PPO-STGNN：一种用于云边端计算中DAG任务调度的结合时空图神经网络的近端策略优化方法

中文摘要 AI 辅助

随着物联网的快速发展，计算密集型有向无环图（DAG）任务在云边端协同环境中变得越来越普遍。然而，云、边缘和终端节点在计算能力、网络带宽和能耗方面高度异构，这使得对具有复杂依赖关系的任务进行高效调度成为一个NP难问题。传统启发式算法和常规强化学习方法往往无法捕捉系统资源的时空动态。本文提出了PPO-STGNN，一种将近端策略优化（PPO）与时空图神经网络（STGNN）相结合的DAG任务调度算法。该方法利用STGNN从DAG任务拓扑结构和物理云边端资源图中提取特征，然后通过PPO优化调度策略，以最小化完成时间（makespan）和调度长度比（SLR），同时提升CPU和内存负载均衡能力。为加速收敛，引入了多教师行为克隆机制用于预训练。实验结果表明，PPO-STGNN在保持低完成时间的同时显著提升了负载均衡，适用于动态且异构的云边端DAG调度场景。

英文摘要

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.

发表机构

• Beijing University of Posts and Telecommunications（北京邮电大学）
• Google（谷歌）
• NanJing University（南京大学）

机构由 AI 辅助整理，请以论文原文为准。