Applications of Artificial Intelligence and Machine Learning in the Area of SDN and NFV: A Survey

Anteneh A. Gebremariam, Muhammad Usman, Marwa Qaraqe

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

39 Citations (Scopus)

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) have gained a huge interest from academia and industry in solving very complex problems in several fields. In this paper, we present a short survey of the main application areas of AI/ML in SDN and NFV based networks. We classify the main advancements in the area in different categories based on their application track and identify the corresponding AI techniques utilized. In addition, identify and discuss the main challenges and future directions in the area. We stress that AI/ML can play a vital role in providing a way towards self-configured, self-adaptive and self-managed networks. However, the research is limited due the identified challenges in this area.

Original languageEnglish
Title of host publication16th International Multi-Conference on Systems, Signals and Devices, SSD 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages545-549
Number of pages5
ISBN (Electronic)9781728118208
DOIs
Publication statusPublished - Mar 2019
Event16th International Multi-Conference on Systems, Signals and Devices, SSD 2019 - Istanbul, Turkey
Duration: 21 Mar 201924 Mar 2019

Publication series

Name16th International Multi-Conference on Systems, Signals and Devices, SSD 2019

Conference

Conference16th International Multi-Conference on Systems, Signals and Devices, SSD 2019
Country/TerritoryTurkey
CityIstanbul
Period21/03/1924/03/19

Keywords

  • 5G networks
  • Data analytics
  • Deep learning
  • Machine learning
  • NFV
  • Network management and operations
  • Network planning
  • Network security
  • SDN

Fingerprint

Dive into the research topics of 'Applications of Artificial Intelligence and Machine Learning in the Area of SDN and NFV: A Survey'. Together they form a unique fingerprint.

Cite this