Autonomous Kubernetes Cluster Healing using Machine Learning
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Abstract
Modern Kubernetes environments underpin mission-critical applications across healthcare, finance, and cloud-native enterprises. While Kubernetes provides robust primitives for container orchestration, it still relies heavily on manual intervention, static rules, and reactive alerts to recover from failures such as pod crashes, node instability, resource exhaustion, and cascading service degradation. As clusters scale in size and complexity, traditional monitoring and rule-based remediation mechanisms become insufficient to meet strict reliability objectives.
This paper presents an autonomous Kubernetes cluster healing framework driven by machine learning, designed to proactively detect anomalies, predict failure patterns, and execute self-healing actions without human intervention. The proposed system combines telemetry from Kubernetes control planes, observability platforms, and application-level signals with machine learning models that learn normal and abnormal operational behavior. By integrating predictive analytics with automated remediation workflows, the framework enables clusters to recover from failures faster, reduce mean time to detect (MTTD), and significantly lower mean time to recovery (MTTR).
Unlike conventional auto-scaling or threshold-based alerting, the proposed approach leverages historical incident patterns, resource utilization trends, and service-level indicators (SLIs) to make context-aware healing decisions. The architecture supports common remediation actions such as intelligent pod restarts, node cordoning, workload rescheduling, configuration rollbacks, and policy-driven scaling. The framework is cloud-agnostic and applicable to Kubernetes platforms deployed on Azure Kubernetes Service (AKS), Amazon EKS, and hybrid environments.
The study demonstrates how machine learning–driven autonomous healing improves cluster resilience, reduces operational toil, and enhances service reliability in regulated, production-grade environments. This work contributes a practical foundation for next-generation self-managing Kubernetes systems and establishes a pathway toward fully autonomous cloud-native infrastructure.
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References
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