TOM: Joint Trajectory, Offloading and Migration Optimization in Stateful Service-Oriented UAV-Enabled VEC System

Qijie Qiu, Lingjie Li, Zhijiao Xiao, Qiuzhen Lin, Lijia Ma, Zhong Ming

Abstract: With the development of unmanned aerial vehicle (UAV) technology, UAV-enabled vehicular edge computing (VEC) has emerged as a powerful computational paradigm that improves edge resource efficiency. In particular, supporting stateful services, which require persistent context across offloading sessions, introduces new challenges. In the VEC systems, computation offloading, UAV trajectory planning, and service migration must be jointly optimized to maintain quality of service (QoS). However, existing works rarely consider this joint optimization, especially under high mobility scenarios. To fill this gap, this paper first considers the joint computation offloading, UAV trajectory, and service migration problem in the stateful service-oriented UAV-enabled VEC system and then formulates it as a dynamic multi-objective optimization problem, with the purpose of minimizing UAV flight cost, vehicle energy consumption, service migration time, and age of information (AoI). To effectively address the formulated problem, a novel joint Trajectory, Offloading, and Migration optimization approach (TOM) based on a dynamic multifactorial evolutionary algorithm is proposed. In particular, a service migration strategy is designed in TOM to efficiently migrate services in a parallel manner. In addition, an environmental adaptation strategy is triggered to cope with rapid dynamic changes in the environment. Extensive simulations on real-world datasets show that our proposed method outperforms several state-of-the-art peer methods.

External IDs:dblp:journals/tsc/QiuLXLMM25