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Two-Stage Workflow Scheduling Based on Deep Reinforcement Learning
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DOI：10.1007/978-3-031-93251-9_18.png)
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Abstract
En 中文
In expanding cloud clusters with increasing users, resource diversity and job variety multiply, making efficient task scheduling crucial for optimal resource utilization. Traditional methods, relying on heuristic rules or static strategies, struggle to adapt to dynamic settings, achieve global optimization, or handle complex task relationships. Therefore, this paper proposes a scheduling algorithm based on two-stage deep reinforcement learning and residual networks (TD2QN). In the task selection stage, the agent prioritizes tasks based on their attributes and urgency. In the resource allocation stage, tasks are allocated considering dependencies and resource availability for virtual execution. Introducing a residual network into the neural network structure enhances model performance and stability, allowing better adaptation to complex workflows, improved resource utilization, and reduced resource usage costs. Simulation results from real-world scientific workflow datasets demonstrate that compared to advanced algorithms like LB-HEFT, GA-GWO, and DQN, this method excels in cost savings, enhancing resource utilization, and other scheduling aspects across various scales and forms of workflows. When dealing with large-scale workflows, the algorithm reduces execution time by 25% to 28%, saves approximately 18% to 28% in costs, and improves resource utilization by 0.6% to 10.5%. Thus, it offers a viable solution to the challenge of workflow scheduling in heterogeneous cloud environments.
Keywords:
Deep reinforcement learning
multi-objective scheduling
cloud computing
workflow scheduling
heterogeneous cloud
residual networks
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