Data plane failure and its recovery techniques in SDN: A systematic literature review
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...
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Format: | Article |
Language: | English |
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Elsevier
2023-03-01
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Series: | Journal of King Saud University: Computer and Information Sciences |
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Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157823000307 |
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author | Nauman Khan Rosli bin Salleh Anis Koubaa Zahid Khan Muhammad Khurram Khan Ihsan Ali |
author_facet | Nauman Khan Rosli bin Salleh Anis Koubaa Zahid Khan Muhammad Khurram Khan Ihsan Ali |
author_sort | Nauman Khan |
collection | DOAJ |
description | 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. |
first_indexed | 2024-04-10T06:21:19Z |
format | Article |
id | doaj.art-19bf0a312deb4aa7945749810de912b6 |
institution | Directory Open Access Journal |
issn | 1319-1578 |
language | English |
last_indexed | 2024-04-10T06:21:19Z |
publishDate | 2023-03-01 |
publisher | Elsevier |
record_format | Article |
series | Journal of King Saud University: Computer and Information Sciences |
spelling | doaj.art-19bf0a312deb4aa7945749810de912b62023-03-02T04:58:58ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782023-03-01353176201Data plane failure and its recovery techniques in SDN: A systematic literature reviewNauman Khan0Rosli bin Salleh1Anis Koubaa2Zahid Khan3Muhammad Khurram Khan4Ihsan Ali5Department of Computer System & Technology, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia; Department of Computer Science & Information Technology, University of Malakand, Chakdara Dir Lower, KPK, Pakistan; Corresponding authors.Department of Computer System & Technology, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia; Corresponding authors.College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi ArabiaCollege of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi ArabiaCenter of Excellence in Information Assurance, King Saud University, Saudi ArabiaDepartment of Computer System & Technology, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, MalaysiaSoftware-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.http://www.sciencedirect.com/science/article/pii/S1319157823000307Software-defined networkingTraditional networkingLink and switch failure detectionRestorationProtectionArtificial intelligence |
spellingShingle | Nauman Khan Rosli bin Salleh Anis Koubaa Zahid Khan Muhammad Khurram Khan Ihsan Ali Data plane failure and its recovery techniques in SDN: A systematic literature review Journal of King Saud University: Computer and Information Sciences Software-defined networking Traditional networking Link and switch failure detection Restoration Protection Artificial intelligence |
title | Data plane failure and its recovery techniques in SDN: A systematic literature review |
title_full | Data plane failure and its recovery techniques in SDN: A systematic literature review |
title_fullStr | Data plane failure and its recovery techniques in SDN: A systematic literature review |
title_full_unstemmed | Data plane failure and its recovery techniques in SDN: A systematic literature review |
title_short | Data plane failure and its recovery techniques in SDN: A systematic literature review |
title_sort | data plane failure and its recovery techniques in sdn a systematic literature review |
topic | Software-defined networking Traditional networking Link and switch failure detection Restoration Protection Artificial intelligence |
url | http://www.sciencedirect.com/science/article/pii/S1319157823000307 |
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