  5G networks will provide the platform for deploying large number of
tenant-associated management, control and end-user applications having
different resource requirements at the infrastructure level. In this context,
the 5G infrastructure provider must optimize the infrastructure resource
utilization and increase its revenue by intelligently admitting network slices
that bring the most revenue to the system. In addition, it must ensure that
resources can be scaled dynamically for the deployed slices when there is a
demand for them from the deployed slices. In this paper, we present a neural
networks-driven policy agent for network slice admission that learns the
characteristics of the slices deployed by the network tenants from their
resource requirements profile and balances the costs and benefits of slice
admission against resource management and orchestration costs. The policy agent
learns to admit the most profitable slices in the network while ensuring their
resource demands can be scaled elastically. We present the system model, the
policy agent architecture and results from simulation study showing an
increased revenue for infra-structure provider compared to other relevant slice
admission strategies.
