Artificial Intelligence-Driven Sensitivity Analysis: Present-Day Approaches in Software-Defined Networking

Abstract

This paper presents an overview of how artificial intelligence (AI) techniques are being explored for sensitivity analysis in the context of software-defined networking (SDN). Sensitivity analysis (SA) is pivotal in determining the influence of variable inputs on system outputs, a process essential for the enhancement of SDN's performance and dependability. The incorporation of AI techniques, especially machine learning algorithms, has led to substantial progress in executing both local and global sensitivity analyses within SDN frameworks. Such progress is instrumental in improving the network's adaptability, operational efficiency, and security measures. This paper highlights some of the present-day methodologies and practical applications in this area, focusing on the role of AI in refining sensitivity analysis in SDN. The objective is to provide a brief overview of the latest research developments for scholars engaged in this rapidly growing field