What type of study is this?

This is a Experimental Study study.

September 28, 2025Open Access

Towards Autonomous Kubernetes: A Framework for AI-Driven Operations (AIOps)

Key Points

The framework shows a substantial reduction in operational toil, enhancing efficiency in Kubernetes management.
Anomalies are detected and remediated automatically, improving reliability and minimizing errors in dynamic systems.
Machine learning is central to predicting scaling needs, creating a more self-sufficient Kubernetes environment.
The methodology utilizes closed-loop cycles, indicating how observations prompt corrective actions in system management.

Abstract

The increasing use of Kubernetes has brought substantial operational complexity because manual management of its numerous dynamic components (pods, nodes, networks) is slow, error-prone, and unsustainable at scale. This research investigates how AIOps (Artificial Intelligence for IT Operations) principles can move past native automation to establish fully autonomous Kubernetes management. The proposed framework uses machine learning to detect anomalies, identify causes, and predict scaling needs before executing automatic remediation steps. Our methodology demonstrates that AIOps can enhance system reliability and reduce operational Toil while optimizing resource efficiency through closed-loop observation-action cycles, leading to self-healing Kubernetes ecosystems that require minimal human intervention.

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Kishan Raj Bellala (Fri,) studied this question.

synapsesocial.com/papers/68d90a0141e1c178a14f5f2f https://doi.org/https://doi.org/10.38124/ijisrt/25sep1016