Articles Open Access
Predictive AI-Based Cloud Observability Frameworks for Enterprise Infrastructure Resilience and Anomaly Detection
Universiti Kebangsaan Malaysia, Selangor, Malaysia
Abstract
Enterprise cloud infrastructures have become increasingly distributed, dynamic, and complex, creating significant challenges for maintaining service reliability, performance, and operational continuity. Conventional observability approaches primarily depend on retrospective analysis of logs, metrics, traces, and alerts, which can limit the ability of organizations to identify emerging failures before they affect business services. Predictive artificial intelligence (AI)-based cloud observability provides an alternative approach by combining machine learning, statistical forecasting, anomaly detection, and automated correlation techniques to anticipate infrastructure degradation and identify abnormal behavior. This study examines a predictive AI-based cloud observability framework designed to improve enterprise infrastructure resilience and anomaly detection. The proposed framework integrates heterogeneous telemetry from cloud resources, applications, networks, databases, and security systems into a unified analytical pipeline. AI models are applied to establish dynamic behavioral baselines, detect deviations, forecast resource exhaustion, and identify potential service failures. The methodology further incorporates contextual correlation and risk-based prioritization to reduce alert noise and support proactive incident management. Evaluation is proposed using metrics including anomaly-detection precision, recall, F1-score, false-positive rate, forecasting accuracy, mean time to detection, and mean time to recovery. The framework aims to demonstrate how predictive observability can transform cloud operations from reactive monitoring toward proactive resilience management
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 10 |
| Number | 4 |
| Pages | 19348-19357 |
| Published | 2026-08-26 |
| Issue | Vol. 10 No. 4 (2026): International Research Journal of Innovative Engineering (IRJIE) |
References
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31. 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.
32. Polamarasetty, V. K. (2025). Scalable Workday benefits integration frameworks for enterprise HR technology. International Journal of Computer Technology and Electronics Communication, 8(2), 10483–10489.
33. 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.
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39. Shaik, N. (2025). Dynamic healthcare intelligence: Integrating AI predictive analytics with Kubernetes scaling for enhanced patient outcomes. International Journal of Engineering & Extended Technologies Research, 7(2), 9715–9720
2. Kalakoti, M. K. R. (2025). Provider-agnostic infrastructure as code: A modular framework for secure multi-tenant cloud automation. Journal of international crisis and risk communication research, 188-197.
3. Matrouk, K., V, S., Kumar, S., Bhadla, M. K., Sabirov, M., & Saadh, M. J. (2023). Deep Learning–based Dynamic User Alignment in Social Networks. ACM Journal of Data and Information Quality, 15(3), 1-26.
4. Kargeti, H. (2026, February). Automating Enterprise Vulnerability Exposure: SCAP-Based Asset-CVE Linkage with Social Signal Triage for Cyber Defense. In 2026 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) (pp. 1-6). IEEE.
5. Ahuja, D. (2023). Multi-Cloud Management Strategies: Tools, Challenges, and Implementation Methods. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, 9(10), 493-506.
6. Maroju, P. K., & Mudunuri, L. N. R. (2025, October). Generative AI as a Cyber Weapon: Synthetic Identity Fraud and Phishing at Scale. In International Conference on Artificial Intelligence and Networking (pp. 573-585). Cham: Springer Nature Switzerland.
7. Yadavalli, V. R. (2024). Hypercare signal fusion for early instability detection in SAP S/4HANA and EWM go-lives. International Research Journal of Innovative Engineering, 8(5), 15423–15437.
8. 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.
9. Reddy, K. V. (2021). UVM methodology for industry-specific applications using AI in hardware verification. International Journal of Science, Research and Technology, 4(3), 5731–5744.
10. Devineni, A., Prabagaran, A., Mali, R. K., Mishra, M. K., Nekkalapudi, K., & Velu, R. (2026, June). Intelligent Incident Prediction in Banking Cloud Operations: A Machine Learning Framework for Proactive SRE Response. In 2026 5th International Conference on Computer Networks, Big Data and IoT (ICCBI) (pp. 1072-1078). IEEE.
11. Selvarajan, K. (2025). Next-generation AI-driven big data platforms. International Journal of Research Publications in Engineering, Technology and Management, 8(2), 11831–11836.
12. Neela, S. (2022). Semantic Interoperability in Real-Time Enterprise Integration Using Middleware Abstractions. American International Journal of Computer Science and Technology, 4(6), 56-66.
