Intelligent Routing Optimization Model For QoS-aware Software-Defined Networks Using Deep Reinforcement Learning
| dc.contributor.author | Mohamed Akram DJERRAR | |
| dc.contributor.author | Zineddine BOURAHLA | |
| dc.contributor.author | Sidahmed LACHENANI (Encadreur) | |
| dc.contributor.author | Hamza KHEDDAR (Co-Encadreur) | |
| dc.date.accessioned | 2026-02-03T12:30:07Z | |
| dc.date.available | 2026-02-03T12:30:07Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | This thesis presents an intelligent routing model for Software-Defined Networks (SDNs), taking into account Quality of Service (QoS) using Deep Reinforcement Learning (DRL). The agent, based on a DQN network, selects the best paths according to real-time network conditions. Results show significant improvement over the Dijkstra algorithm: up to 60% reduction in delay, 50% less jitter, and a 207% increase in throughput. The model is flexible, scalable, and suitable for data centers, IoT, and 5G networks. | |
| dc.identifier.uri | https://dspace.univ-medea.dz/handle/123456789/14109 | |
| dc.language.iso | en | |
| dc.relation.ispartofseries | M25 107; M125 107 | |
| dc.title | Intelligent Routing Optimization Model For QoS-aware Software-Defined Networks Using Deep Reinforcement Learning | |
| dc.type | Thesis |