mlsanityData to 14 Oct 2026

GATES: Cost-aware Dynamic Workflow Scheduling via Graph Attention Networks and Evolution Strategy

arXiv 2505.12355 ↗First posted 18 May 2025cs.AIcited by 1

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Abstract (arXiv)

Cost-aware Dynamic Workflow Scheduling (CADWS) is a key challenge in cloud computing, focusing on devising an effective scheduling policy to efficiently schedule dynamically arriving workflow tasks, represented as Directed Acyclic Graphs (DAG), to suitable virtual machines (VMs). Deep reinforcement learning (DRL) has been widely employed for automated scheduling policy design. However, the performance of DRL is heavily influenced by the design of the problem-tailored policy network and is highly sensitive to hyperparameters and the design of reward feedback. Considering the above-mentioned issues, this study proposes a novel DRL method combining Graph Attention Networks-based policy network and Evolution Strategy, referred to as GATES. The contributions of GATES are summarized as follows: (1) GATES can capture the impact of current task scheduling on subsequent tasks by learning the topological relationships between tasks in a DAG. (2) GATES can assess the importance of each VM to the ready task, enabling it to adapt to dynamically changing VM resources. (3) Utilizing Evolution Strategy's robustness, exploratory nature, and tolerance for delayed rewards, GATES achieves stable policy learning in CADWS. Extensive experimental results demonstrate the superiority of the proposed GATES in CADWS, outperforming several state-of-the-art algorithms. The source code is available at: https://github.com/YaShen998/GATES.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 33 references that are in the map. First posted on arXiv 18 May 2025.

Builds on

12 of 33 references.

References · 12

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2019Heterogeneous Graph Attention Networkcited by 734
  4. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 574
  5. 2017Learning Combinatorial Optimization Algorithms over Graphscited by 561
  6. 2023DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimizationcited by 221
  7. 2020Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learningcited by 140
  8. 2018Attention-based Graph Neural Network for Semi-supervised Learningcited by 101
  9. 2018Evolution-Guided Policy Gradient in Reinforcement Learningcited by 83
  10. 2020A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAScited by 55
  11. 2022Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Schedulingcited by 20
  12. 2024Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learningcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Deft Scheduling of Dynamic Cloud Workflows with Varying Deadlines via Mixture-of-Expertscited by 0

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  4. 2026On the Role of DAG topology in Energy-Aware Cloud Scheduling : A GNN-Based Deep Reinforcement Learning Approach
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  8. 2026Deep Reinforcement Learning for HPC Job Scheduling: A Statistical Evaluation