计算机科学
移动边缘计算
Lyapunov优化
水准点(测量)
最优化问题
数学优化
理论(学习稳定性)
实时计算
GSM演进的增强数据速率
算法
数学
李雅普诺夫方程
李雅普诺夫指数
人工智能
大地测量学
机器学习
混乱的
地理
作者
Zheyuan Yang,Suzhi Bi,Ying Jun Zhang
标识
DOI:10.1109/twc.2022.3184953
摘要
Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has recently emerged as a cost-effective solution to provide computation service to distributed devices in the absence of terrestrial infrastructure. In this paper, we consider a UAV-enabled MEC system serving multiple energy harvesting (EH) devices, where the energy and task data arrive at the users stochastically. Without any future knowledge of task data and energy arrivals, our objective is to design an online algorithm to jointly optimize the UAV energy and task processing rate, meanwhile satisfying the long-term data queue stability. We formulate the problem as a multi-stage stochastic programming and propose an online algorithm, named PLOT, based on perturbed Lyapunov optimization technique. In particular, PLOT resolves the coupling effect of sequential control actions, and converts the stochastic problem into per-slot deterministic optimization problem. For each per-slot problem, we design a low-complexity algorithm to solve it. We show that the PLOT algorithm can derive a feasible solution to the original problem and achieve an $[O(1/V),O(V)]$ trade-off between the system cost and the data queue length. Simulation results justify our analysis and demonstrate that the PLOT algorithm achieves better performance in terms of system utility and maintains queue stability that is not achieved by other benchmark methods.
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