Taxonomy of traffic engineering mechanisms in software-defined networks: a survey

Mohammadi, Ramin; Akleylek, Sedat; Ghaffari, Ali; Shirmarz, Alireza

doi:10.1007/s11235-022-00947-6

Cited by 4 publications

References 109 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Comprehensive Survey on Machine Learning using in Software Defined Networks (SDN)

Faezi

Shirmarz

2023

Hum-Cent Intell Syst

View full text Add to dashboard Cite

These days, Internet coverage and technologies are growing rapidly, hence, it makes the network more complex and heterogeneous. Software defined network (SDN) revolutionized the network architecture and simplified the network by separating the control and data plane. On the other hand, machine learning (ML) and its derivations have made the systems more intelligent. Many pieces of research papers have addressed ML and SDN. In this survey, we collected the papers published in Springer, Elsevier, IEEE, and ACM and addressed SDN and ML between 2016 and 2023. The research papers are organized based on the solutions, evaluation parameters, and evaluation environments to help those working on SDN and ML for improving the target functional or non-functional parameters. The research papers will be analyzed to extract the solutions, evaluation parameters and environments. The extracted solutions, evaluation parameters and environments will be clustered in this review paper. The research gap and future research directions will be stated in this work. This survey is completely useful for those who working on SDN and want to improve the functional and non-functional parameters using machine learning.

show abstract