无人机辅助铁路边缘计算网络动态计算任务卸载策略

Unloading Strategy of Dynamic Computing Tasks in UAV-Assisted Railway Edge Computing Network

在无人机辅助铁路移动边缘计算(moving edge computing,MEC)系统下,以服务时延和任务失败的执行成本为性能指标,同时保证无人机能耗稳定性和计算时延约束要求,提出了一种有效的计算任务卸载和功率控制方法。利用Lyapunov优化理论将基于长期性能指标的优化问题转化为多个时隙的子问题,提出了一种基于域自适应学习的动态计算卸载算法,对每个时隙内的计算任务进行卸载决策。同时在每个时隙内,当卸载决策决定计算任务在本地设备上执行时,所提算法还需联合优化得出业务数据下载的传输功率。实验结果表明,所提算法能够有效缩短服务时延、提高任务完成效率。

In the UAV-assisted railway moving edge computing(MEC)system,an effective method for computing task unloading and power control is proposed with service delay and task failure execution cost as the performance index,while ensuring the stability of energy consumption of UAVs and the requirement of computing delay constraints.With the Lyapunov optimization theory,the optimization problem based on long-term performance index is transformed into the sub-problems of multiple time slots.A dynamic computational unloading algorithm based on domain adaptive learning is proposed to make unloading decisions for computing tasks in each time slot.Meanwhile,in each time slot,the proposed algorithm also optimizes the transmission power of the service data download when the unloading decision determines that the computing task is executed on the local device.The experimental results show that the proposed algorithm can effectively reduce the service delay and improve the efficiency of task completion.