Open Access
Article
The Promise of Applying Machine Learning Techniques to Network Function Virtualization
Houda Jmila1
, Mohamed Ibn Khedher2, *
, Mounim A. El-Yacoubi3
Author Information
Submitted: 28 Dec 2023 | Accepted: 15 Aug 2024 | Published: 24 Dec 2024
Abstract
“Network Function Virtualization” (NFV) is an emerging technology and 5G key enabler. It promises operating expenditure savings and high flexibility in managing the network by decoupling the network functions, like firewalls, proxies etc., from the physical equipments on which they run. In order to reap the full benefits of NFV, some challenges still need to be overcome, namely those related to resource management, security and anomaly detection. Recently, Machine learning (ML) has been applied in different fields and has demonstrated amazing results. Utilizing Machine learning to address the challenges faced by NFV is a promising research field that requires further investigation. In this paper, we shed light on this domain by discussing the potential and challenges of ML application to NFV and by surveying existing works.
Keywords
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Volume 3, Issue 4How to Cite
Jmila, H., Khedher, M. I., & El-Yacoubi, M. A. (2024). The Promise of Applying Machine Learning Techniques to Network Function Virtualization. International Journal of Network Dynamics and Intelligence, 3(4), 100020. https://doi.org/10.53941/ijndi.2024.100020
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