A Systematic Survey of Autonomous AIOps and Generative AI in Cloud-Native Infrastructure
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I2P125Keywords:
Autonomous AIOps, Generative AI, Cloud-Native Infrastructure, Anomaly Detection, Intelligent Cloud OperationsAbstract
Cloud-native infrastructures have transformed modern computing through scalable, flexible, and resilient deployment models based on microservices, containers, orchestration, and DevOps practices. Nevertheless, increasing system complexity, distributed workloads, and dynamic operational environments create major challenges in monitoring, fault management, security, and resource optimization. This systematic survey reviews the integration of Autonomous Artificial Intelligence for IT Operations (AIOps) and Generative Artificial Intelligence within cloud-native infrastructure. The study examines cloud-native architectural foundations and explores how artificial intelligence and machine learning enhance operational automation and infrastructure intelligence. Core AIOps capabilities, including automated monitoring, anomaly detection, predictive maintenance, and root cause analysis, are analyzed alongside Generative AI applications in log analysis, incident management, and decision support. Recent literature demonstrates that combining AIOps with Generative AI improves reliability, reduces mean time to recovery, optimizes resource utilization, and minimizes manual intervention. The survey also identifies key challenges, including model drift, explainability limitations, privacy concerns, integration complexity, security risks, and computational demands. Finally, future research directions toward secure, scalable, trustworthy, and self-healing autonomous cloud operations are highlighted for future next-generation digital infrastructure ecosystems.
References
[1] R. Lingam, S. Nagi, M. Chigurupati, and B. T. Myneni, “Resilient DevSecOps: Self-Healing Cloud-Native Systems via SRE-Driven AI Threat Detection and Response,” in 2026 International Conference on Smart Futuristic Technology, IEEE, Jan. 2026, pp. 1–6. doi: 10.1109/ICSFT66733.2026.11508049.
[2] S. Deng et al., “Cloud-Native Computing: A Survey From the Perspective of Services,” Proc. IEEE, vol. 112, no. 1, pp. 12–46, 2024, doi: 10.1109/JPROC.2024.3353855.
[3] M. R. C. Mukkolakkal, “InfraLLM: A Generic Large Language Model Framework for Production-Grade Microservice Auto-Scaling in Cloud Infrastructure,” Int. J. Sci. Res. Mod. Technol., vol. 4, no. 11, pp. 113–123, 2025, doi: 10.38124/ijsrmt.v4i11.1023.
[4] H. Allam, “From Monitoring to Understanding: AIOps for Dynamic Infrastructure,” Int. J. AI, BigData, Comput. Manag. Stud., vol. 4, no. 2, pp. 77–86, 2023, doi: 10.63282/3050-9416.IJAIBDCMS-V4I2P109.
[5] M. G. Paolo Notaro, Jorge Cardoso, “A Systematic Mapping Study in AIOps,” 2020. doi: https://doi.org/10.48550/arXiv.2012.09108.
[6] V. S. R. Narapareddy, “Generative AI and Foundation Models,” Univers. Libr. Innov. Res. Stud., vol. 02, no. 02, pp. 07–21, May 2025, doi: 10.70315/uloap.ulirs.2025.0202002.
[7] B. P. Singh and H. Singh, “Using LLMs for Autonomous Cloud Infrastructure Entitlement Management to Prevent Overprivileged Access,” J. Eng. Comput. Sci., vol. 5, no. 4, pp. 1–14, April, 2026, doi: https://doi.org/10.5281/zenodo.19488212.
[8] Y. Patel, “Self-Adaptive Al-Based Orchestration for Multi-Cloud Interoperability and Performance Optimization,” in SoutheastCon 2026, Huntsville, AL, USA: IEEE, 2026, pp. 01–08, April. doi: 10.1109/SoutheastCon63549.2026.11476031.
[9] A. K. Padhy, T. P. Patel, V. Soni, A. K. Elengovan, G. B. Thokala, and N. Seshagiri, “Cloud-Native Multimodal Semantic Search and Recommendation for Large-Scale Digital Commerce,” in 2026 4th Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON), 2026, pp. 1–6, February. doi: 10.1109/ODICON66687.2026.11470613.
[10] Oyekunle Claudius Oyeniran, Adebunmi Okechukwu Adewusi, Adams Gbolahan Adeleke, Lucy Anthony Akwawa, and Chidimma Francisca Azubuko, “Microservices architecture in cloud-native applications: Design patterns and scalability,” Comput. Sci. IT Res. J., vol. 2, no. 1, pp. 92–106, 2024, doi: 10.51594/csitrj.v5i9.1554.
[11] A. Balalaie, A. Heydarnoori, and P. Jamshidi, “Microservices Architecture Enables DevOps: an Experience Report on Migration to a Cloud-Native Architecture,” IEEE, 2016, pp. 42–52, March. doi: 10.1109/MS.2016.64.
[12] [12] P. Naayini and S. Kamatala, “Automating Infrastructure Platforms with Cloud, Kubernetes, and Site Reliability Engineering,” Int. J. Comput. Tech., vol. 8, no. 6, pp. 1–9, 2021.
