An, AijunZhou, Zehao2026-07-242026-07-242026-04-282026-07-24https://hdl.handle.net/10315/43939Efficient scheduling of deep learning workloads in heterogeneous GPU clusters is increasingly challenging due to diverse workload characteristics and stringent memory constraints. Existing approaches often rely on coarse-grained metrics such as runtime or resource demand, which fail to capture workload semantics and lead to resource misallocation, where high-memory GPUs are occupied by low-demand jobs while memory-intensive workloads remain blocked. To address this limitation, we first develop a semantic-aware workload simulator that models deep learning jobs using intrinsic attributes such as model type, dataset size, and training configuration, and estimates execution behavior based on hardware-agnostic computational workloads. This enables more realistic modeling of heterogeneous performance and system-level behaviors. Building on this foundation, we propose SARL, a Semantic-Aware Reinforcement Learning scheduler that incorporates workload semantics and hardware heterogeneity into scheduling decisions. We formulate the scheduling problem as a Markov Decision Process and introduce a model-based GPU allocation mechanism to enable efficient decision making under large action spaces. Experimental results demonstrate that SARL consistently outperforms strong baselines, achieving a 29.6% reduction in deadline miss rate and a 14.4% reduction in job completion time (JCT).Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.Computer scienceElectronic Thesis or Dissertation2026-07-24Reinforcement learningGPU Cluster SchedulingHeterogeneous GPU ClustersDeep Learning WorkloadsResource AllocationJob SchedulingDeadline-Aware Scheduling