Optimization of Multi-Resource Scheduling Strategies in Distributed Training Environments

In large-scale model training, distributed training frameworks offer an effective solution to the limitations of a single GPU or node in handling massive model parameters and datasets. By distributing training tasks across multiple nodes, distributed model training enables parallel utilization of computational resources, significantly improving training efficiency. However, efficient scheduling of multi-resources (such as computation, storage, and network) in distributed environments remains a critical challenge. This paper proposes an optimized multi-resource scheduling strategy that integrates Kubernetes’ scheduling principles. By improving the scheduling algorithm, the strategy effectively enhances resource utilization and task completion efficiency. Experimental results demonstrate that the proposed method significantly reduces task waiting time and improves overall system throughput in distributed environments.