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

TL;DR

This paper introduces a neural network-based policy agent for 5G network slice admission that optimizes resource utilization and revenue by learning tenant slice characteristics and balancing costs and benefits.

Contribution

It presents a novel neural network-driven policy for slice admission that adapts to tenant requirements and enhances infrastructure provider revenue.

Findings

Increased revenue compared to other strategies

Effective learning of tenant slice characteristics

Elastic resource scaling support

Abstract

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.