Applying NFV and SDN to LTE mobile core gateways, the functions placement problem

TLDR

Mobile operators face growing data demands and cost pressures, and NFV and SDN are emerging technologies that promise scalable, flexible, and cost‑efficient network architectures, though their impact on network load and data‑plane delay remains underexplored. The study introduces the functions placement problem, aiming to determine optimal combinations of NFV and SDN across a mobile network to minimize transport load while respecting delay, datacenter count, and SDN control overhead constraints. A mathematical model is proposed that assigns functions to NFV or SDN deployments based on load and delay, and its effectiveness is demonstrated through a concrete use‑case example.

Abstract

With the rapid growth of user data, service innovation, and the persistent necessity to reduce costs, today's mobile operators are faced with severe challenges. In networking, two new concepts have emerged aiming at cost reduction, increase of network scalability and service flexibility, namely Network Functions Virtualization (NFV) and Software Defined Networking (SDN). NFV proposes to run the mobile network functions as software instances on commodity servers or datacenters (DC), while SDN supports a decomposition of the mobile network into control-plane and data-plane functions. Whereas these new concepts are considered as very promising drivers to design cost efficient mobile network architectures, limited attention has been drawn to the network load and infringed data-plane delay imposed by introducing NFV or SDN. We argue that within a widely-spanned mobile network, there is in fact a high potential to combine both concepts. Taking load and delay into account, there will be areas of the mobile network rather benefiting from an NFV deployment with all functions virtualized, while for other areas, an SDN deployment with functions decomposition is more advantageous. We refer to this problem as the functions placement problem. We propose a model that resolves the functions placement and aims at minimizing the transport network load overhead against several parameters such as data-plane delay, number of potential datacenters and SDN control overhead. We illustrate our proposed concept along with a concrete use case example.