As networks advance toward the Sixth Generation (6G), management of high-speed and ubiquitous connectivity poses major challenges in meeting diverse Service Level Agreements (SLAs). The Zero Touch Network (ZTN) framework has been proposed to automate and optimize network management tasks. It ensures SLAs are met effectively even during dynamic network conditions. Though, ZTN literature proposes closed-loop control, methods for implementing such a mechanism remain largely unexplored. This paper proposes a novel two-stage closedloop control for ZTN to optimize the network continuously. First, an XGBoosted Bidirectional Long Short Term Memory (BiLSTM) model is trained to predict the network state (in terms of bandwidth). In the second stage, the Q-learning algorithm selects actions based on the predicted network state to optimize Quality of Service (QoS) parameters. By selecting appropriate actions, it serves the applications perpetually within the available resource limits in a closed loop. Considering the scenario of network congestion, with available bandwidth as state and traffic shaping options as an action for mitigation, results show that the proposed closed-loop mechanism can adjust to changing network conditions. Simulation results show that the proposed mechanism achieves 95% accuracy in matching the actual network state by selecting the appropriate action based on the predicted state.

翻译：随着网络向第六代（6G）演进，高速泛在连接的管理在满足多样化服务等级协议（SLA）方面面临重大挑战。零接触网络（ZTN）框架被提出以实现网络管理任务的自动化与优化，确保即使在动态网络条件下也能有效满足SLA要求。尽管ZTN相关文献提出了闭环控制理念，但实现此类控制机制的具体方法仍鲜有探索。本文提出一种用于ZTN的新型两阶段闭环控制机制，以实现网络的持续优化。首先，训练一个基于XGBoost增强的双向长短期记忆（BiLSTM）模型来预测网络状态（以带宽为指标）。在第二阶段，Q-learning算法根据预测的网络状态选择动作，以优化服务质量（QoS）参数。通过选择恰当的动作，该机制能够在可用资源限制内以闭环形式持续服务于各类应用。以网络拥塞场景为例，将可用带宽作为状态、流量整形选项作为缓解拥塞的动作，实验结果表明所提出的闭环机制能够适应变化的网络条件。仿真结果显示，该机制通过基于预测状态选择合适动作，在匹配实际网络状态方面达到了95%的准确率。