HiGFRL: Hierarchical Graph Fusion-Driven Reinforcement Learning for Dependency-Aware Task Scheduling in Heterogeneous Cloud

arXiv:2609.14968 · cs.LG · Submitted 2026-09-14 · Read on arXiv

cs.LG

Submitted: 2026-09-14

Updated: 2026-09-14

Comments: 16 pages, 13 figures

Code: https://github.com/igeng/HiGFRL

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Reinforcement Learning — 25× in this paper · explained in 4 episodes
• Hypergraph — 17× in this paper · explained in 2 episodes
• Actor — 16× in this paper · explained in 3 episodes
• Deep Reinforcement Learning — 12× in this paper · explained in 2 episodes
• Average Task Wait Time — 10× in this paper
• Aggregation — 9× in this paper · explained in 1 episode
• Trace — 9× in this paper · explained in 2 episodes
• Average Task Flow Time — 8× in this paper
• Action Space — 7× in this paper · explained in 1 episode
• Resource Constraints — 7× in this paper · explained in 3 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Decoupling — 6× in this paper · explained in 2 episodes
• GNNs — 6× in this paper · explained in 2 episodes
• Allocator — 5× in this paper · explained in 1 episode
• Over-smoothing — 5× in this paper · explained in 4 episodes
• Adaptability — 4× in this paper · explained in 1 episode
• Core — 4× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Feature Extraction — 4× in this paper · explained in 2 episodes
• Objective Function — 4× in this paper · explained in 2 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Total Retry Count — 3× in this paper
• Transformer — 3× in this paper · explained in 2 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Action Masking — 2× in this paper · explained in 1 episode
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Deep Reinforcement Learning (DRL) — 2× in this paper · explained in 2 episodes
• Entanglement — 2× in this paper · explained in 3 episodes
• Graph Construction — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Directed Acyclic Graph — 1× in this paper
• Dual-Stream Architecture — 1× in this paper · explained in 3 episodes
• Dynamic Weighting — 1× in this paper · explained in 3 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Graph Reinforcement Learning — 1× in this paper
• Markov Decision Process — 1× in this paper
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• Mobile Edge Computing — 1× in this paper · explained in 2 episodes
• Monte Carlo Tree Search — 1× in this paper
• Multi-objective Optimization — 1× in this paper · explained in 4 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• While Deep Reinforcement Learning — 1× in this paper

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