用时空图神经网络优化云边端任务调度，提升资源均衡与执行效率。
PPO-STGNN: A Proximal Policy Optimization Approach with Spatio-Temporal Graph Neural Networks for DAG Task Scheduling in Cloud-Edge-End Computing
- 结合PPO与时空图神经网络，捕捉任务与资源的动态关系。
- 使任务完成时间低、负载均衡度高，显著改善系统性能。
- 适合复杂异构环境下动态任务调度，尤其适用于物联网场景。
随着物联网快速发展，计算密集型有向无环图（DAG）任务在云-边-端协同环境中日益普遍。然而，云端、边缘端和终端节点在计算能力、网络带宽和能耗方面存在高度异质性，使得具有复杂依赖关系的任务调度成为NP难问题。传统启发式算法和常规强化学习方法难以捕捉系统资源的时空动态特性。本文提出PPO-STGNN，一种将近端策略优化（PPO）与时空图神经网络（STGNN）结合的DAG任务调度算法。该方法利用STGNN从任务拓扑和物理资源图中提取特征，并通过PPO优化调度策略，以最小化完工时间（makespan）和调度长度比（SLR），同时提升CPU与内存负载均衡。为加速收敛，引入多教师行为克隆机制进行预训练。实验结果表明，PPO-STGNN在保持低完成时间的同时显著改善负载均衡，适用于动态异构的云-边-端DAG调度场景。
原文摘要 · Abstract (English)
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 highly heterogeneous in computing capacity, network bandwidth, and energy consumption, which makes the efficient scheduling of tasks with complex dependencies an NP-hard problem. Traditional heuristic algorithms and conventional reinforcement-learning methods often fail to capture the spatio-temporal dynamics of system resources. This paper proposes PPO-STGNN, a DAG task-scheduling algorithm that integrates proximal policy optimization (PPO) with spatio-temporal graph neural networks (STGNNs). The method uses an STGNN to extract features from both the DAG task topology and the physical cloud-edge-end resource graph, and then optimizes the scheduling policy through PPO to minimize makespan and schedule length ratio (SLR) while improving CPU and memory load balancing. To accelerate convergence, a multi-teacher behavior-cloning mechanism is introduced for pretraining. Experimental results show that PPO-STGNN significantly improves load balancing while maintaining a low completion time, making it suitable for dynamic and heterogeneous cloud-edge- end DAG scheduling scenarios.
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