Knowledge-defined networking: Applications, challenges and future work

Ashtari, S; Zhou, I; Abolhasan, M; Shariati, N; Lipman, J; Ni, W

Knowledge-defined networking: Applications, challenges and future work

Ashtari, S Zhou, I

Abolhasan, M

Shariati, N Lipman, J

Ni, W

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Array, 2022, 14, pp. 100136
Issue Date:: 2022-07-01

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Field	Value	Language
dc.contributor.author	Ashtari, S
dc.contributor.author	Zhou, I https://orcid.org/0000-0002-7154-4561
dc.contributor.author	Abolhasan, M https://orcid.org/0000-0002-4282-6666
dc.contributor.author	Shariati, N
dc.contributor.author	Lipman, J https://orcid.org/0000-0003-2877-1168
dc.contributor.author	Ni, W
dc.date.accessioned	2023-03-22T23:53:51Z
dc.date.available	2023-03-22T23:53:51Z
dc.date.issued	2022-07-01
dc.identifier.citation	Array, 2022, 14, pp. 100136
dc.identifier.issn	2590-0056
dc.identifier.issn	2590-0056
dc.identifier.uri	http://hdl.handle.net/10453/168105
dc.description.abstract	Future 6G wireless communication systems are expected to feature intelligence and automation. Knowledge-defined networking (KDN) is an evolutionary step toward autonomous and self-driving networks. The building blocks of the KDN paradigm in achieving self-driving networks are software-defined networking (SDN), packet-level network telemetry, and machine learning (ML). The KDN paradigm intends to integrate intelligence to manage and control networks automatically. In this study, we first introduce the disadvantages of current network technologies. Then, the KDN and associated technologies are explored with three possible KDN architectures for heterogeneous wireless networks. Furthermore, a thorough investigation of recent survey studies on different wireless network applications was conducted. The aim is to identify and review suitable ML-based studies for KDN-based wireless cellular networks. These applications are categorized as resource management, network management, mobility management, and localization. Resource management applications can be further classified as spectrum allocation, power management, quality-of-service (QoS), base station (BS) switching, cache, and backhaul management. Within network management configurations, routing strategies, clustering, user/BS association, traffic classification, and data aggregation were investigated. Applications in mobility management include user mobility prediction and handover management. To improve the accuracy of positioning in indoor environments, localization techniques were discussed. We classify existing research into the respective KDN architecture and identify how the knowledge obtained will enhance future networks; as a result, researchers can extend their work to empower intelligence and self-organization in the network using the KDN paradigm. Finally, the requirements, motivations, applications, challenges, and open issues are presented.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Array
dc.relation.isbasedon	10.1016/j.array.2022.100136
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Knowledge-defined networking: Applications, challenges and future work
dc.type	Journal Article
utslib.citation.volume	14
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-22T23:53:49Z
pubs.publication-status	Published
pubs.volume	14

Abstract:

Future 6G wireless communication systems are expected to feature intelligence and automation. Knowledge-defined networking (KDN) is an evolutionary step toward autonomous and self-driving networks. The building blocks of the KDN paradigm in achieving self-driving networks are software-defined networking (SDN), packet-level network telemetry, and machine learning (ML). The KDN paradigm intends to integrate intelligence to manage and control networks automatically. In this study, we first introduce the disadvantages of current network technologies. Then, the KDN and associated technologies are explored with three possible KDN architectures for heterogeneous wireless networks. Furthermore, a thorough investigation of recent survey studies on different wireless network applications was conducted. The aim is to identify and review suitable ML-based studies for KDN-based wireless cellular networks. These applications are categorized as resource management, network management, mobility management, and localization. Resource management applications can be further classified as spectrum allocation, power management, quality-of-service (QoS), base station (BS) switching, cache, and backhaul management. Within network management configurations, routing strategies, clustering, user/BS association, traffic classification, and data aggregation were investigated. Applications in mobility management include user mobility prediction and handover management. To improve the accuracy of positioning in indoor environments, localization techniques were discussed. We classify existing research into the respective KDN architecture and identify how the knowledge obtained will enhance future networks; as a result, researchers can extend their work to empower intelligence and self-organization in the network using the KDN paradigm. Finally, the requirements, motivations, applications, challenges, and open issues are presented.

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http://hdl.handle.net/10453/168105