GART: Graph Neural Network-Based Adaptive and Robust Task Scheduler for Heterogeneous Distributed Computing
Shiyu Yang, Guanyu Ding, Zifan Chen, Jie Si Yang · IEEE Access · 2025
Modern distributed computing systems face significant challenges in achieving optimal resource utilization and maintaining performance stability when operating in heterogeneous cluster environments with dynamic workloads. Existing task scheduling approaches often rely on heuristic rules or static policies that fail to adapt to runtime variations such as node failures, load fluctuations, and heterogeneous resource capacities. To address these limitations, we propose GART (Graph-based Adaptive Robust Task scheduling), a novel framework that leverages graph neural networks (GNN) and reinforcement learning for intelligent task scheduling in distributed systems. GART models distributed task workflows as directed acyclic graphs (DAGs) and employs a specially designed GNN architecture to capture complex inter-task dependencies and resource requirements. The framework learns optimal scheduling policies through deep reinforcement learning, enabling adaptive decision-making that responds to dynamic system conditions. Our approach incorporates a robust scheduling mechanism that maintains high throughput and low latency even under adverse conditions including node failures and sudden load spikes. Extensive experiments on the Alibaba cluster trace dataset demonstrate that GART achieves 23.7% improvement in average job completion time, 31.4% reduction in resource imbalance, and 42.6% better resilience to node failures compared to state-of-the-art baseline schedulers including Shortest Job First, Tetrisched, and Decima. The results validate GART’s effectiveness in real-world heterogeneous distributed computing environments and its potential for deployment in production systems.