Optimization of Multi-UAV Edge Computing Systems with Dependent Tasks

Unmanned aerial vehicle (UAV) edge computing effectively reduces task latency and mitigates computing pressure for ground terminals (GTs), particularly in scenarios lacking fixed terrestrial infrastructure. This paper constructs a novel framework for a multi-UAV edge computing system with cross-terminal dependent subtasks, in which the task offloading decision, communication bandwidth allocation, and UAV trajectory planning are jointly optimized. Unlike traditional task offloading schemes, the internal dependency relationships of subtasks impose complex temporal constraints on task offloading decision. Firstly, a directed acyclic graph (DAG) is employed to describe the structure of dependent subtasks. Accounting for computing timeliness requirements and UAV energy constraints, a system cost based on weighted delay and energy consumption is defined. Subsequently, a long-term optimization problem with the objective of minimizing system cost is formulated. In order to solve this complex non-convex mixed-integer programming problem, an algorithm combined with a pre-trained graph attention network (GAT) and the proximal policy optimization (PPO) is proposed. GAT utilizes its specialized graph-processing capabilities to extract high-level subtask features from the DAG. Then PPO integrates these high-dimensional features with environmental state information for global reasoning to obtain the task offloading decision and the UAV trajectory planning. Comprehensive simulations demonstrate that the proposed algorithm effectively reduces system cost under varying system parameters and successfully addresses the unique challenges of a multi-UAV edge computing system with dependent tasks.