mlsanityData to 14 Oct 2026

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

arXiv 2609.14952 ↗First posted 14 Sep 2026cs.LG cs.NIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 34 references that are in the map. First posted on arXiv 14 Sep 2026.

Builds on

5 of 34 references.

References · 5

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Graph Attention Networkscited by 8,923
  3. 2023GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Cloudscited by 10
  4. 2020Performance Optimization for Edge-Cloud Serverless Platforms via Dynamic Task Placementcited by 4
  5. 2023Dependent Task Offloading in Edge Computing Using GNN and Deep Reinforcement Learningcited by 3

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Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026HiGFRL: Hierarchical Graph Fusion-Driven Reinforcement Learning for Dependency-Aware Task Scheduling in Heterogeneous Cloud
  2. 2024Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learning
  3. 2026Deft Scheduling of Dynamic Cloud Workflows with Varying Deadlines via Mixture-of-Experts
  4. 2025GATES: Cost-aware Dynamic Workflow Scheduling via Graph Attention Networks and Evolution Strategy
  5. 2024Reinforcement Learning-driven Data-intensive Workflow Scheduling for Volunteer Edge-Cloud
  6. 2026On the Role of DAG topology in Energy-Aware Cloud Scheduling : A GNN-Based Deep Reinforcement Learning Approach
  7. 2026MCRL2: Multi-resource Cross-attention-based Representation Learning-augmented Reinforcement Learning for Cloud Microservice Scheduling
  8. 2025Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review