Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview

TL;DR

This paper provides a comprehensive overview of how artificial intelligence techniques like machine learning, meta-heuristics, and fuzzy inference systems are integrated into software defined networking to enhance decision making and network management.

Contribution

It systematically reviews recent AI applications in SDN, highlighting the main AI sub-fields used and their impact on network management and performance.

Findings

AI enhances SDN decision-making capabilities

Machine learning, meta-heuristics, and fuzzy systems are key AI techniques in SDN

Significant improvements in network management with AI integration

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 showed 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, we provide a detailed overview of the recent efforts to include AI in SDN. Our 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 we investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.