@inproceedings{9978a3a05d714adc8b2c00e9749d36f2,
title = "DL-ViNE: Reinforcement Learning Algorithm for Efficient Virtual Network Embedding Under Direct-Link Constraints",
abstract = "The Fifth and Sixth Generation (5G/6G) networks aim to support diverse applications with specific QoS and resource needs. Network Slicing has emerged as a key paradigm to meet these demands by creating multiple Virtual Networks (VNs) over shared physical infrastructure. This process, known as Virtual Network Embedding (VNE), maps virtual nodes and links to physical resources. With Kubernetes becoming the dominant orchestration platform, most infrastructures now rely on Kubernetes clusters, which enforce direct pod-to-pod communication, necessitating a direct-link approach to VNE. However, most existing methods focus on path-based link mapping. In this paper, we present DL-ViNE, a Reinforcement Learning(RL)-based algorithm that improves slice acceptance while addressing the specific constraints of Kubernetes-hosted infrastructures.",
keywords = "5G, Kubernetes, Network Slicing, RL, VNE",
author = "Brahmi, \{Abdenour Yasser\} and \{Ait Aba\}, Massinissa and Hadil Bouasker and Badii Jouaber and Hind Castel-Taleb",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 11th IEEE International Conference on Network Softwarization, NetSoft 2025 ; Conference date: 23-06-2025 Through 27-06-2025",
year = "2025",
month = jan,
day = "1",
doi = "10.1109/NetSoft64993.2025.11080579",
language = "English",
series = "Proceedings of the 11th IEEE International Conference on Network Softwarization, NetSoft 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "448--452",
editor = "Pal Varga and Walter Cerroni and Carol Fung and Robert Szabo and Massimo Tornatore",
booktitle = "Proceedings of the 11th IEEE International Conference on Network Softwarization, NetSoft 2025",
}