An Artificial Intelligence Framework for Slice Deployment and Orchestration in 5G Networks

Dandachi, G; De Domenico, A; Hoang, DT; Niyato, D

An Artificial Intelligence Framework for Slice Deployment and Orchestration in 5G Networks

Dandachi, G De Domenico, A Hoang, DT Niyato, D

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Cognitive Communications and Networking, 2020, 6, (2), pp. 858-871
Issue Date:: 2020-06-01

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Field	Value	Language
dc.contributor.author	Dandachi, G
dc.contributor.author	De Domenico, A
dc.contributor.author	Hoang, DT
dc.contributor.author	Niyato, D
dc.date.accessioned	2021-02-16T08:48:04Z
dc.date.available	2021-02-16T08:48:04Z
dc.date.issued	2020-06-01
dc.identifier.citation	IEEE Transactions on Cognitive Communications and Networking, 2020, 6, (2), pp. 858-871
dc.identifier.issn	2372-2045
dc.identifier.issn	2332-7731
dc.identifier.uri	http://hdl.handle.net/10453/146146
dc.description.abstract	© 2015 IEEE. Network slicing is a key enabler to successfully support 5G services with specific requirements and priorities. Due to the diversity of these services, slice deployment and orchestration are essential to guarantee service performance in a cost-effective way. Here, we propose an Artificial Intelligence framework for cross-slice admission and congestion control that simultaneously considers communication, computing, and storage resources to maximize resources utilization and operator revenue. First, we propose a smart feature extraction solution to analyze the characteristics of incoming requests together with the already deployed slices, and then automatically evaluates the request requirements to make appropriate decisions. Second, we design an online algorithm that controls the slice admission based on their priorities, the arrival and departure characteristics, and the available resources. To mitigate system overloading, our framework dynamically adjusts resources allocated to low priority slices, thereby reducing the dropping probability of new slice requests. The proposed algorithm offers outstanding advantages over traditional static approaches by automatically adapting the controller decisions to the system changes. Simulation results show that our framework significantly improves the resource utilization and reduces the slice request dropping probabilities up to 44% as compared to the baseline schemes.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Cognitive Communications and Networking
dc.relation.isbasedon	10.1109/TCCN.2019.2952882
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	An Artificial Intelligence Framework for Slice Deployment and Orchestration in 5G Networks
dc.type	Journal Article
utslib.citation.volume	6
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2021-02-16T08:47:28Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	6
utslib.citation.issue	2

Abstract:

© 2015 IEEE. Network slicing is a key enabler to successfully support 5G services with specific requirements and priorities. Due to the diversity of these services, slice deployment and orchestration are essential to guarantee service performance in a cost-effective way. Here, we propose an Artificial Intelligence framework for cross-slice admission and congestion control that simultaneously considers communication, computing, and storage resources to maximize resources utilization and operator revenue. First, we propose a smart feature extraction solution to analyze the characteristics of incoming requests together with the already deployed slices, and then automatically evaluates the request requirements to make appropriate decisions. Second, we design an online algorithm that controls the slice admission based on their priorities, the arrival and departure characteristics, and the available resources. To mitigate system overloading, our framework dynamically adjusts resources allocated to low priority slices, thereby reducing the dropping probability of new slice requests. The proposed algorithm offers outstanding advantages over traditional static approaches by automatically adapting the controller decisions to the system changes. Simulation results show that our framework significantly improves the resource utilization and reduces the slice request dropping probabilities up to 44% as compared to the baseline schemes.

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http://hdl.handle.net/10453/146146