13. Mohile, A. (2026, April). Zero Trust Architectures for Securing Foundation Model Lifecycles in Enterprise AI Systems. In 2026 3rd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE) (pp. 1-6). IEEE.
14. Selvarajan, K. (2025). AI-powered big data platforms for enterprise analytics. International Journal of Computer Technology and Electronics Communication, 8(2), 10490–10496.
15. 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.
16. Goyal, K. K., Hebbar, K. S., & Mali, R. K. (2026, March). AI Modernization Enabled by Cloud-Native and Edge-Intelligent Architectures. In International Conference on Information Technology and Artificial Intelligence (pp. 102-114). Cham: Springer Nature Switzerland.
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. Chundi, V. R. K., Agarwal, V., & Arya, P. (2025, November). Green AI for Sustainable Supply Chains: Challenges in Emerging Economies. In 2025 International Conference on Computational Engineering, Sensing Technology and Management (ICCETM) (pp. 1-5). IEEE.
19. 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.
20. Tasnim, M., Khan, M. A. N., Himeluzzaman, M., Alam, A., Gazi, M. S., Akter, K. S., & Jakir, T. (2026). AI-Driven Healthcare Cyber Threat Intelligence Using Explainable Machine Learning and Federated Learning. Vascular and Endovascular Review, 9(1), 423-435.
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. Shrestha, G., Ivanov, T., Vemireddy, K., Pyayt, A., & Gubanov, M. (2026, July). Hemolix. TabGen: Optimized Table Generation from Documents. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) (pp. 1055-1066).
23. Ramasamy, M. (2025). AI-driven automation and platform orchestration in network engineering and cloud infrastructure. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(3), 12170–12174.
24. 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.
25. Hossain, I., Hossain, M. S., Rasul, I., Prince, N. U., Datta, A., & Akand, A. R. (2026, June). Machine Learning-Based Evaluation of Password Security and User Awareness for Cyber Risk Prevention. In 2026 Third International Conference on Innovations in Cybersecurity and Data Science (ICICDS) (pp. 499-504). IEEE.
26. Anand, L. (2023). Machine Learning Enabled Enterprise Integration through Intelligent API Governance Secure Cloud Infrastructure and Automated Operations. International Journal of Research and Applied Innovations, 6(3), 5972-5979.
27. Pothuri, M. K. (2026). Predicting Very High-Cost Claimants Using Symmetry ETG/PEG Feature Engineering Combined with Advanced Machine Learning. International Journal of AI, BigData, Computational and Management Studies, 352-357.
28. Tarigoppula, S. (2021). Policy-driven autonomous infrastructure provisioning: A framework for enterprise cloud governance using infrastructure as code. International Journal of Emerging Trends in Engineering and Management Research, 6(5), 10361–10369.
29. Chidambaranathan, S., Mudunuri, L. N. R., Banu, S. S., Anitha, V., Praveen, R., & Golla, S. (2024, November). Effects of Water Quality Deterioration and the Application of Artificial Intelligence and Blockchain Technology. In 2024 International Conference on Advances in Computing, Communication and Materials (ICACCM) (pp. 1-6). IEEE.
30. Gopinathan, V. R. (2025). Privacy-Preserving Federated AI for Secure Digital Enterprises across Hybrid Cloud Computing Ecosystems and Distributed Intelligence. International Journal of Computer Technology and Electronics Communication, 8(6), 11999-12007.
31. 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.
32. Polamarasetty, V. K. (2025). Scalable Workday benefits integration frameworks for enterprise HR technology. International Journal of Computer Technology and Electronics Communication, 8(2), 10483–10489.
33. 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.
34. 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.
35. Khare, A., & Goyal, A. K. (2026, May). Integrating Artificial Intelligence and Big Data in Supply Chain Management for Increased Efficiency and Sustainability. In Proceedings of Fifth International Conference on Computing and Communication Networks: ICCCN 2025, Volume 4 (Vol. 4, p. 447). Springer Nature.
36. 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.
37. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
38. Ambalakannu, M. (2025, November). Next-Gen Healthcare Claims Optimization: DL-Based ResAttBiL Integrated with CDC, Modular Design, and Data Observability. In 2025 5th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) (pp. 980-985). IEEE.
39. Shaik, N. (2025). Dynamic healthcare intelligence: Integrating AI predictive analytics with Kubernetes scaling for enhanced patient outcomes. International Journal of Engineering & Extended Technologies Research, 7(2), 9715–9720
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