SOURCE-LINKED INTELLIGENCE

Cloud Workflow Scheduling Based on Graph Attention-Driven Hierarchical Reinforcement Learning

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

Dynamic cloud workflow scheduling must balance deadline satisfaction, container utilization, and energy consumption while dealing with stochastic task-execution speeds, placement-dependent communication, and coupled task and container decisions. Workflows are naturally modeled as directed acyclic graphs (DAGs), but conventional vector- or matrix-based states do not fully capture their dependency topology. To better represent task urgency and structural relationships, we assign predicted sub-deadlines to tasks and use a multi-head graph attention network (GAT) to extract dependency information

Read original source ↗ Open in workspace

recordType

paper

region

Global