| Title |
Machine Learning for Management in Software-defined Networks: A Systematic Literature Review |
| Authors |
(Andres J. Aparcana-Tasayco) ; (Javier Gamboa-Cruzado) |
| DOI |
https://doi.org/10.5573/IEIESPC.2022.11.6.400 |
| Keywords |
Machine learning (ML); Management; Software-defined networks (SDN); Systematic literature review (SLR) |
| Abstract |
Software-Defined Networking (SDN) has emerged as a new paradigm for managing data networks, and Machine Learning (ML) techniques have become relevant in the scientific community to solve management problems. Research on using these two variables has increased in recent years. Therefore, a systematic literature review based on Kitchenham’s guidelines and PRISMA guidelines is necessary. The review included publications from between 2016 and 2021. The study recorded 21,743 primary articles, and after applying rigorous exclusion and quality criteria, 81 articles were obtained. The results show the most productive authors, such as Julong, as well as the relationships between the most productive authors and the keywords “SDN” and “Machine Learning,” which are the most used among researchers. |