This paper shows how programmability can improve operators’ revenues and it presents a dynamic resource slicing policy that leads to more than one order of magnitude better resource utilization levels than convectional (static) allocation strategies.
The advent of 5th generation of mobile networks (5G) will introduce some new challenges for the transport network. Different strategies can be employed by the network providers to address these challenges with the aim to achieve an efficient utilization of network resources. The most feasible option to achieve this goal is to introduce intelligence in the transport infrastructure by designing a flexible and programmable transport network.
Network function virtualization (NFV) and dynamic resource sharing (DRS) are two possible techniques for realizing a flexible transport network. NFV allows to dynamically push network functions to different locations in the network, while DRS allows for sharing transport resources in a flexible manner. Both of these strategies can be realized by employing a programmable control framework based on software defined networking (SDN), which has implications on both the network data and control planes. However, this thesis specifically focuses on the data plane aspects of NFV and the control plane aspects of DRS.
Considering the network caching as a specific example of network function, the data plane aspects of NFV are studied in terms of different architectural options for cache placement in order to see which options are the most efficient in terms of network power consumption and cost. The results presented in this thesis show that placing large-sized caches farther in the network for a large group of users is the most efficient approach.
The control plane aspects of DRS are analyzed in terms of which provisioning strategy should be used for sharing a limited amount of transport resources. The analysis is presented for both a single-tenant case (i.e., where the role of service and network provider is played by the same entity), and a multi-tenant case (i.e., where a network provider manages the resources assigned to different service providers in an intelligent way). The results show that DRS performs much better than the conventional static approach (i.e., without sharing of resources), which translates into significant cost savings for the network providers.