The Promise of Applying Machine Learning Techniques to Network Function Virtualization

Article The Promise of Applying Machine Learning Techniques to Network Function Virtualization Houda Jmila 1, Mohamed Ibn Khedher 2,*, and Mounim A. El-Yacoubi 3 1 Institute LIST, CEA, Paris-Saclay University, 91190 Palaiseau, France 2 IRT-SystemX, 2 Bd Thomas Gobert, 91120 Palaiseau, France 3 Samovar, Telecom SudParis, Institut Polytechnique de Paris, 19 place Marguerite Perey, 91120 Palaiseau, France * Correspondence: mohamed.ibn-khedher@irt-systemx.fr Received: 28 December 2023 Accepted: 15 August 2024 Published: 24 December 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.