Resource scheduling optimization for cloud-native AI inference services based on Kubernetes

Cloud-native AI inference services have emerged as critical components in modern distributed computing environments, requiring sophisticated resource management strategies to achieve optimal performance and cost efficiency. This paper presents a comprehensive framework for resource scheduling optimization in Kubernetes-based AI inference deployments, addressing the fundamental challenges of dynamic workload distribution, multi-dimensional resource allocation, and quality of service preservation. Our proposed methodology integrates machine learning-driven prediction models with adaptive scheduling algorithms to enhance resource utilization efficiency while maintaining service level agreements. The experimental evaluation demonstrates substantial enhancements in resource allocation accuracy, reduction in inference latency by 23.7%, and overall cluster utilization optimization of 31.2% compared to traditional scheduling approaches. The framework incorporates real-time monitoring capabilities, predictive scaling mechanisms, and intelligent load balancing strategies specifically designed for AI inference workloads. Performance analysis across heterogeneous cluster configurations validates the effectiveness of our approach in diverse deployment scenarios, providing substantial benefits for cloud-native AI service providers.