Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.13806 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916927164645376 |
|---|---|
| author | Torres, José Eduardo Zerna Avgeris, Marios Papagianni, Chrysa Pongrácz, Gergely Gódor, István Grosso, Paola |
| author_facet | Torres, José Eduardo Zerna Avgeris, Marios Papagianni, Chrysa Pongrácz, Gergely Gódor, István Grosso, Paola |
| contents | This work presents a proof-of-concept implementation of a distributed, in-network reinforcement learning (IN-RL) framework for adaptive path selection in programmable networks. By combining Stochastic Learning Automata (SLA) with real-time telemetry data collected via In-Band Network Telemetry (INT), the proposed system enables local, data-driven forwarding decisions that adapt dynamically to congestion conditions. The system is evaluated on a Mininet-based testbed using P4-programmable BMv2 switches, demonstrating how our SLA-based mechanism converges to effective path selections and adapts to shifting network conditions at line rate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13806 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning-based Adaptive Path Selection for Programmable Networks Torres, José Eduardo Zerna Avgeris, Marios Papagianni, Chrysa Pongrácz, Gergely Gódor, István Grosso, Paola Machine Learning This work presents a proof-of-concept implementation of a distributed, in-network reinforcement learning (IN-RL) framework for adaptive path selection in programmable networks. By combining Stochastic Learning Automata (SLA) with real-time telemetry data collected via In-Band Network Telemetry (INT), the proposed system enables local, data-driven forwarding decisions that adapt dynamically to congestion conditions. The system is evaluated on a Mininet-based testbed using P4-programmable BMv2 switches, demonstrating how our SLA-based mechanism converges to effective path selections and adapts to shifting network conditions at line rate. |
| title | Reinforcement Learning-based Adaptive Path Selection for Programmable Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.13806 |