A Distributed Intelligence Architecture for B5G Network Automation
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| _version_ | 1866917816594071552 |
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| author | Majumdar, Sayantini Trivisonno, Riccardo Carle, Georg |
| author_facet | Majumdar, Sayantini Trivisonno, Riccardo Carle, Georg |
| contents | The management of networks is automated by closed loops. Concurrent closed loops aiming for individual optimization cause conflicts which, left unresolved, leads to significant degradation in performance indicators, resulting in sub-optimal network performance. Centralized optimization avoids conflicts, but impractical in large-scale networks for time-critical applications. Distributed, pervasive intelligence is therefore envisaged in the evolution to B5G networks. In this letter, we propose a Q-Learning-based distributed architecture (QLC), addressing the conflict issue by encouraging cooperation among intelligent agents. We design a realistic B5G network slice auto-scaling model and validate the performance of QLC via simulations, justifying further research in this direction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2107_13268 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | A Distributed Intelligence Architecture for B5G Network Automation Majumdar, Sayantini Trivisonno, Riccardo Carle, Georg Networking and Internet Architecture Machine Learning The management of networks is automated by closed loops. Concurrent closed loops aiming for individual optimization cause conflicts which, left unresolved, leads to significant degradation in performance indicators, resulting in sub-optimal network performance. Centralized optimization avoids conflicts, but impractical in large-scale networks for time-critical applications. Distributed, pervasive intelligence is therefore envisaged in the evolution to B5G networks. In this letter, we propose a Q-Learning-based distributed architecture (QLC), addressing the conflict issue by encouraging cooperation among intelligent agents. We design a realistic B5G network slice auto-scaling model and validate the performance of QLC via simulations, justifying further research in this direction. |
| title | A Distributed Intelligence Architecture for B5G Network Automation |
| topic | Networking and Internet Architecture Machine Learning |
| url | https://arxiv.org/abs/2107.13268 |