A Two Time-Scale Joint Optimization Approach for UAV-assisted MEC
Zemin Sun, Geng Sun, Long He, Fang Mei, Shuang Liang, Yanheng Liu
TL;DR
This paper tackles efficient computation offloading and UAV trajectory control in a UAV-assisted MEC system with heterogeneous edge resources and multi-time-scale dynamics. It introduces a two-time-scale framework, TJCCT, within an SDN-controlled hierarchy, combining price-incentive resource trading, many-to-one matching for offloading, and convex optimization for UAV trajectories to maximize a system utility $U^t$ over decisions $(f_{j,i}^t,p_{j,i}^t)$, offloading $\mathbf{O}^t$, and positions $\mathbf{Q}^t$. The authors prove stability, establish optimality properties (weak Pareto for offloading and convexity-based trajectory optimality), and show polynomial-time complexity. Simulations demonstrate that TJCCT outperforms several benchmarks in system utility, MD QoE, and MEC revenue under varying time and workload conditions. The proposed approach offers a scalable, near-optimal framework for real-time MEC service provisioning in dynamic aerial-terrestrial networks by effectively coordinating computation, communication, and mobility decisions.
Abstract
Unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) is emerging as a promising paradigm to provide aerial-terrestrial computing services close to mobile devices (MDs). However, meeting the demands of computation-intensive and delay-sensitive tasks for MDs poses several challenges, including the demand-supply contradiction between MDs and MEC servers, the demand-supply heterogeneity between MDs and MEC servers, the trajectory control requirements on energy efficiency and timeliness, and the different time-scale dynamics of the network. To address these issues, we first present a hierarchical architecture by incorporating terrestrial-aerial computing capabilities and leveraging UAV flexibility. Furthermore, we formulate a joint computing resource allocation, computation offloading, and trajectory control problem to maximize the system utility. Since the problem is a non-convex mixed integer nonlinear programming (MINLP), we propose a two time-scale joint computing resource allocation, computation offloading, and trajectory control (TJCCT) approach. In the short time scale, we propose a price-incentive method for on-demand computing resource allocation and a matching mechanism-based method for computation offloading. In the long time scale, we propose a convex optimization-based method for UAV trajectory control. Besides, we prove the stability, optimality, and polynomial complexity of TJCCT. Simulation results demonstrate that TJCCT outperforms the comparative algorithms in terms of the utility of the system, the QoE of MDs, and the revenue of MEC servers.