Task Demand-Oriented Collaborative Offloading and Deployment Strategy in Software-Defined UAV-Assisted Edge Networks

Abstract

Unmanned aerial vehicles (UAVs), which are a crucial element of the future air-space-ground integrated network, can serve as potential mobile edge computing (MEC) nodes due to their onboard capabilities for storage, communication, and computation. However, current UAV-assisted MEC collaborative offloading methods primarily focus on addressing network requirements for computing tasks, while neglecting the heterogeneity in task demands due to the heterogeneity of task types and UAV service types. To address this, we propose a task demand-oriented collaborative offloading and deployment strategy in software-defined UAV-assisted edge networks. Specifically, to enhance the cache utilization of UAVs, we introduce a software-defined UAV edge network (SD-UEN) architecture, which facilitates cooperation among UAVs under the guidance of a software-defined networking (SDN) controller. In light of the heterogeneity of task demands, we employ the Tabu search-based matching (TSM) algorithm to accurately match computing tasks with the appropriate UAV modes. Furthermore, to enable intelligent UAV mode switching and dynamic UAV location deployment, we leverage the multiagent deep deterministic policy gradient (MADDPG) algorithm. By centrally training the MADDPG model offline, MEC servers and UAVs, acting as learning agents, can efficiently adjust UAV modes and deploy UAVs during online execution. This algorithm dynamically optimizes UAV actions to minimize task completion time and energy consumption. The simulation results highlight that our algorithm substantially reduces task response time and energy consumption compared with other algorithms, demonstrating its effectiveness.