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#cloud-computing

2 curated events
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 The work is a preprint and has not yet been peer reviewed.

reinforcement-learning/scheduling/cloud-computing/graph-neural-networks
papersTODAY 04:00 UTC

Graph Attention-Driven Hierarchical Reinforcement Learning for Cloud Workflow Scheduling

A new arXiv paper proposes a hierarchical reinforcement learning method that uses graph attention to schedule workflows in cloud environments. The approach targets three competing goals at once: meeting deadlines, improving container utilization, and lowering energy use. It also accounts for unpredictable task runtimes, communication costs that depend on where tasks are placed, and the need to decide task assignment and container selection together.

reinforcement-learning/cloud-computing/scheduling/graph-neural-networks