Generative AI for Self-Healing Systems
Pitikorn Khlaisamniang, Prachaya Khomduean, Kriangkrai Saetan, Supasin Wonglapsuwan
Abstract
In large-scale system production, the risk of component failures like hardware issues, software bugs, network disruptions, and memory errors is a concern. To mitigate this, human experts such as IT analysts, system engineers, and infrastructure architects use system monitoring to detect and respond to failures. This study aims to integrate generative AI technology into self-healing systems to enhance the operations of large-scale systems and facilitate automatic repairs. The focus is on optimizing system functionality and efficiency at scale while reducing reactive tasks that require human intervention. Our proposed solutions involve leveraging generative AI for anomaly detection, code generation, debugging and auto generative report within self-healing systems. Furthermore, the automated response ansible scripts, generated by generative AI such as GPT-4 to create a comprehensive and efficient python code completion solution that enhances backend system functionality and repairs failing components.