Design and Performance Analysis of AI-Augmented Cloud Orchestration Layers Using Kubernetes
Keywords:
Kubernetes, AI orchestration, cloud computing, auto-scaling, workload prediction, container managementAbstract
Cloud computing has become a backbone for delivering scalable and efficient services, with Kubernetes emerging as the de facto standard for container orchestration. However, increasing complexity in resource management and dynamic workload optimization necessitates intelligent orchestration mechanisms. This paper presents the design and performance evaluation of an AI-augmented orchestration layer integrated with Kubernetes. By embedding machine learning models for workload prediction and auto-scaling decisions, we demonstrate enhanced performance in terms of latency, resource utilization, and fault tolerance. The architecture is tested on hybrid cloud environments, and the results confirm that AI-enhanced orchestration significantly outperforms baseline Kubernetes scheduling.
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Copyright (c) 2022 Tristan Matthias (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
