Vehicular clouds (VCs) are modern platforms for processing of computation-intensive tasks over vehicles. Such tasks are often represented as directed acyclic graphs (DAGs) consisting of interdependent vertices/subtasks and directed edges. In this paper, we propose a graph neural network-augmented deep reinforcement learning scheme (GA-DRL) for scheduling DAG tasks over dynamic VCs. In doing so, we first model the VC-assisted DAG task scheduling as a Markov decision process. We then adopt a multi-head graph attention network (GAT) to extract the features of DAG subtasks. Our developed GAT enables a two-way aggregation of the topological information in a DAG task by simultaneously considering predecessors and successors of each subtask. We further introduce non-uniform DAG neighborhood sampling through codifying the scheduling priority of different subtasks, which makes our developed GAT generalizable to completely unseen DAG task topologies. Finally, we augment GAT into a double deep Q-network learning module to conduct subtask-to-vehicle assignment according to the extracted features of subtasks, while considering the dynamics and heterogeneity of the vehicles in VCs. Through simulating various DAG tasks under real-world movement traces of vehicles, we demonstrate that GA-DRL outperforms existing benchmarks in terms of DAG task completion time.

翻译：车载云（VCs）是处理车辆计算密集型任务的现代平台。这类任务通常表示为包含相互依赖的顶点/子任务与有向边的有向无环图（DAG）。本文提出一种图神经网络增强的深度强化学习方案（GA-DRL），用于动态车载云中的DAG任务调度。首先，我们将车载云辅助的DAG任务调度建模为马尔可夫决策过程。然后采用多头图注意力网络（GAT）提取DAG子任务特征，所开发的GAT通过同时考虑每个子任务的前驱与后继节点，实现了DAG任务拓扑信息的双向聚合。我们进一步通过编码不同子任务的调度优先级引入非均匀DAG邻域采样，使所开发的GAT能够泛化至完全未见过的DAG任务拓扑结构。最后，我们将GAT增强到双深度Q网络学习模块中，依据提取的子任务特征进行子任务-车辆分配，同时考虑车载云中车辆的动态性与异质性。通过基于真实车辆移动轨迹对多种DAG任务的仿真，我们证明了GA-DRL在DAG任务完成时间上优于现有基准方法。