Computation Offloading Optimization for UAV-Based Cloud-Edge Collaborative Task Scheduling Strategy
Haosheng Chen, Haixia Cui, Jiahuan Wang, Peng Cao, Yejun He, Mohsen Guizani
Abstract
Unmanned aerial vehicles (UAVs) with communication and computing capabilities are essential for supporting mobile edge computing (MEC). They can be deployed flexibly to provide computing services for remote mountainous areas or disaster-stricken areas with damaged communication facilities, thereby enhancing communication quality and coverage area. Considering the 5G ultra-reliable and low-latency communication (URLLC) application requirement issues, this paper focuses on a UAV-based cloud-edge cooperative scheduling system aimed at minimizing the system processing delay while maintaining offloading fairness. We address the optimization of user scheduling, UAV flight trajectory, and task offloading ratio under constraints on the energy consumption and discrete variables. Given the complexity of formulated optimization problem, which involves mixed-integer nonlinear programming (MINLP), non-convexity, high-dimensional state space, and continuous action space, we introduce a deep reinforcement learning (DRL)-based computation offloading approach that leverages the deep deterministic policy gradient (DDPG) algorithm. It explores the continuous action space, encompassing variables like UAV flight angle and speed, takes precise actions according to the current state, and ultimately identifies an optimal offloading strategy. Simulation experiments show that the proposed algorithm notably diminishes the processing delay in contrast to other baseline approaches, while they also confirm that the effectiveness of cloud-edge hybrid offloading surpasses that of either single cloud offloading or UAV offloading strategies.