Dynamic Resource Trading in Sliced Mobile Networks

The success of 5G will be determined by the ability of providing cost efficient solutions while being able to serve a multitude of services with heterogeneous expectations. 'Network Slicing' provides logically separated and dedicated networks that can be customized according to the needs of individual services. Moreover, it also guarantees cost efficiency by harboring different network operators in the same network. However, the optimization of the network slices in order to handle both the heterogeneous services and the heterogeneous expectations of tenants is challenging . Therefore, we propose a dynamic network management model and analyze the effects of varying specific network parameters. The proposed framework not only dynamically adjusts the network slices but also learns the network conditions (e.g. traffic mix, achievable rates) and takes these changes into account when updating the resource allocation. Moreover, in order to guarantee timely and accurate capacity expansion, we proposed a dynamic pricing mechanism that is scaled to the tenants' expectations and the resource availability.