This article presents Dynamic GPU-Aware Scheduling, an innovative approach for optimizing distributed data science workloads in Kubernetes environments. Traditional Kubernetes schedulers treat GPUs as binary resources without considering their utilization patterns, memory characteristics, or computational capabilities, leading to significant inefficiencies in resource allocation. The proposed system enhances scheduling through real-time GPU metrics collection, predictive analytics using machine learning models, intelligent workload assignment, and robust multi-tenancy support. Implementation strategies focus on seamless integration with existing Kubernetes infrastructure through custom scheduler extensions, resource definitions, and API primitives. Real-world deployments across manufacturing, cloud computing, scientific research, and healthcare demonstrate substantial improvements in resource efficiency, workload performance, and operational benefits. The system addresses key challenges including monitoring overhead, prediction accuracy, hardware heterogeneity, and reliability concerns. Future development directions include cross-cluster federation, specialized hardware integration, energy-aware scheduling, and federated learning optimizations. This article represents a significant advancement in cloud-native GPU resource management, enabling organizations to achieve higher utilization, reduced costs, and improved performance for AI and data science applications.
Keywords: GPU resource management, Kubernetes scheduling, distributed data science, machine learning infrastructure, multi-tenant computing
