enabled Indoor Communication. In 2022 International Conference on Electronics and Renewable Systems (ICEARS) (pp. 702-707). IEEE.
12. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
13. Karakondu, M., Jambagi, G., & Tatavarthi, S. (2025). Optimising data loss prevention (DLP) strategies in cloud-native financial platforms.
14. Raja, G. V. (2022). AI Enabled Cloud and Data Engineering Frameworks for Secure, Scalable, and Intelligent Cyber Physical Analytics Systems. International Journal of Research and Applied Innovations, 5(4), 7395-7409.
15. Rahul Reddy Bandhela, RamMohan Reddy Kundavaram. (2023). A Comparative Study on Neural Network Architectures for Image Recognition Applications. Journal of Computational Analysis and Applications (JoCAAA), 31(1), 1334–1342. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3566
16. Ramasamy, M. (2023). Cloud-native control plane design for modern network automation systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9082–9091.
17. 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.
18. 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.
19. Roy, S., & Sen, A. (2021, January). A self-updating K-contingency list for smart grid system. In 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0341-0346). IEEE.
20. 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.
21. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
22. Shaik, N. (2025). Artificial intelligence in healthcare economics: Transforming cost efficiency, member experience, and preventative care. International Journal of Research Publications in Engineering, Technology and Management, 8(2), 11850–11854.
23. Mudunuri, L. N. R., Maroju, P. K., & Aragani, V. M. (2025, January). Leveraging nlp-driven sentiment analysis for enhancing decision-making in supply chain management. In 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) (pp. 1-6). IEEE.
24. Soundappan, S. J. (2021). Integrated Artificial Intelligence Framework for Enterprise Cloud Modernization and Intelligent Threat Management Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(4), 5274-5284.
25. Tarigoppula, S. (2022). Zero-trust security architecture for hybrid enterprise cloud environments using AWS native services. International Research Journal of Innovative Engineering, 6(6), 11433–11444.
26. Mathew, A., & Alex, H. (2025). Federated Learning for Secure Genomic Research: Privacy-Preserving AI Solutions for Precision Medicine. Science and Technology: Developments and Applications, 9, 36-43.
27. Gopinathan, V. R. (2023). Modernizing Enterprise Applications Using Intelligent API Frameworks Machine Learning and Cloud Native Computing. International Journal of Science, Research and Technology, 6(5), 10690-10697.
28. Polamarasetty, V. K. (2022). Enterprise SAP application modernization for post-acquisition business transformation. International Journal of Science, Research and Technology (IJSRAT), 5(3), 7777–7783. https://www.ijsrat.com/index.php/ijsrat/issue/view/23
29. Punithavathi, R., Selvi, R. T., Latha, R., Kadiravan, G., Srikanth, V., & Shukla, N. K. (2022). Robust node localization with intrusion detection for wireless sensor networks. Intelligent Automation and Soft Computing, 33(1), 143-156.
30. Chinnam, N. B. (2024). Cross-network scheduling optimization for unified retail distribution networks. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8183–8188.
31. Parast, F., Sindhav, C., Nikam, S., et al. (2022). Cloud computing security: A survey of service-based models. Computers & Security, 114, 102580. https://doi.org/10.1016/j.cose.2021.102580
