English(EN) PPO-STGNN: A Proximal Policy Optimization Approach with Spatio-Temporal Graph Neural Networks for DAG Task Scheduling in Cloud-Edge-End Computing

新算法使用PPO和STGNN优化云-边-端有向无环图任务调度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-04 04:00

研究人员开发了PPO-STGNN，这是一种用于复杂云-边-端计算环境中定向无环图（DAG）任务调度的创新算法。该方法结合了近端策略优化（PPO）和时空图神经网络（STGNNs），以有效管理跨不同计算节点的异构资源。该算法旨在最小化任务完成时间和改善负载均衡，利用多教师行为克隆机制加速收敛。实验结果表明，PPO-STGNN在动态调度场景下的负载均衡和效率方面提供了显著改进。 AI

影响这项研究为优化分布式计算环境中的任务调度引入了一种新颖的方法，有望提高复杂云-边-端系统的效率和资源利用率。

排序理由该集群包含一篇详细介绍新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yangshuo Qi, Chenwei Wang, Zihan Shen, Songlin Sun · 2026-09-04 04:00

PPO-STGNN：一种用于云-边-端计算中 DAG 任务调度的基于时空图神经网络的近端策略优化方法

arXiv:2609.03503v1 Announce Type: new Abstract: With the rapid development of the Internet of Things, computation intensive directed acyclic graph (DAG) tasks have become increasingly common in cloud-edge-end collaborative environments. However, cloud, edge, and end nodes are hig…

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报道来源 [1]

PPO-STGNN：一种用于云-边-端计算中 DAG 任务调度的基于时空图神经网络的近端策略优化方法

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