Optimal Cross Slice Orchestration for 5G Mobile Services

Hoang, DT; Niyato, D; Wang, P; De Domenico, A; Strinati, EC

Optimal Cross Slice Orchestration for 5G Mobile Services

Hoang, DT

Niyato, D Wang, P De Domenico, A Strinati, EC

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Publication Type:: Conference Proceeding
Citation:: IEEE Vehicular Technology Conference, 2018, 2018-August
Issue Date:: 2018-07-02

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Field	Value	Language
dc.contributor.author	Hoang, DT https://orcid.org/0000-0002-9528-0863	en_US
dc.contributor.author	Niyato, D	en_US
dc.contributor.author	Wang, P	en_US
dc.contributor.author	De Domenico, A	en_US
dc.contributor.author	Strinati, EC	en_US
dc.date.issued	2018-07-02	en_US
dc.identifier.citation	IEEE Vehicular Technology Conference, 2018, 2018-August	en_US
dc.identifier.isbn	9781538663585	en_US
dc.identifier.issn	1550-2252	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129848
dc.description.abstract	© 2018 IEEE. 5G mobile networks encompass the capabilities of hosting a variety of services such as mobile social networks, multimedia delivery, healthcare, transportation, and public safety. Therefore, the major challenge in designing the 5G networks is how to support different types of users and applications with different quality-of-service requirements under a single physical network infrastructure. Recently, network slicing has been introduced as a promising solution to address this challenge. Network slicing allows programmable network instances which match the service requirements by using network virtualization technologies. However, how to efficiently allocate resources across network slices has not been well studied in the literature. Therefore, in this paper, we first introduce a model for orchestrating network slices based on the service requirements and available resources. Then, we propose a Markov decision process framework to formulate and determine the optimal policy that manages cross-slice admission control and resource allocation for the 5G networks. Through simulation results, we show that the proposed solution is efficient not only in providing slice-as-a-service based on service requirements, but also in maximizing the provider's revenue.	en_US
dc.relation.ispartof	IEEE Vehicular Technology Conference	en_US
dc.relation.isbasedon	10.1109/VTCFall.2018.8690608	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Optimal Cross Slice Orchestration for 5G Mobile Services	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-August	en_US
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-August	en_US

Abstract:

© 2018 IEEE. 5G mobile networks encompass the capabilities of hosting a variety of services such as mobile social networks, multimedia delivery, healthcare, transportation, and public safety. Therefore, the major challenge in designing the 5G networks is how to support different types of users and applications with different quality-of-service requirements under a single physical network infrastructure. Recently, network slicing has been introduced as a promising solution to address this challenge. Network slicing allows programmable network instances which match the service requirements by using network virtualization technologies. However, how to efficiently allocate resources across network slices has not been well studied in the literature. Therefore, in this paper, we first introduce a model for orchestrating network slices based on the service requirements and available resources. Then, we propose a Markov decision process framework to formulate and determine the optimal policy that manages cross-slice admission control and resource allocation for the 5G networks. Through simulation results, we show that the proposed solution is efficient not only in providing slice-as-a-service based on service requirements, but also in maximizing the provider's revenue.

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http://hdl.handle.net/10453/129848