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papersTODAY 04:00 UTC

HiGFRL Combines Hierarchical Graph Fusion With Reinforcement Learning for Cloud Scheduling

A new arXiv paper introduces HiGFRL, a method that fuses hierarchical graph representations with reinforcement learning to schedule tasks with dependencies across heterogeneous cloud clusters. The authors target the difficulty of jointly handling DAG structure and multi-dimensional resource limits in online settings, which they say existing deep RL schedulers address only partially. The work is a preprint and has not yet been peer reviewed.

arXiv HiGFRL DAG scheduling cloud scheduling graph neural networks reinforcement-learning

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arXiv cs.LGHiGFRL: Hierarchical Graph Fusion-Driven Reinforcement Learning for Dependency-Aware Task Scheduling in Heterogeneous Cloud ↗TODAY 04:00 UTC