Review Article

Advances and Challenges in AI-Driven Kubernetes Management: A Comprehensive Survey of Root Cause Analysis, Self-Healing and Observability

Volume: 3 Number: 1 August 24, 2026

Advances and Challenges in AI-Driven Kubernetes Management: A Comprehensive Survey of Root Cause Analysis, Self-Healing and Observability

Abstract

The adoption of Kubernetes as a foundational container orchestration platform has revolutionized the deployment, scaling, and management of microservice-based applications. This survey provides a comprehensive review of representative studies in Kuber-netes management, anomaly detection, root cause analysis (RCA), and self-healing systems. We classify the literature into five core domains: container management and scheduling, anomaly detection and observability, AI-assisted root cause analysis, self-healing frameworks, and security-hardening mechanisms. Through critical analysis, we identify prevailing methodological approaches, including rule-based heuristics, machine learning, reinforcement learning, knowledge graph embeddings, and large language model (LLM)-assisted diagnostics. Emerging trends include proactive AI-driven monitoring, multi-agent orchestration, predictive self-healing, and explainable automated reasoning. Despite substantial advances, open challenges persist in scalability to heterogeneous and multi-cloud environments, dataset standardization, human-in-the-loop integration, and secure automated remediation. By syn-thesizing these findings, this survey provides a structured roadmap for researchers and practitioners, highlighting key gaps and opportunities to develop resilient, efficient, and intelligent Kubernetes ecosystems capable of meeting the demands of modern cloud-native and edge-aware infrastructures.

Keywords

References

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Details

Primary Language

English

Subjects

Cloud Computing

Journal Section

Review Article

Publication Date

August 24, 2026

Submission Date

February 20, 2026

Acceptance Date

August 3, 2026

Published in Issue

Year 2026 Volume: 3 Number: 1

APA
Gundogdu, E., Kayaduman, U., & Pınarer, O. (2026). Advances and Challenges in AI-Driven Kubernetes Management: A Comprehensive Survey of Root Cause Analysis, Self-Healing and Observability. Transactions on Computer Science and Applications, 3(1), 1-13. https://izlik.org/JA63WW23SU
AMA
1.Gundogdu E, Kayaduman U, Pınarer O. Advances and Challenges in AI-Driven Kubernetes Management: A Comprehensive Survey of Root Cause Analysis, Self-Healing and Observability. TCSA. 2026;3(1):1-13. https://izlik.org/JA63WW23SU
Chicago
Gundogdu, Emre, Unal Kayaduman, and Ozgun Pınarer. 2026. “Advances and Challenges in AI-Driven Kubernetes Management: A Comprehensive Survey of Root Cause Analysis, Self-Healing and Observability”. Transactions on Computer Science and Applications 3 (1): 1-13. https://izlik.org/JA63WW23SU.
EndNote
Gundogdu E, Kayaduman U, Pınarer O (August 1, 2026) Advances and Challenges in AI-Driven Kubernetes Management: A Comprehensive Survey of Root Cause Analysis, Self-Healing and Observability. Transactions on Computer Science and Applications 3 1 1–13.
IEEE
[1]E. Gundogdu, U. Kayaduman, and O. Pınarer, “Advances and Challenges in AI-Driven Kubernetes Management: A Comprehensive Survey of Root Cause Analysis, Self-Healing and Observability”, TCSA, vol. 3, no. 1, pp. 1–13, Aug. 2026, [Online]. Available: https://izlik.org/JA63WW23SU
ISNAD
Gundogdu, Emre - Kayaduman, Unal - Pınarer, Ozgun. “Advances and Challenges in AI-Driven Kubernetes Management: A Comprehensive Survey of Root Cause Analysis, Self-Healing and Observability”. Transactions on Computer Science and Applications 3/1 (August 1, 2026): 1-13. https://izlik.org/JA63WW23SU.
JAMA
1.Gundogdu E, Kayaduman U, Pınarer O. Advances and Challenges in AI-Driven Kubernetes Management: A Comprehensive Survey of Root Cause Analysis, Self-Healing and Observability. TCSA. 2026;3:1–13.
MLA
Gundogdu, Emre, et al. “Advances and Challenges in AI-Driven Kubernetes Management: A Comprehensive Survey of Root Cause Analysis, Self-Healing and Observability”. Transactions on Computer Science and Applications, vol. 3, no. 1, Aug. 2026, pp. 1-13, https://izlik.org/JA63WW23SU.
Vancouver
1.Emre Gundogdu, Unal Kayaduman, Ozgun Pınarer. Advances and Challenges in AI-Driven Kubernetes Management: A Comprehensive Survey of Root Cause Analysis, Self-Healing and Observability. TCSA [Internet]. 2026 Aug. 1;3(1):1-13. Available from: https://izlik.org/JA63WW23SU