HARMONI: Heterogeneity-Aware I/O Scheduling for Mixed Workloads in SSD-Based HPC Systems
Year: 2026
Type: article
Abstract: Modern HPC systems increasingly rely on tiered storage architectures with SSDs serving as a critical performance tier. However, the inherent asynchronous I/O characteristics of SSDs, including read/write bandwidth asymmetry and interference, pose significant challenges for traditional I/O schedulers. These challenges are exacerbated by the convergence of bursty HPC write workloads (e.g., checkpointing) and sustained AI read workloads (e.g., data streaming) on shared SSD infrastructure. Existing schedulers fail to adequately address these combined workloads, leading to suboptimal resource utilization. This article introduces HARMONI, a heterogeneity-aware reinforcement learning scheduler for mixed I/O in HPC storage systems. HARMONI leverages a graph neural network (GNN) to encode task-SSD dependencies and a hybrid interference predictor to adapt to hardware and I/O variations. Experimental results across diverse HPC and AI workloads demonstrate that HARMONI significantly reduces average makespan by up to 90% compared with state-of-the-art schedulers, effectively bridging the gap between evolving storage hardware and the dynamic I/O demands of modern HPC systems.
DOI: 10.1145/3839231
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7203879777