Cloudified Mobility and Bandwidth Prediction in Virtualized LTE Networks

Zongliang Zhao, Morteza Karimzadeh Motallebi Azar, Torsten Braun, Aiko Pras, Hans Leo van den Berg

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Network Function Virtualization involves implementing network functions (e.g., virtualized LTE component) in software that can run on a range of industry standard server hardware, and can be migrated or instantiated on demand. A prediction service hosted on cloud infrastructures enables consumers to request the prediction information on-demand and respond accordingly. In this paper we introduce MOBaaS, which is a network function of Mobility and Bandwidth prediction cloudified over the cloud computing infrastructure. We implemented the service orchestration framework of MOBaaS, which can easily be setup and integrated with any other cloud-based LTE entities to provide prediction information about the future location of mobile user(s) as well as the network link(s) bandwidth availability. This information can be used to generate required triggers for on-demand deployment or scaling-up/down of virtualized network components as well as for the self-adaptation procedures and optimal network function configuration. We also describe the performance evaluation of the MOBaaS cloudification procedures and present an example of the benefit of such a prediction service
LanguageEnglish
Title of host publicationIFIP/IEEE International Symposium on Integrated Network Management (IM 2017)
PublisherIEEE
Number of pages7
ISBN (Electronic)978-3-901882-89-0
DOIs
StatePublished - Jul 2017
Event15th IFIP/IEEE International Symposium on Integrated Network Management (IM 2017) - Lisbon, Portugal
Duration: 8 May 201712 May 2017
Conference number: 15
http://im2017.ieee-im.org/

Conference

Conference15th IFIP/IEEE International Symposium on Integrated Network Management (IM 2017)
Abbreviated titleIM 2017
CountryPortugal
CityLisbon
Period8/05/1712/05/17
Internet address

Fingerprint

Bandwidth
Network components
Cloud computing
Computer hardware
Telecommunication links
Servers
Availability
Industry

Cite this

Zhao, Z., Karimzadeh Motallebi Azar, M., Braun, T., Pras, A., & van den Berg, H. L. (2017). Cloudified Mobility and Bandwidth Prediction in Virtualized LTE Networks. In IFIP/IEEE International Symposium on Integrated Network Management (IM 2017) IEEE. DOI: 10.23919/INM.2017.7987321
Zhao, Zongliang ; Karimzadeh Motallebi Azar, Morteza ; Braun, Torsten ; Pras, Aiko ; van den Berg, Hans Leo. / Cloudified Mobility and Bandwidth Prediction in Virtualized LTE Networks. IFIP/IEEE International Symposium on Integrated Network Management (IM 2017). IEEE, 2017.
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abstract = "Network Function Virtualization involves implementing network functions (e.g., virtualized LTE component) in software that can run on a range of industry standard server hardware, and can be migrated or instantiated on demand. A prediction service hosted on cloud infrastructures enables consumers to request the prediction information on-demand and respond accordingly. In this paper we introduce MOBaaS, which is a network function of Mobility and Bandwidth prediction cloudified over the cloud computing infrastructure. We implemented the service orchestration framework of MOBaaS, which can easily be setup and integrated with any other cloud-based LTE entities to provide prediction information about the future location of mobile user(s) as well as the network link(s) bandwidth availability. This information can be used to generate required triggers for on-demand deployment or scaling-up/down of virtualized network components as well as for the self-adaptation procedures and optimal network function configuration. We also describe the performance evaluation of the MOBaaS cloudification procedures and present an example of the benefit of such a prediction service",
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Zhao, Z, Karimzadeh Motallebi Azar, M, Braun, T, Pras, A & van den Berg, HL 2017, Cloudified Mobility and Bandwidth Prediction in Virtualized LTE Networks. in IFIP/IEEE International Symposium on Integrated Network Management (IM 2017). IEEE, 15th IFIP/IEEE International Symposium on Integrated Network Management (IM 2017), Lisbon, Portugal, 8/05/17. DOI: 10.23919/INM.2017.7987321

Cloudified Mobility and Bandwidth Prediction in Virtualized LTE Networks. / Zhao, Zongliang; Karimzadeh Motallebi Azar, Morteza ; Braun, Torsten; Pras, Aiko ; van den Berg, Hans Leo.

IFIP/IEEE International Symposium on Integrated Network Management (IM 2017). IEEE, 2017.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Network Function Virtualization involves implementing network functions (e.g., virtualized LTE component) in software that can run on a range of industry standard server hardware, and can be migrated or instantiated on demand. A prediction service hosted on cloud infrastructures enables consumers to request the prediction information on-demand and respond accordingly. In this paper we introduce MOBaaS, which is a network function of Mobility and Bandwidth prediction cloudified over the cloud computing infrastructure. We implemented the service orchestration framework of MOBaaS, which can easily be setup and integrated with any other cloud-based LTE entities to provide prediction information about the future location of mobile user(s) as well as the network link(s) bandwidth availability. This information can be used to generate required triggers for on-demand deployment or scaling-up/down of virtualized network components as well as for the self-adaptation procedures and optimal network function configuration. We also describe the performance evaluation of the MOBaaS cloudification procedures and present an example of the benefit of such a prediction service

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Zhao Z, Karimzadeh Motallebi Azar M, Braun T, Pras A, van den Berg HL. Cloudified Mobility and Bandwidth Prediction in Virtualized LTE Networks. In IFIP/IEEE International Symposium on Integrated Network Management (IM 2017). IEEE. 2017. Available from, DOI: 10.23919/INM.2017.7987321