论文标题
IIOT自动网络的两次计算资源分配
Two-timescale Resource Allocation for Automated Networks in IIoT
论文作者
论文摘要
细胞技术的快速技术进步将彻底改变工业互联网（IIOT）的网络自动化。在本文中，我们研究了与混合能源供应的IIT网络中的两次计算资源分配问题，其中能量收集的时间变化（EH），电价，渠道状态和数据到达具有不同的粒度。公式的问题包括在大时刻表上的能源管理，以及在小时尺度上的速率控制，渠道选择和电力分配。为了应对这一挑战，我们开发了一种在线解决方案，以保证仅通过因果信息提供有限的性能偏差。具体而言，利用Lyapunov优化将长期随机优化问题转变为一系列短期确定性优化问题。然后，基于乘数的交替方向方法（ADMM）开发了低复杂性率控制算法，该方法通过分解协调方法加速了收敛速度。接下来，将联合渠道的选择和功率分配问题转变为一对一的匹配问题，并通过提议的基于价格的配额限制来解决。最后，通过在各种系统配置下的模拟来验证所提出的算法。
The rapid technological advances of cellular technologies will revolutionize network automation in industrial internet of things (IIoT). In this paper, we investigate the two-timescale resource allocation problem in IIoT networks with hybrid energy supply, where temporal variations of energy harvesting (EH), electricity price, channel state, and data arrival exhibit different granularity. The formulated problem consists of energy management at a large timescale, as well as rate control, channel selection, and power allocation at a small timescale. To address this challenge, we develop an online solution to guarantee bounded performance deviation with only causal information. Specifically, Lyapunov optimization is leveraged to transform the long-term stochastic optimization problem into a series of short-term deterministic optimization problems. Then, a low-complexity rate control algorithm is developed based on alternating direction method of multipliers (ADMM), which accelerates the convergence speed via the decomposition-coordination approach. Next, the joint channel selection and power allocation problem is transformed into a one-to-many matching problem, and solved by the proposed price-based matching with quota restriction. Finally, the proposed algorithm is verified through simulations under various system configurations.
