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

Tenant-Aware Slice Admission Control using Neural Networks-Based Policy Agent