[13] V. U. Ugwueze, “Cloud Native Application Development: Best Practices and Challenges,” Int. J. Res. Publ. Rev., vol. 5, no. 12, pp. 2399–2412, Dec. 2024, doi: 10.55248/gengpi.5.1224.3533.
[14] A. K. Padhy, C. Medicherla, B. Vulugundam, C. Kulkarni, T. P. Patel, and S. Shivam, “Latency-Optimised Microservices Orchestration for Real-Time E-Commerce in Multi-Cloud Environments,” in 2025 International Conference on Computer and Applications (ICCA), IEEE, Dec. 2025, pp. 1–6. doi: 10.1109/ICCA66035.2025.11430930.
[15] S. P. P. -, “Cloud Intelligence and AIOps Integration: A Framework for Autonomous IT Operations in Modern Cloud Environments,” Int. J. Multidiscip. Res., vol. 6, no. 6, pp. 1–10, 2024, doi: 10.36948/ijfmr.2024.v06i06.33643.
[16] P. Kumar and H. Shreyas Kumar, “AIOps: Analysing Cloud Failure Detection Approaches for Enhanced Operational Efficency,” in 2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC), IEEE, Jun. 2023, pp. 1–6. doi: 10.1109/ICAISC58445.2023.10199929.
[17] J. Zhaoxue, L. Tong, Z. Zhenguo, G. Jingguo, Y. Junling, and L. Liangxiong, “A Survey On Log Research Of AIOps: Methods and Trends,” Mob. Networks Appl., vol. 26, pp. 2353–2364, 2021, doi: 10.1007/s11036-021-01832-3.
[18] C. Patel, “A Review of Multi-Channel CRM Strategies Using Big Data and Cloud Integration,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 8, no. 1, January-February, pp. 577–588, 2022, doi: https://doi.org/10.32628/IJSRCSEIT.
[19] A. B. Nassif, M. A. Talib, Q. Nasir, and F. M. Dakalbab, “Machine Learning for Anomaly Detection: A Systematic Review,” IEEE Access, vol. 9, pp. 78658–78700, 2021, doi: 10.1109/ACCESS.2021.3083060.
[20] Z. Zhong et al., “A Survey of Time Series Anomaly Detection Methods in the AIOps Domain,” Aug. 2023. doi: https://doi.org/10.48550/arXiv.2308.00393.
[21] D. K. D. Pal, V. R. R. Alluri, S. Thota, V. S. M. B. S. Chitta, M. Shaik, and C. Schwab, “AIOps : Integrating AI and Machine Learning into IT Operations,” Aust. J. Mach. Learn. Res. Appl., vol. 4, no. 1, pp. 288–311, 2024.
[22] T. P. Patel, “Adaptive Token Routing for Heterogeneous LLM Inference in Edge-Cloud Continuum,” in SoutheastCon 2026, Huntsville, AL, USA: IEEE, 2026, pp. 1–7, April. doi: 10.1109/SoutheastCon63549.2026.11476596.
[23] R. Vadisetty et al., “Generative AI for Cloud Infrastructure Automation,” Int. J. Artif. Intell. Data Sci. Mach. Learn., vol. 1, pp. 15–20, 2020, doi: 10.63282/3050-9262.IJAIDSML-V1I3P103.
[24] H. N. Gadbail, K. Sharma, R. Rewatkar, D. Murle, S. Zade, and P. Waghmare, “Integrating AI with Cloud Computing: Architecture, Benefits, and Emerging Innovations,” in 2026 7th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI), Goathgaun, Nepal, 2026, pp. 1242–1248, March. doi: 10.1109/ICMCSI67283.2026.11412712.
[25] V. Raj, “Utilizing AIOps for Predictive Maintenance in Hybrid Cloud Environments,” in 2025 IEEE International Conference on Joint Cloud Computing (JCC), 2025, pp. 131–136. doi: 10.1109/JCC67032.2025.00022.
[26] D. K. Seth, K. K. Ratra, and A. P. Sundareswaran, “AI and Generative AI-Driven Automation for Multi-Cloud and Hybrid Cloud Architectures: Enhancing Security, Performance, and Operational Efficiency,” in 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC), 2025, pp. 784–793. doi: 10.1109/CCWC62904.2025.10903928.
[27] W. C. Potts and C. Carver, “Best Practices Implementing AIOps in Large Organizations,” in 2024 International Conference on Smart Applications, Communications and Networking (SmartNets), IEEE, May 2024, pp. 1–5. doi: 10.1109/SmartNets61466.2024.10577643.
[28] P. K. Thota, “A Generative AI Framework for Autonomous Infrastructure Management in Cloud Operations,” Int. Sci. J. Eng. Manag., vol. 03, no. 10, pp. 1–8, Oct. 2024, doi: 10.55041/ISJEM02027.
[29] S. Sundar Ray, “Autonomous Incident Response Using Generative AI and Agentic Systems in Distributed Enterprise Architectures,” Available SSRN 6647338, 2023.
[30] A. Hayes, E. Carter, D. Foster, S. Reynolds, M. Bennett, and J. Krishnan, “From Logs to Insights: Generative AI for Automated Root-Cause Triage in Distributed Enterprise Systems,” 2022. doi: 10.5281/zenodo.20265420.
