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

• URL: http://arxiv.org/abs/2609.14952v1
• Date: Mon, 14 Sep 2026 02:50:40 GMT
• Title: Cloud Workflow Scheduling Based on Graph Attention-Driven Hierarchical Reinforcement Learning
• Abstract summary: We develop a Graph Attention-Driven Reinforcement Learning (GA-HRL) framework and model scheduling process.<n>It balances deadline satisfaction, container utilization, and energy consumption while dealing with task-execution speeds, placement-dependent communication, and coupled task and container decisions.<n>Experiments on the 2018 Alibaba cluster trace show that GA-HRL maintains competitive workflow success rate and, in settings where success is comparable, achieves higher container utilization and lower energy consumption.
• Score: 10.978371228753518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 from the evolving DAGs. Based on these representations, we develop a Graph Attention-Driven Hierarchical Reinforcement Learning (GA-HRL) framework and model the scheduling process as an event-driven hierarchical semi-Markov decision process (SMDP). Workflow arrivals and task completions trigger scheduling events. At each scheduling event, the Task Scheduling (TS) agent first processes the currently ready tasks by assigning them to admissible existing containers or requesting new ones. The requested containers are then processed by the Container Scheduling (CS) agent for host placement before the environment advances. The two agents are trained alternately using separate Proximal Policy Optimization (PPO). Experiments on the 2018 Alibaba cluster trace show that GA-HRL maintains competitive workflow success rate and, in settings where success is comparable, generally achieves higher container utilization and lower energy consumption. Under the largest speed variation, it trades a small success-rate margin for substantially lower energy. Simulation code is available at: https://github.com/zongjin130/GA-HRL.

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