基于深度强化学习的5G电力虚拟专用光网络切片时延优化算法研究

Research on Delay Optimization Algorithm in 5G Power Virtual Private Network Slicing Based on Deep Reinforcement Learning

摘要:

考虑到5G电力虚拟专用光网络切片中业务端到端时延较大，以及仿真中网元状态同步时延较大导致仿真策略不准确的问题，提出一种基于深度强化学习的5G电力虚拟专用光网络切片中时延优化算法。首先建立5G电力虚拟专用光网络切片系统模型，该模型包括一个用于网元状态更新的同步节点和其余业务节点，其中同步节点通过专用特种光纤与软件定义网络控制器直接连接。在此基础上，在保证业务服务质量的同时，提出一个最小化包括业务时延和网元同步时延的优化问题。由于该优化问题变量即有离散变量又有连续变量，因此分别使用离散和连续的深度强化学习算法求解。仿真结果表明，所提算法能够有效降低电力虚拟专用光网络切片网络时延，满足服务质量要求的同时有效保证仿真策略的实时性。

Abstract:

Aiming at the issue of high end-to-end latency and the inaccurate of simulation strategy because of the large synchronization delay in 5G power virtual private optical network slicing, a latency optimization algorithm based on deep reinforcement learning is proposed. Firstly, a 5G power virtual private optical network slicing system model is established, which includes a synchronization node for network element updates and other service nodes, where the synchronization node is directly connected to the software defined network controller through a dedicated special fiber. Then, while ensuring the quality of service, an optimization problem is proposed to minimize the total latency, including business and network element update latency. As the optimization problem involves both discrete and continuous variables, both discrete and continuous deep reinforcement learning algorithms were employed for solution. Simulation results show that the proposed algorithm can effectively reduce the latency of the power virtual private optical network slicing network, meet the requirements of service quality, and effectively ensure the real-time performance of the simulation strategy.