Network slicing is a key enabler to successfully support 5G services with specific requirements and priorities. Due to the diversity of these services, slice deployment and orchestration are essential to guarantee service performance in a cost-effective way. Here, we propose an Artificial Intelligence framework for cross-slice admission and congestion control that simultaneously considers communication, computing, and storage resources to maximize resources utilization and operator revenue. First, we propose a smart feature extraction solution to analyze the characteristics of incoming requests together with the already deployed slices, and then automatically evaluates the request requirements to make appropriate decisions. Second, we design an online algorithm that controls the slice admission based on their priorities, the arrival and departure characteristics, and the available resources. To mitigate system overloading, our framework dynamically adjusts resources allocated to low priority slices, thereby reducing the dropping probability of new slice requests. The proposed algorithm offers outstanding advantages over traditional static approaches by automatically adapting the controller decisions to the system changes. Simulation results show that our framework significantly improves the resource utilization and reduces the slice request dropping probabilities up to 44% as compared to the baseline schemes.

中文翻译：

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用于 5G 网络中切片部署和编排的人工智能框架

网络切片是成功支持具有特定要求和优先级的 5G 服务的关键推动因素。由于这些服务的多样性，切片部署和编排对于以经济高效的方式保证服务性能至关重要。在这里，我们提出了一个跨切片准入和拥塞控制的人工智能框架，它同时考虑了通信、计算和存储资源，以最大限度地提高资源利用率和运营商收入。首先，我们提出了一种智能特征提取解决方案，将传入请求的特征与已部署的切片一起分析，然后自动评估请求要求以做出适当的决策。其次，我们设计了一个在线算法，根据他们的优先级来控制切片准入，到达和离开特征，以及可用资源。为了减轻系统过载，我们的框架动态调整分配给低优先级切片的资源，从而降低新切片请求的丢弃概率。通过自动使控制器决策适应系统变化，所提出的算法提供了优于传统静态方法的显着优势。仿真结果表明，与基线方案相比，我们的框架显着提高了资源利用率，并将切片请求丢弃概率降低了 44%。通过自动使控制器决策适应系统变化，所提出的算法提供了优于传统静态方法的显着优势。仿真结果表明，与基线方案相比，我们的框架显着提高了资源利用率，并将切片请求丢弃概率降低了 44%。通过自动使控制器决策适应系统变化，所提出的算法提供了优于传统静态方法的显着优势。仿真结果表明，与基线方案相比，我们的框架显着提高了资源利用率，并将切片请求丢弃概率降低了 44%。