论文标题
在工业互联网上，数据年龄的无线移动边缘计算时间表
Data Age Aware Scheduling for Wireless Powered Mobile-Edge Computing in Industrial Internet of Things
论文作者
论文摘要
无线电动移动边缘计算被认为是一种有希望的范式，以增强工业互联网中低功率无线设备的计算能力。在这种情况下，由于随机流量到达，偶联上行链路/下行链路决策和不完整的系统状态知识，在这种情况下设计有效的资源调度方法对于设计至关重要。为了应对这些挑战，在本文中提出了一种在线优化算法，以最大程度地提高长期系统实用性平衡吞吐量和公平性，但要遵守数据年龄和稳定性约束。一组虚拟队列旨在改变调度任务，由于时间相关的数据年龄限制，很难解决该任务，将其转变为随机优化问题。利用Lyapunov和凸优化技术，提出的方法可以在没有任何事先统计知识的情况下实现渐近的近乎最佳的在线决策，并在存在部分和过时的网络状态信息的情况下保持渐近最佳性。数值模拟证实了理论分析并证明了所提出的方法的有效性。
Wireless powered mobile edge computing has been envisioned as a promising paradigm to enhance the computation capability of low-power wireless devices in Industrial Internet of Things. An efficient resource scheduling method is critical yet challenging to design in such a scenario due to stochastic traffic arrival, time-coupling uplink/downlink decision and incomplete system state knowledge. To tackle these challenges, an online optimization algorithm is proposed in this paper to maximize long-term system utility balancing throughput and fairness, subject to data age and stability constraints. A set of virtual queues is designed to transform the scheduling task, which is hard to solve due to time-dependent data age constraints, into a stochastic optimization problem. Leveraging Lyapunov and convex optimization techniques, the proposed approach can achieve asymptotically near-optimal online decisions without any prior statistical knowledge, and maintain the asymptotic optimality in the presence of partial and outdated network state information. Numerical simulations corroborate the theoretical analysis and demonstrate the effectiveness of the proposed approach.
