Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2025

Abstract

Dynamic workflow scheduling (DWS) in cloud computing presents substantial challenges due to heterogeneous machine configurations, unpredictable workflow arrivals/patterns, and constantly evolving environments. However, existing research often assumes homogeneous setups and static conditions, limiting flexibility and adaptability in real-world scenarios. In this paper, we propose a novel Graph assisted Offline-Online Deep Reinforcement Learning (GOODRL) approach to building an effective and efficient scheduling agent for DWS. Our approach features three key innovations: (1) a task-specific graph representation and a Graph Attention Actor Network that enable the agent to dynamically assign focused tasks to heterogeneous machines while explicitly considering the future impact of each machine on these tasks; (2) a system-oriented graph representation and a Graph Attention Critic Network that facilitate efficient processing of new information and understanding its impact on the current state, crucial for managing unpredictable workflow arrivals/patterns in real-time; and (3) an offline-online method that utilizes imitation learning for effective offline training and applies gradient control and decoupled high-frequency critic training techniques during online learning to sustain the agent’s robust performance in rapidly changing environments. Experimental results demonstrate that GOODRL significantly outperforms several state-of-the-art algorithms, achieving substantially lower mean flowtime and high adaptability in various online and offline scenarios.

Keywords

workflow scheduling, graph attention neural network, reinforcement learning, online learning

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Sustainability

Publication

Proceedings of the 13th International Conference on Learning Representations, Singapore, 2025 April 24

First Page

8174

Last Page

8202

ISBN

9798331320850

Publisher

ICLR

City or Country

Singapore

Additional URL

https://openreview.net/forum?id=4PlbIfmX9o

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