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Vehicular clouds (VCs) are modern platforms for processing ofcomputation-intensive tasks over vehicles. Such tasks are often represented asdirected acyclic graphs (DAGs) consisting of interdependent vertices/subtasksand directed edges. In this paper, we propose a graph neural network-augmenteddeep reinforcement learning scheme (GA-DRL) for scheduling DAG tasks overdynamic VCs. In doing so, we first model the VC-assisted DAG task scheduling asa Markov decision process. We then adopt a multi-head graph attention network(GAT) to extract the features of DAG subtasks. Our developed GAT enables atwo-way aggregation of the topological information in a DAG task bysimultaneously considering predecessors and successors of each subtask. Wefurther introduce non-uniform DAG neighborhood sampling through codifying thescheduling priority of different subtasks, which makes our developed GATgeneralizable to completely unseen DAG task topologies. Finally, we augment GATinto a double deep Q-network learning module to conduct subtask-to-vehicleassignment according to the extracted features of subtasks, while consideringthe dynamics and heterogeneity of the vehicles in VCs. Through simulatingvarious DAG tasks under real-world movement traces of vehicles, we demonstratethat GA-DRL outperforms existing benchmarks in terms of DAG task completiontime.

GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Clouds