In this paper, we present an application-specific Multi-Access Edge Computing (MEC) network architecture by leveraging the Control and User Plane Separation (CUPS) in mobile core networks to offload data processing from central servers to edge servers to reduce the transmitted traffic volume and also the response latency of connected vehicle mobility service. We first apply deep learning to classify packets of different applications to different Radio Access Networks (RAN) slices for application-specific spectrum scheduling. Then, we slice Evolved Packet Core (EPC) and deploy EPC data plane slices on-demand for each application and route packets from RAN slices to edge servers. By applying network slicing, multiple RAN, EPC and MEC slices that support different categories of services with different quality of service (QoS) requirements can be deployed in the same physical infrastructure. We prototype the proposed application-specific CUPS architecture using modified open source software OpenAirInterface on our deeply programmable platform. The preliminary experimental results show the feasibility and efficiency of proposed application-specific CUPS architecture, which can achieve a significant decrease in transmission data volume and latency.

中文翻译：

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车联网边缘计算智能网络切片


在本文中，我们提出了一种特定于应用程序的多接入边缘计算（MEC）网络架构，利用移动核心网络中的控制和用户平面分离（CUPS）将数据处理从中央服务器卸载到边缘服务器，以减少传输流量量以及互联车辆移动服务的响应延迟。我们首先应用深度学习将不同应用的数据包分类到不同的无线接入网络（RAN）切片，以进行特定于应用的频谱调度。然后，我们对演进分组核心 (EPC) 进行切片，并为每个应用程序按需部署 EPC 数据平面切片，并将数据包从 RAN 切片路由到边缘服务器。通过应用网络切片，可以在同一物理基础设施中部署支持具有不同服务质量（QoS）要求的不同类别服务的多个RAN、EPC和MEC切片。我们在我们的深度可编程平台上使用修改后的开源软件 OpenAirInterface 对所提出的特定于应用程序的 CUPS 架构进行原型设计。初步实验结果表明了所提出的专用CUPS架构的可行性和效率，可以实现传输数据量和延迟的显着降低。