Dynamic Machine Learning Algorithm Selection For Network Slicing in Beyond 5G Networks - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2023

Dynamic Machine Learning Algorithm Selection For Network Slicing in Beyond 5G Networks

Abstract

The advanced 5G and 6G mobile network generations offer new capabilities that enable the creation of multiple virtual network instances with distinct and stringent requirements. However, the coexistence of multiple network functions on top of a shared substrate network poses a resource allocation challenge known as the Virtual Network Embedding (VNE) problem. In recent years, this NP-hard problem has received increasing attention in the literature due to the growing need to optimize resources at the edge of the network, where computational and storage capabilities are limited. In this demo paper, we propose a solution to this problem, utilizing the Algorithm Selection (AS) paradigm. This selects the most optimal Deep Reinforcement Learning (DRL) algorithm from a portfolio of agents, in an offline manner, based on past performance. To evaluate our solution, we developed a simulation platform using the OMNeT++ framework, with an orchestration module containerized using Docker. The proposed solution shows good performance and outperforms standalone algorithms.
Fichier principal
Vignette du fichier
Demo_paper_WMNC_22.pdf (174.72 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04368576 , version 1 (01-01-2024)

Licence

Attribution

Identifiers

Cite

Abdelmounaim Bouroudi, Abdelkader Outtagarts, Yassine Hadjadj-Aoul. Dynamic Machine Learning Algorithm Selection For Network Slicing in Beyond 5G Networks. NetSoft 2023 - IEEE 9th International Conference on Network Softwarization, Jun 2023, Madrid, Spain. pp.314-316, ⟨10.1109/NetSoft57336.2023.10175443⟩. ⟨hal-04368576⟩
6 View
11 Download

Altmetric

Share

Gmail Facebook X LinkedIn More