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SDN enhancements for the sliced, deep programmable 5G core

: Eichhorn, Fabian; Corici, Marius; Magedanz, Thomas; Du, Ping; Kiriha, Yoshiaki; Nakao, Akihiro


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Communications Society; IEEE Computer Society:
13th International Conference on Network and Service Management, CNSM 2017 : Workshop on Internet Charging and QoS Technologies (ICQT 2017), Tokyo, Japan, November 26 - 30, 2017
Piscataway, NJ: IEEE, 2017
ISBN: 978-3-901882-98-2
ISBN: 978-1-5386-2153-0
International Conference on Network and Service Management (CNSM) <13, 2017, Tokyo>
Workshop on Internet Charging and QoS Technologies (ICQT) <2017, Tokyo>
Fraunhofer FOKUS ()
SDN; NFV; 5G Mobile Networks

Standardisation, research and development efforts for the fifth generation (5G) of mobile telecommunication net- works are well under way. Software Defined Networking (SDN) and Network Function Virtualisation (NFV) are two of the key enabling technologies, considered in these efforts. The need for a flexible, high performant and efficient architecture is well established. Network slicing, which combines SDN and NFV, can contribute to such an architecture. It allows the parallel deployment of differing network stacks on top of any physical infrastructure. SDN's separation of control and data plane components allows for flexible deployments. How can SDN's flexibility be leveraged in a sliced, 5G network infrastructure? There needs to be an efficient way to integrate SDN into 5G networks. In this paper, we posit a way of integration,which allows decoupling the data plane components from any particular control plane. This can improve flexibility, utilisation and extensibility. We envision utilising an SDN switch implementation as the User Plane Function (UPF), and introducing an SDN controller between Session Management Function (SMF) and UPF, to effectively decouple the two. Based on this decoupling, control and data plane components can be deployed in separately and new slice orchestration opportunities can be developed. Furthermore, we can leverage deep data plane programmability, to enhance the system in terms of function and flexibility.