Joint UAV Trajectory Planning, DAG Task Scheduling, and Service Function Deployment Based on DRL in UAV-Empowered Edge Computing
Joint UAV Trajectory Planning, DAG Task Scheduling, and Service Function Deployment Based on DRL in UAV-Empowered Edge Computing
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无人机边缘计算中基于DRL的无人机轨迹规划、DAG任务调度和服务功能部署
DOI：
10.1109/jiot.2023.3257291
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发表时间：
2023-07
影响因子：
10.6
通讯作者：
Xianglin Wei;Lingfeng Cai;Nan Wei;P. Zou;Jin Zhang;S. Subramaniam
中科院分区：
文献类型：
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作者：
Xianglin Wei;Lingfeng Cai;Nan Wei;P. Zou;Jin Zhang;S. Subramaniam
无人机（UAV）授权的边缘计算在无障碍场景中得到了广泛的研究，其中移动的UAV负责处理来自地面上的移动的设备的卸载的单件任务。然而，很少有人关注的情况下，无人机服务于一个复杂的区域，多个障碍物和依赖的任务。相关任务可以被公式化为包含多个子任务的有向非循环图（DAG）;并且每个子任务可以由部署在UAV上的对应服务功能（SF）执行。在此背景下，本文将联合无人机航迹规划、DAG任务调度和SF部署问题转化为一个优化问题。然后，提出了一种基于深度强化学习（DRL）的算法来解决NP难问题。代理的状态空间、动作空间和奖励函数，即，无人机分别在DRL框架下定义。为了评估该建议的有效性，进行了一系列的实验与不同的参数设置。实验结果表明，基于DRL的算法在轨迹规划成功率、执行任务数和平均任务响应延迟等方面均优于三种启发式算法.
Unmanned aerial vehicle (UAV)-empowered edge computing has been widely investigated in obstacle-free scenarios, where a moving UAV is in charge of handling offloaded singleton tasks from mobile devices on the ground. However, little attention has been paid to the scenario, in which the UAV serves a complex area with multiple obstacles and dependent tasks. A dependent task can be formulated as a directed acyclic graph (DAG) that contains a number of subtasks; and each subtask can be executed by a corresponding service function (SF) deployed on the UAV. In this backdrop, the joint UAV trajectory planning, DAG task scheduling, and SF deployment is formulated as an optimization problem in this article. Afterwards, a deep reinforcement learning (DRL)-based algorithm is presented to tackle the NP-hard problem. The state space, action space, and the reward function of the agent, i.e., the UAV, are defined, respectively, under the DRL framework. To evaluate the effectiveness of the proposal, a series of experiments is conducted with different parameter settings. Results show that the DRL-based algorithm performs much better than three heuristic algorithms in success rate of trajectory planning, the number of executed tasks, and the average task response latency.