Data plane failure and its recovery techniques in SDN: A systematic literature review
Nauman Ali Khan, Rosli Salleh, Anis Koubâa, Zahid Khan, Muhammad Khurram Khan, Ihsan Ali
Abstract
Software-defined networking (SDN) plays a crucial role in the enterprise and wide-area networking. The increasing demand for strict service-level agreement applications on the Internet requires networks to be scalable and resilient in the face of link and switch failure. However, there is a lack of systematic reviews on SDN data plane failure recovery techniques. This review article assesses SDN current state-of-the-art link and switches failure recovery solutions. We cover the root causes of failures in the traditional core network and their detection and classify the current failure recovery techniques for SDN into two categories: traditional and artificial intelligence (AI) approaches. AI-based techniques enable efficient failure recovery and enhance the quality of service. We also consider performance measure metrics to evaluate and determine the limitations of existing solutions. This study reviews 188 papers from 2010 to 2021, selecting 70 articles that are highly relevant to our work. All articles are written in English. Our research aims to collect a large amount of evidence that will assist the industry and academic researchers in networking to address current research gaps in failure recovery solutions for the SDN data plane.