A Semantic-Aware Reinforcement Learning Scheduler for Parameterized Deep Learning Jobs on Heterogeneous Multi-GPU Clusters
Year: 2026
Type: dissertation
Abstract: Efficient 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).
Source: YorkSpace (York University)
Author: Zhou, Zehao
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7171274090