Abstract
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.
Highlights
The evolving fifth Generation of Mobile Communications System (5G) communications are envisioned to be classified into three categories [1]–[3]: enhanced Mobile Broad Band to deliver gigabytes of bandwidth to mobile devices on-demand, massive Machine Type Communications to connect sensors and machines, and Ultra Reliable and Low Latency Communications (URLLC) targeted to low latency and reliable applications like autonomous driving
Compared to the multiplebearer network architecture defined in 3GPP [14], where User Equipments (UE) needs to monitor and select an optimal radio bearer for its packet based on the quality of service (QoS) classes, the merit of our single radio bearer architecture can eliminate the workload of UE side and save its battery life
In this subsection, we propose that the packets should be diverted to different Evolved Packet Core (EPC) user planes and be able to be assigned to different Radio Access Network (RAN) slices with different radio resource blocks (RBs) and spectrum scheduling algorithms
Summary
Multi-Access Edge Computing (MEC) has been considered as an indispensable component for the 5G networks. It brings the applications from the centralized data centers to the network edge that is close to Radio Access Network (RAN) and User Equipments (UE). As long as data has been transmitted from UEs into a mobile network, the contextual information of the data (e.g., which application the data belongs to and which device the data generated from) is hidden from the network alliances. Packet header marking fails to identify a broad scope of applications while DPI is becoming harder and harder due to that applicationspecific information conveyed in the payload is most likely encrypted
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