Joint UAV Placement and Dependent Task Offloading in Multi-UAV MEC Networks: A Graph Attention Enhanced DRL Approach

Unmanned aerial vehicles (UAVs) have emerged as effective platforms for mobile edge computing (MEC), offering flexible and efficient computational support to ground users (GUs). Many practical applications, such as deep neural network inference tasks, generate subtasks with complex dependencies, significantly complicating scheduling and offloading decisions. In this paper, we study the joint optimization of UAV deployment, UAV-GU associations, and dependent task offloading decisions within a multi-UAV-enabled MECsystem, aiming to minimize the end time of the overall tasks. The tasks generated by GUs are modeled using directed acyclic graphs (DAGs), explicitly capturing subtask dependencies and execution orders. To address the resulting complex optimization problem, we first propose a Joint Successive convex approximation and Penalty dual decomposition-based Optimization (JSPO) algorithm to determine the initial UAV deployment and UAV-GU associations. Next, we formulate the dependent task offloading decision process as a Markov decision process (MDP), which is solved by employing deep reinforcement learning (DRL). To effectively exploit the structural information within DAG tasks, we integrate a graph attention network (GAT) to provide enhanced state representations for DRL. JSPO and the DRL framework were executed in turns to gradually improve the performance. Extensive simulation results verify that our proposed framework significantly reduces the end time compared to existing methods, demonstrating its superiority in multi-UAV MEC systems.