KubePlaybook: A Repository of Ansible Playbooks for Kubernetes Auto-Remediation with LLMs (Data Artifact)

Abstract

In the evolving landscape of software development and system op- erations, the demand for automating traditionally manual tasks has surged. Continuous operation and minimal downtimes highlight the need for automated detection and remediation of runtime anom- alies. Ansible, known for its scalable features, including high-level abstraction and modularity, stands out as a reliable solution for managing complex systems securely. The challenge lies in creat- ing an on-the-spot Ansible solution for dynamic auto-remediation, requiring a substantial dataset for in-context tuning of large lan- guage models (LLMs). Our research introduces KubePlaybook, a curated dataset with 130 natural language prompts for generat- ing automation-focused remediation code scripts. After rigorous manual testing, the generated code achieved an impressive 98.86% accuracy rate, affirming the solution’s reliability and performance in addressing dynamic auto-remediation complexities.