Hierarchical Placement Learning for Network Slice Provisioning
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866913980711174144 |
|---|---|
| author | Ajayi, Jesutofunmi Di Maio, Antonio Braun, Torsten |
| author_facet | Ajayi, Jesutofunmi Di Maio, Antonio Braun, Torsten |
| contents | In this work, we aim to address the challenge of slice provisioning in edge-based mobile networks. We propose a solution that learns a service function chain placement policy for Network Slice Requests, to maximize the request acceptance rate, while minimizing the average node resource utilization. To do this, we consider a Hierarchical Multi-Armed Bandit problem and propose a two-level hierarchical bandit solution which aims to learn a scalable placement policy that optimizes the stated objectives in an online manner. Simulations on two real network topologies show that our proposed approach achieves 5% average node resource utilization while admitting over 25% more slice requests in certain scenarios, compared to baseline methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06432 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical Placement Learning for Network Slice Provisioning Ajayi, Jesutofunmi Di Maio, Antonio Braun, Torsten Networking and Internet Architecture In this work, we aim to address the challenge of slice provisioning in edge-based mobile networks. We propose a solution that learns a service function chain placement policy for Network Slice Requests, to maximize the request acceptance rate, while minimizing the average node resource utilization. To do this, we consider a Hierarchical Multi-Armed Bandit problem and propose a two-level hierarchical bandit solution which aims to learn a scalable placement policy that optimizes the stated objectives in an online manner. Simulations on two real network topologies show that our proposed approach achieves 5% average node resource utilization while admitting over 25% more slice requests in certain scenarios, compared to baseline methods. |
| title | Hierarchical Placement Learning for Network Slice Provisioning |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2508.06432 |