Q-Learning for Conflict Resolution in B5G Network Automation
- URL: http://arxiv.org/abs/2107.13268v1
- Date: Wed, 28 Jul 2021 11:00:16 GMT
- Title: Q-Learning for Conflict Resolution in B5G Network Automation
- Authors: Sayantini Majumdar and Riccardo Trivisonno and Georg Carle
- Abstract summary: This work explores pervasive intelligence for conflict resolution in network automation, as an alternative to centralized orchestration.
A Q-Learning decentralized approach to network automation is proposed, and an application to network slice auto-scaling is designed and evaluated.
- Score: 0.9625436987364907
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Network automation is gaining significant attention in the development of B5G
networks, primarily for reducing operational complexity, expenditures and
improving network efficiency. Concurrently operating closed loops aiming for
individual optimization targets may cause conflicts which, left unresolved,
would lead to significant degradation in network Key Performance Indicators
(KPIs), thereby resulting in sub-optimal network performance. Centralized
coordination, albeit optimal, is impractical in large scale networks and for
time-critical applications. Decentralized approaches are therefore envisaged in
the evolution to B5G and subsequently, 6G networks. This work explores
pervasive intelligence for conflict resolution in network automation, as an
alternative to centralized orchestration. A Q-Learning decentralized approach
to network automation is proposed, and an application to network slice
auto-scaling is designed and evaluated. Preliminary results highlight the
potential of the proposed scheme and justify further research work in this
direction.
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