AI-Driven Platform Engineering for Autonomous Kubernetes Operations: An Experimental Framework for Predictive Resource Optimization, Incident Prediction, and Self-Healing in Cloud-Native Environments

Abstract

The rapid adoption of Kubernetes and cloud-native architectures has transformed modern application deployment by enabling scalability, portability, and automated management. However, the increasing complexity of distributed cloud-native systems introduces significant operational challenges, including inefficient resource allocation, reactive scaling limitations, delayed incident detection, and prolonged recovery processes. Existing Kubernetes management approaches typically address individual operational tasks, such as autoscaling, anomaly detection, or failure recovery, while an integrated framework that combines predictive intelligence, resource optimization, and autonomous remediation remains limited.

This study proposes an AI-driven platform-engineering framework for autonomous Kubernetes operations that integrates workload prediction, incident detection, intelligent resource optimization, and policy-controlled self-healing within a closed-loop architecture. The proposed framework combines Kubernetes observability data, including resource metrics, logs, events, and distributed traces, with machine-learning and deep-learning models to enable proactive operational decision-making.

The framework is evaluated using reproducible cloud-native benchmark environments, including Kubernetes-based microservice applications and publicly available workload traces such as Google Cluster Workload Trace and Alibaba Cluster Trace. Multiple artificial intelligence models are investigated, including ARIMA, XGBoost, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer models for workload forecasting, as well as Logistic Regression, Random Forest, XGBoost, Isolation Forest, and Autoencoder-based approaches for incident prediction.

The evaluation examines workload prediction accuracy, incident detection performance, Kubernetes resource efficiency, self-healing capability, and operational cost effectiveness using metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), R², accuracy, precision, recall, F1-score, AUROC, CPU utilization, memory utilization, latency, Mean Time to Recovery (MTTR), and resource efficiency indicators.

The experimental findings demonstrate that the proposed framework improves predictive workload management, enhances incident identification, reduces resource overprovisioning, and accelerates automated recovery compared with conventional Kubernetes management approaches. This research contributes a reproducible AI-driven platform-engineering architecture that advances Kubernetes operations from reactive administration toward predictive, adaptive, and autonomous cloud-native infrastructure management