Integrating Machine Learning in Software-Defined Networks: A Comprehensive Survey
DOI:
https://doi.org/10.58564/IJCCN.1.1.2025.2Keywords:
software defined, network (SDN),artificial intelligence, Machine learning (ML),Deep learning,Deep reinforcement learningAbstract
As we know the Internet became an important part of our daily life, as the fast increasing spread of the Internet and technologies nowadays is resulting in a more varied and complicated network. Through the network separation between the control plane and the data plane where software-defined networking (SDN) has transformed the network architecture. The systems have become more Intelligence thanks in part to machine learning (ML) and the rest of its branches. Machine learning and SDN have been subjects of several academic publications. This survey compiles research articles published in Springer, Elsevier, IEEE, ACM on the issue of SDN &ML (2018 - 2023). Research is structured according to solutions, evaluation criteria, and evaluation environments so that SDN and ML teams have the greatest opportunity of optimizing the intended non-functional and functional qualities. This review paper will gather the results of the analyses of the researchers to extract environments, assessment criteria, and solutions. This paper will discuss future areas of research and the research deficit.
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Copyright (c) 2025 Mohammad Khalid, Hassan Mohamed Muhialdeen
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