Two Time-Scale Joint Service Caching and Task Offloading for UAV-assisted Mobile Edge Computing

The emergence of unmanned aerial vehicles (UAVs) extends the mobile edge computing (MEC) services in broader coverage to offer new flexible and low-latency computing services for user equipment (UE) in the era of 5G and beyond. One of the fundamental requirements in UAV-assisted mobile wireless systems is the low latency, which can be jointly optimized with service caching and task offloading. However, this is challenged by the communication overhead involved with service caching and constrained by limited energy capacity. In this work, we present a comprehensive optimization framework with the objective of minimizing the service latency while incorporating the unique features of UAVs. Specifically, to reduce the caching overhead, we make caching placement decision every T slots (specified by service providers), and adjust UAV trajectory, user equipment or UE-UAV association, and task offloading decisions at each time slot under the constraints of UAV’s energy and resource capacity. By leveraging Lyapunov optimization approach and dependent rounding technique, we design an alternating optimization-based algorithm, named TJSO, which iteratively optimizes caching and offloading decisions. Theoretical analysis proves that TJSO converges to the near-optimal solution in polynomial time. Extensive simulations further verify that our proposed solution can significantly reduce the service delay for UEs while maintaining low energy consumption when compared to the three state-of-the-art baselines.