Khan, Nauman and Bin Salleh, Rosli and Koubaa, Anis and Khan, Zahid and Khan, Muhammad Khurram and Ali, Ihsan (2023) Data plane failure and its recovery techniques in SDN: A systematic literature review. Journal of King Saud University - Computer and Information Sciences, 35 (3). pp. 176-201. ISSN 1319-1578, DOI https://doi.org/10.1016/j.jksuci.2023.02.001.
Full text not available from this repository.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. (c) 2023 Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Software-defined networking; Traditional networking; Link and switch failure detection; Restoration; Protection; Artificial intelligence |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Z Bibliography. Library Science. Information Resources > Libraries > Library science. Information science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Computer System & Technology |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 23 Nov 2023 01:19 |
| Last Modified: | 23 Nov 2023 01:19 |
| URI: | http://eprints.um.edu.my/id/eprint/38574 |
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