# Effects of Transport Network Slicing on 5G Applications
###### Abstract
The research in [3] describes how 5G E2E network slicing is orchestrated and managed, and proposes a RAN slicing model for 5G services. The work in [4] presents an E2E network slicing testbed based on open-source tools capable of supporting slice provision dynamicity, real-time monitoring of virtual machines, and Virtual Network Function (VNF) instantiation. In [5], the authors introduce a Software-Defined Networking (SDN)-enabled 5G experimental platform that focuses on the RAN and the core network. The study [6] also focuses on RAN slicing and proposes an SDN-based scheduler to enable the dynamic slicing of radio resources among IoT and enhanced Mobile Broadband (eMBB) applications. The proposed solution emulates a Cloud Radio Access Network (C-RAN) scenario by using the OpenAirInterface (OAI) platform [7] and the FlexRAN SDN Controller [8]; then measures the efficiency of the proposed solution through emulations. In the study [9], the  
Figure 1: 5G Service-Based Architecture (SBA). The control-plane network functions that evolved from the 4G core network are decomposed into several microservices that interact with each other through Service-based interfaces. authors introduce a mathematical model based on combinatorial designs for E2E network slicing. In the simulation results, the optimal strategy for the Network Slice Management Function (NSMF) can not only maximize the utilization of the network components but also decrease the average delay time of slice instantiation, configuration, and activation. Transport networks provide connectivity between disaggregated RAN components and the core network. The RAN disaggregation and emerging service requirements drive the need for a flexible architecture for transport networks. Thus, the International Telecommunication Union Telecommunication Standardization Sector (ITU-T) introduces a reference architecture for SDN control of transport networks [10]. With SDN's flexibility, network slicing in transport networks can differentiate or isolate traffic flows, dedicate resources to a network slice instance, and enforce slice-specific requirements. The study [11] leverages SDN technologies to learn a global view of network resource allocation and then calculates forwarding paths accordingly. Nonetheless, it comes with significant control plane overhead for scheduling while network complexity grows, and may fail to optimize resource utilization with path allocation. Researchers [12; 13; 14] utilize data plane programmability to develop Quality of Service (QoS) management solutions using Programming Protocol-Independent Packet Processors (P4) switches. The study [12] proposes a customized P4 pipeline for strict bandwidth limitation and UDP stream guarantees. The earlier study [13] found that the P4 meters in commodity P4 switches can only reach a 10% target rate for TCP streams. As such, they proposed TCP-friendly meters that can achieve up to 85% in target rate. The work in [14] utilizes the P4 NetFPGA reference implementation to develop a QoS-aware data plane for slicing that classifies traffic flows and schedules them to multi-level priority queues, but the study only considers the effects of network slicing on media-rich applications. Most of the studies mentioned above focus on the orchestration and management of either E2E or RAN network slicing while barely addressing transport network slicing. Moreover, none of these studies measure the effects of network slicing on a broad range of applications. By contrast, this article utilizes real applications and their emulated counterparts to generate traffic flows and conduct empirical evaluations in a hardware testbed, which is more realistic than the simulation approach. To investigate 5G network slicing performances, we have deployed several smart applications to provide traffic flows to an SDN-based 5G testbed at National Chiao Tung University (NCTU). However, it is challenging to generate actual IoT application traffic on a large scale, which demands massive deployment of IoT devices of various types.