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
Authors
Emre Gundogdu
This is me
0009-0004-1712-2757
Türkiye
Unal Kayaduman
This is me
0009-0007-1131-4071
Türkiye
Ozgun Pınarer
*
0000-0002-0280-3689
Türkiye
Publication Date
August 24, 2026
Submission Date
February 20, 2026
Acceptance Date
August 3, 2026
Published in Issue
Year 2026 Volume: 3 Number: 1