Tenant-Aware Slice Admission Control using Neural Networks-Based Policy Agent release_wbfxtqqombgklgsg5ocnody3de

by Pedro Batista, Shah Nawaz Khan, Peter Öhlén, Aldebaro Klautau

Released as a article .

2019  

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.
In text/plain format

Archived Files and Locations

application/pdf  232.9 kB
file_neovh3jio5h4jk6qrss422m32q
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2019-08-20
Version   v1
Language   en ?
arXiv  1908.07494v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 095af610-3281-4575-9748-b8160caa18db
API URL: JSON