Generative AI-Enhanced Diagnosis of Kubernetes Resources Using K8sGPT and Ollama
Mussa, Muna (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202504156595
https://urn.fi/URN:NBN:fi:amk-202504156595
Tiivistelmä
Cloud computing and containerization have transformed IT infrastructure by providing scalable, flexible, and cost-effective application deployment methods. Cloud computing provides on-demand access to computing resources, accelerating innovation and deployment operational efficiency. Containerization complements this by providing lightweight and portable application environments, ensuring consistency across platforms and driving the adoption of microservices architectures, which enhance scalability and resilience.
However, managing large-scale distributed systems with containers brings complexity, which demands orchestration tools. Kubernetes is an opensource platform created by Google which simplifies deployment, scaling, and management of containerized applications. Despite its advantages, diagnosing and troubleshooting Kubernetes environments is challenging due to the large volume of logs, configuration complexities, and the rabid evolution of the technology.
Generative AI, using natural language processing (NLP) models such as GPT, offers new solutions to Kubernetes diagnostics by simplifying data analysis and providing actionable insights. Tools such as K8sGPT and Ollama showcase how AI enhances the efficiency and ease of Kubernetes management.
This thesis evaluates the impact of generative AI on Kubernetes diagnostics, concentrating on improving operational efficiency and user satisfaction. The study includes an evaluation of K8sGPT and Ollama in practical scenarios to assess their effectiveness in identifying and resolving Kubernetes-related issues.
However, managing large-scale distributed systems with containers brings complexity, which demands orchestration tools. Kubernetes is an opensource platform created by Google which simplifies deployment, scaling, and management of containerized applications. Despite its advantages, diagnosing and troubleshooting Kubernetes environments is challenging due to the large volume of logs, configuration complexities, and the rabid evolution of the technology.
Generative AI, using natural language processing (NLP) models such as GPT, offers new solutions to Kubernetes diagnostics by simplifying data analysis and providing actionable insights. Tools such as K8sGPT and Ollama showcase how AI enhances the efficiency and ease of Kubernetes management.
This thesis evaluates the impact of generative AI on Kubernetes diagnostics, concentrating on improving operational efficiency and user satisfaction. The study includes an evaluation of K8sGPT and Ollama in practical scenarios to assess their effectiveness in identifying and resolving Kubernetes-related issues.