Artificial intelligence enabled software‐defined networking: a comprehensive overview

Abstract

Software-defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller that can be programmed and used as the brain of the network. Recently, the research community has shown an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, the authors provide a detailed overview of the recent efforts to include AI in SDN. The study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work, the authors investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

AI-Assisted Framework for Green-Routing and Load Balancing in Hybrid Software-Defined Networking: Proposal, Challenges and Future Perspective

“…AI represents a rapidly growing field of science and engineering which features the use of methods that empower or endow computing devices (machines) and systems with advanced capabilities to imitate human behaviour [69]. Besides, AI is considered as an increasingly growing computation paradigm, whose objective focuses on teaching computing devices how to work and respond in a human-like manner.…”

“…The progress in ML and DL techniques are envisioned to perform a significant part in current and future networks. For a comprehensive review of ML algorithms used in SDN traffic classification, optimization or routing and QoS and QoE prediction, see [68] [69].…”

“…ML is a science concerned with the study of algorithms that employ statistical methods to enhance the performance of computerised systems by leveraging past experience [69].…”

Artificial Neural Network (ANN) Based DDoS Attack Detection Model on Software Defined Networking (SDN)

“…Many machine learning algorithms [7] are there to predict and detect network attacks based on the historical dataset, algorithms such as Support Vector Mechanism (SVM), Kneighbor classification (KNN), Logistic Regression (LR), Decision Tree (DT) and so on. Decision Tree (DT) [8] is a very popular and successful classification algorithm. Data can be described as a tree like structure for discrete or continues any kind of data.…”

“…It performs a sequence of test with each internal node of the input attributes. Support Vector Mechanism (SVM) [8] algorithm based on the boundary between its two different types to provide enhanced classification. SVM can represent against complex functions.…”